### What changes were proposed in this pull request?
This PR adds `DROP NAMESPACE` support for V2 catalogs.
### Why are the changes needed?
Currently, you cannot drop namespaces for v2 catalogs.
### Does this PR introduce any user-facing change?
The user can now perform the following:
```SQL
CREATE NAMESPACE mycatalog.ns
DROP NAMESPACE mycatalog.ns
SHOW NAMESPACES IN mycatalog # Will show no namespaces
```
to drop a namespace `ns` inside `mycatalog` V2 catalog.
### How was this patch tested?
Added unit tests.
Closes#26262 from imback82/drop_namespace.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add LoadDataStatement and make LOAD DATA INTO TABLE go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users. e.g.
```
USE my_catalog
DESC t // success and describe the table t from my_catalog
LOAD DATA INPATH 'filepath' INTO TABLE t // report table not found as there is no table t in the session catalog
```
### Does this PR introduce any user-facing change?
yes. When running LOAD DATA INTO TABLE, Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
Unit tests.
Closes#26178 from viirya/SPARK-29521.
Lead-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Co-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
### Why are the changes needed?
When make the `LocalShuffleReaderExec` to leaf node, there exists a potential issue: the leaf node will hide the running query stage and make the unfinished query stage as finished query stage when creating its parent query stage.
This PR make the leaf node to unary node.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests
Closes#26250 from JkSelf/updateLeafNodeofLocalReaderToUnaryExecNode.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Reset the `WritableColumnVector` when getting "next" ColumnarBatch in `RowToColumnarExec`
### Why are the changes needed?
When converting `Iterator[InternalRow]` to `Iterator[ColumnarBatch]`, the vectors used to create a new `ColumnarBatch` should be reset in the iterator's "next()" method.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26137 from rongma1997/reset-WritableColumnVector.
Authored-by: rongma1997 <rong.ma@intel.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
```
hive> select version();
OK
3.1.1 rf4e0529634b6231a0072295da48af466cf2f10b7
Time taken: 2.113 seconds, Fetched: 1 row(s)
```
### Why are the changes needed?
From hive behavior and I guess it is useful for debugging and developing etc.
### Does this PR introduce any user-facing change?
add a misc func
### How was this patch tested?
add ut
Closes#26209 from yaooqinn/SPARK-29554.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add ShowCreateTableStatement and make SHOW CREATE TABLE go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users. e.g.
```
USE my_catalog
DESC t // success and describe the table t from my_catalog
SHOW CREATE TABLE t // report table not found as there is no table t in the session catalog
```
### Does this PR introduce any user-facing change?
yes. When running SHOW CREATE TABLE, Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
Unit tests.
Closes#26184 from viirya/SPARK-29527.
Lead-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Co-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch adds the functionality to measure records being written for JDBC writer. In reality, the value is meant to be a number of records being updated from queries, as per JDBC spec it will return updated count.
### Why are the changes needed?
Output metrics for JDBC writer are missing now. The value of "bytesWritten" is also missing, but we can't measure it from JDBC API.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Unit test added.
Closes#26109 from HeartSaVioR/SPARK-29461.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
First, a bit of background on the code being changed. The current code tracks
metric updates for each task, recording which metrics the task is monitoring
and the last update value.
Once a SQL execution finishes, then the metrics for all the stages are
aggregated, by building a list with all (metric ID, value) pairs collected
for all tasks in the stages related to the execution, then grouping by metric
ID, and then calculating the values shown in the UI.
That is full of inefficiencies:
- in normal operation, all tasks will be tracking and updating the same
metrics. So recording the metric IDs per task is wasteful.
- tracking by task means we might be double-counting values if you have
speculative tasks (as a comment in the code mentions).
- creating a list of (metric ID, value) is extremely inefficient, because now
you have a huge map in memory storing boxed versions of the metric IDs and
values.
- same thing for the aggregation part, where now a Seq is built with the values
for each metric ID.
The end result is that for large queries, this code can become both really
slow, thus affecting the processing of events, and memory hungry.
The updated code changes the approach to the following:
- stages track metrics by their ID; this means the stage tracking code
naturally groups values, making aggregation later simpler.
- each metric ID being tracked uses a long array matching the number of
partitions of the stage; this means that it's cheap to update the value of
the metric once a task ends.
- when aggregating, custom code just concatenates the arrays corresponding to
the matching metric IDs; this is cheaper than the previous, boxing-heavy
approach.
The end result is that the listener uses about half as much memory as before
for tracking metrics, since it doesn't need to track metric IDs per task.
I captured heap dumps with the old and the new code during metric aggregation
in the listener, for an execution with 3 stages, 100k tasks per stage, 50
metrics updated per task. The dumps contained just reachable memory - so data
kept by the listener plus the variables in the aggregateMetrics() method.
With the old code, the thread doing aggregation references >1G of memory - and
that does not include temporary data created by the "groupBy" transformation
(for which the intermediate state is not referenced in the aggregation method).
The same thread with the new code references ~250M of memory. The old code uses
about ~250M to track all the metric values for that execution, while the new
code uses about ~130M. (Note the per-thread numbers include the amount used to
track the metrics - so, e.g., in the old case, aggregation was referencing
about ~750M of temporary data.)
I'm also including a small benchmark (based on the Benchmark class) so that we
can measure how much changes to this code affect performance. The benchmark
contains some extra code to measure things the normal Benchmark class does not,
given that the code under test does not really map that well to the
expectations of that class.
Running with the old code (I removed results that don't make much
sense for this benchmark):
```
[info] Java HotSpot(TM) 64-Bit Server VM 1.8.0_181-b13 on Linux 4.15.0-66-generic
[info] Intel(R) Core(TM) i7-6820HQ CPU 2.70GHz
[info] metrics aggregation (50 metrics, 100k tasks per stage): Best Time(ms) Avg Time(ms)
[info] --------------------------------------------------------------------------------------
[info] 1 stage(s) 2113 2118
[info] 2 stage(s) 4172 4392
[info] 3 stage(s) 7755 8460
[info]
[info] Stage Count Stage Proc. Time Aggreg. Time
[info] 1 614 1187
[info] 2 620 2480
[info] 3 718 5069
```
With the new code:
```
[info] Java HotSpot(TM) 64-Bit Server VM 1.8.0_181-b13 on Linux 4.15.0-66-generic
[info] Intel(R) Core(TM) i7-6820HQ CPU 2.70GHz
[info] metrics aggregation (50 metrics, 100k tasks per stage): Best Time(ms) Avg Time(ms)
[info] --------------------------------------------------------------------------------------
[info] 1 stage(s) 727 886
[info] 2 stage(s) 1722 1983
[info] 3 stage(s) 2752 3013
[info]
[info] Stage Count Stage Proc. Time Aggreg. Time
[info] 1 408 177
[info] 2 389 423
[info] 3 372 660
```
So the new code is faster than the old when processing task events, and about
an order of maginute faster when aggregating metrics.
Note this still leaves room for improvement; for example, using the above
measurements, 600ms is still a huge amount of time to spend in an event
handler. But I'll leave further enhancements for a separate change.
Tested with benchmarking code + existing unit tests.
Closes#26218 from vanzin/SPARK-29562.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Track timing info for each rule in optimization phase using `QueryPlanningTracker` in Structured Streaming
### Why are the changes needed?
In Structured Streaming we only track rule info in analysis phase, not in optimization phase.
### Does this PR introduce any user-facing change?
No
Closes#25914 from wenxuanguan/spark-29227.
Authored-by: wenxuanguan <choose_home@126.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add UncacheTableStatement and make UNCACHE TABLE go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users. e.g.
```
USE my_catalog
DESC t // success and describe the table t from my_catalog
UNCACHE TABLE t // report table not found as there is no table t in the session catalog
```
### Does this PR introduce any user-facing change?
yes. When running UNCACHE TABLE, Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
New unit tests
Closes#26237 from imback82/uncache_table.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Remove the requirement of fetch_size>=0 from JDBCOptions to allow negative fetch size.
### Why are the changes needed?
Namely, to allow data fetch in stream manner (row-by-row fetch) against MySQL database.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Unit test (JDBCSuite)
This closes#26230 .
Closes#26244 from fuwhu/SPARK-21287-FIX.
Authored-by: fuwhu <bestwwg@163.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
# What changes were proposed in this pull request?
Add description for ignoreNullFields, which is commited in #26098 , in DataFrameWriter and readwriter.py.
Enable user to use ignoreNullFields in pyspark.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
run unit tests
Closes#26227 from stczwd/json-generator-doc.
Authored-by: stczwd <qcsd2011@163.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Only use antlr4 to parse the interval string, and remove the duplicated parsing logic from `CalendarInterval`.
### Why are the changes needed?
Simplify the code and fix inconsistent behaviors.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Pass the Jenkins with the updated test cases.
Closes#26190 from cloud-fan/parser.
Lead-authored-by: Wenchen Fan <wenchen@databricks.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Support SparkSQL use iN/EXISTS with subquery in JOIN condition.
### Why are the changes needed?
Support SQL use iN/EXISTS with subquery in JOIN condition.
### Does this PR introduce any user-facing change?
This PR is for enable user use subquery in `JOIN`'s ON condition. such as we have create three table
```
CREATE TABLE A(id String);
CREATE TABLE B(id String);
CREATE TABLE C(id String);
```
we can do query like :
```
SELECT A.id from A JOIN B ON A.id = B.id and A.id IN (select C.id from C)
```
### How was this patch tested?
ADDED UT
Closes#25854 from AngersZhuuuu/SPARK-29145.
Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Reimplement the iterator in UnsafeExternalRowSorter in database style. This can be done by reusing the `RowIterator` in our code base.
### Why are the changes needed?
During the job in #26164, after involving a var `isReleased` in `hasNext`, there's possible that `isReleased` is false when calling `hasNext`, but it becomes true before calling `next`. A safer way is using database-style iterator: `advanceNext` and `getRow`.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#26229 from xuanyuanking/SPARK-21492-follow-up.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add CacheTableStatement and make CACHE TABLE go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users. e.g.
```
USE my_catalog
DESC t // success and describe the table t from my_catalog
CACHE TABLE t // report table not found as there is no table t in the session catalog
```
### Does this PR introduce any user-facing change?
yes. When running CACHE TABLE, Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
Unit tests.
Closes#26179 from viirya/SPARK-29522.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a follow-up of #24052 to correct assert condition.
### Why are the changes needed?
To test IllegalArgumentException condition..
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual Test (during fixing of SPARK-29453 find this issue)
Closes#26234 from 07ARB/SPARK-29571.
Authored-by: 07ARB <ankitrajboudh@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/26189 to regenerate the result on EC2.
### Why are the changes needed?
This will be used for the other PR reviews.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A.
Closes#26233 from dongjoon-hyun/SPARK-29533.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
`OptimizeLocalShuffleReader` rule is very conservative and gives up optimization as long as there are extra shuffles introduced. It's very likely that most of the added local shuffle readers are fine and only one introduces extra shuffle.
However, it's very hard to make `OptimizeLocalShuffleReader` optimal, a simple workaround is to run this rule again right before executing a query stage.
### Why are the changes needed?
Optimize more shuffle reader to local shuffle reader.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing ut
Closes#26207 from JkSelf/resolve-multi-joins-issue.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add RefreshTableStatement and make REFRESH TABLE go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users. e.g.
```
USE my_catalog
DESC t // success and describe the table t from my_catalog
REFRESH TABLE t // report table not found as there is no table t in the session catalog
```
### Does this PR introduce any user-facing change?
yes. When running REFRESH TABLE, Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
New unit tests
Closes#26183 from imback82/refresh_table.
Lead-authored-by: Terry Kim <yuminkim@gmail.com>
Co-authored-by: Terry Kim <terryk@terrys-mbp-2.lan>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
This moves the tracking of active queries from a per SparkSession state, to the shared SparkSession for better safety in isolated Spark Session environments.
### Why are the changes needed?
We have checks to prevent the restarting of the same stream on the same spark session, but we can actually make that better in multi-tenant environments by actually putting that state in the SharedState instead of SessionState. This would allow a more comprehensive check for multi-tenant clusters.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added tests to StreamingQueryManagerSuite
Closes#26018 from brkyvz/sharedStreamingQueryManager.
Lead-authored-by: Burak Yavuz <burak@databricks.com>
Co-authored-by: Burak Yavuz <brkyvz@gmail.com>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
### What changes were proposed in this pull request?
This PR adds `CREATE NAMESPACE` support for V2 catalogs.
### Why are the changes needed?
Currently, you cannot explicitly create namespaces for v2 catalogs.
### Does this PR introduce any user-facing change?
The user can now perform the following:
```SQL
CREATE NAMESPACE mycatalog.ns
```
to create a namespace `ns` inside `mycatalog` V2 catalog.
### How was this patch tested?
Added unit tests.
Closes#26166 from imback82/create_namespace.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add ShowPartitionsStatement and make SHOW PARTITIONS go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users.
### Does this PR introduce any user-facing change?
Yes. When running SHOW PARTITIONS, Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
Unit tests.
Closes#26198 from huaxingao/spark-29539.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
Add TruncateTableStatement and make TRUNCATE TABLE go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users. e.g.
```
USE my_catalog
DESC t // success and describe the table t from my_catalog
TRUNCATE TABLE t // report table not found as there is no table t in the session catalog
```
### Does this PR introduce any user-facing change?
yes. When running TRUNCATE TABLE, Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
Unit tests.
Closes#26174 from viirya/SPARK-29517.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
We shall have a new mechanism that the downstream operators may notify its parents that they may release the output data stream. In this PR, we implement the mechanism as below:
- Add function named `cleanupResources` in SparkPlan, which default call children's `cleanupResources` function, the operator which need a resource cleanup should rewrite this with the self cleanup and also call `super.cleanupResources`, like SortExec in this PR.
- Add logic support on the trigger side, in this PR is SortMergeJoinExec, which make sure and call the `cleanupResources` to do the cleanup job for all its upstream(children) operator.
### Why are the changes needed?
Bugfix for SortMergeJoin memory leak, and implement a general framework for SparkPlan resource cleanup.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
UT: Add new test suite JoinWithResourceCleanSuite to check both standard and code generation scenario.
Integrate Test: Test with driver/executor default memory set 1g, local mode 10 thread. The below test(thanks taosaildrone for providing this test [here](https://github.com/apache/spark/pull/23762#issuecomment-463303175)) will pass with this PR.
```
from pyspark.sql.functions import rand, col
spark.conf.set("spark.sql.join.preferSortMergeJoin", "true")
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", -1)
# spark.conf.set("spark.sql.sortMergeJoinExec.eagerCleanupResources", "true")
r1 = spark.range(1, 1001).select(col("id").alias("timestamp1"))
r1 = r1.withColumn('value', rand())
r2 = spark.range(1000, 1001).select(col("id").alias("timestamp2"))
r2 = r2.withColumn('value2', rand())
joined = r1.join(r2, r1.timestamp1 == r2.timestamp2, "inner")
joined = joined.coalesce(1)
joined.explain()
joined.show()
```
Closes#26164 from xuanyuanking/SPARK-21492.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
As I have comment in [SPARK-29516](https://github.com/apache/spark/pull/26172#issuecomment-544364977)
SparkSession.sql() method parse process not under current sparksession's conf, so some configuration about parser is not valid in multi-thread situation.
In this pr, we add a SQLConf parameter to AbstractSqlParser and initial it with SessionState's conf.
Then for each SparkSession's parser process. It will use's it's own SessionState's SQLConf and to be thread safe
### Why are the changes needed?
Fix bug
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
NO
Closes#26187 from AngersZhuuuu/SPARK-29530.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Only invoke `checkAndGlobPathIfNecessary()` when we have to use `InMemoryFileIndex`.
### Why are the changes needed?
Avoid unnecessary function invocation.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass Jenkins.
Closes#26196 from Ngone51/dev-avoid-unnecessary-invocation-on-globpath.
Authored-by: wuyi <ngone_5451@163.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
I extended `ExtractBenchmark` to support the `INTERVAL` type of the `source` parameter of the `date_part` function.
### Why are the changes needed?
- To detect performance issues while changing implementation of the `date_part` function in the future.
- To find out current performance bottlenecks in `date_part` for the `INTERVAL` type
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the benchmark and print out produced values per each `field` value.
Closes#26175 from MaxGekk/extract-interval-benchmark.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Added new benchmark `IntervalBenchmark` to measure performance of interval related functions. In the PR, I added benchmarks for casting strings to interval. In particular, interval strings with `interval` prefix and without it because there is special code for this da576a737c/common/unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java (L100-L103) . And also I added benchmarks for different number of units in interval strings, for example 1 unit is `interval 10 years`, 2 units w/o interval is `10 years 5 months`, and etc.
### Why are the changes needed?
- To find out current performance issues in casting to intervals
- The benchmark can be used while refactoring/re-implementing `CalendarInterval.fromString()` or `CalendarInterval.fromCaseInsensitiveString()`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the benchmark via the command:
```shell
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.IntervalBenchmark"
```
Closes#26189 from MaxGekk/interval-from-string-benchmark.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
```
bit_and(expression) -- The bitwise AND of all non-null input values, or null if none
bit_or(expression) -- The bitwise OR of all non-null input values, or null if none
```
More details:
https://www.postgresql.org/docs/9.3/functions-aggregate.html
### Why are the changes needed?
Postgres, Mysql and many other popular db support them.
### Does this PR introduce any user-facing change?
add two bit agg
### How was this patch tested?
add ut
Closes#26155 from yaooqinn/SPARK-27879.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fix Fix the associated location already exists in `SQLQueryTestSuite`:
```
build/sbt "~sql/test-only *SQLQueryTestSuite -- -z postgreSQL/join.sql"
...
[info] - postgreSQL/join.sql *** FAILED *** (35 seconds, 420 milliseconds)
[info] postgreSQL/join.sql
[info] Expected "[]", but got "[org.apache.spark.sql.AnalysisException
[info] Can not create the managed table('`default`.`tt3`'). The associated location('file:/root/spark/sql/core/spark-warehouse/org.apache.spark.sql.SQLQueryTestSuite/tt3') already exists.;]" Result did not match for query #108
```
### Why are the changes needed?
Fix bug.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26181 from wangyum/TestError.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add RepairTableStatement and make REPAIR TABLE go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users. e.g.
```
USE my_catalog
DESC t // success and describe the table t from my_catalog
MSCK REPAIR TABLE t // report table not found as there is no table t in the session catalog
```
### Does this PR introduce any user-facing change?
yes. When running MSCK REPAIR TABLE, Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
New unit tests
Closes#26168 from imback82/repair_table.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
Current Spark SQL `SHOW FUNCTIONS` don't show `!=`, `<>`, `between`, `case`
But these expressions is truly functions. We should show it in SQL `SHOW FUNCTIONS`
### Why are the changes needed?
SHOW FUNCTIONS show '!=', '<>' , 'between', 'case'
### Does this PR introduce any user-facing change?
SHOW FUNCTIONS show '!=', '<>' , 'between', 'case'
### How was this patch tested?
UT
Closes#26053 from AngersZhuuuu/SPARK-29379.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The `date_part()` function can accept the `source` parameter of the `INTERVAL` type (`CalendarIntervalType`). The following values of the `field` parameter are supported:
- `"MILLENNIUM"` (`"MILLENNIA"`, `"MIL"`, `"MILS"`) - number of millenniums in the given interval. It is `YEAR / 1000`.
- `"CENTURY"` (`"CENTURIES"`, `"C"`, `"CENT"`) - number of centuries in the interval calculated as `YEAR / 100`.
- `"DECADE"` (`"DECADES"`, `"DEC"`, `"DECS"`) - decades in the `YEAR` part of the interval calculated as `YEAR / 10`.
- `"YEAR"` (`"Y"`, `"YEARS"`, `"YR"`, `"YRS"`) - years in a values of `CalendarIntervalType`. It is `MONTHS / 12`.
- `"QUARTER"` (`"QTR"`) - a quarter of year calculated as `MONTHS / 3 + 1`
- `"MONTH"` (`"MON"`, `"MONS"`, `"MONTHS"`) - the months part of the interval calculated as `CalendarInterval.months % 12`
- `"DAY"` (`"D"`, `"DAYS"`) - total number of days in `CalendarInterval.microseconds`
- `"HOUR"` (`"H"`, `"HOURS"`, `"HR"`, `"HRS"`) - the hour part of the interval.
- `"MINUTE"` (`"M"`, `"MIN"`, `"MINS"`, `"MINUTES"`) - the minute part of the interval.
- `"SECOND"` (`"S"`, `"SEC"`, `"SECONDS"`, `"SECS"`) - the seconds part with fractional microsecond part.
- `"MILLISECONDS"` (`"MSEC"`, `"MSECS"`, `"MILLISECON"`, `"MSECONDS"`, `"MS"`) - the millisecond part of the interval with fractional microsecond part.
- `"MICROSECONDS"` (`"USEC"`, `"USECS"`, `"USECONDS"`, `"MICROSECON"`, `"US"`) - the total number of microseconds in the `second`, `millisecond` and `microsecond` parts of the given interval.
- `"EPOCH"` - the total number of seconds in the interval including the fractional part with microsecond precision. Here we assume 365.25 days per year (leap year every four years).
For example:
```sql
> SELECT date_part('days', interval 1 year 10 months 5 days);
5
> SELECT date_part('seconds', interval 30 seconds 1 milliseconds 1 microseconds);
30.001001
```
### Why are the changes needed?
To maintain feature parity with PostgreSQL (https://www.postgresql.org/docs/11/functions-datetime.html#FUNCTIONS-DATETIME-EXTRACT)
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
- Added new test suite `IntervalExpressionsSuite`
- Add new test cases to `date_part.sql`
Closes#25981 from MaxGekk/extract-from-intervals.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
A followup of [#25295](https://github.com/apache/spark/pull/25295).
1) change the logWarning to logDebug in `OptimizeLocalShuffleReader`.
2) update the test to check whether query stage reuse can work well with local shuffle reader.
### Why are the changes needed?
make code robust
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#26157 from JkSelf/followup-25295.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The handling of the catalog across plans should be as follows ([SPARK-29014](https://issues.apache.org/jira/browse/SPARK-29014)):
* The *current* catalog should be used when no catalog is specified
* The default catalog is the catalog *current* is initialized to
* If the *default* catalog is not set, then *current* catalog is the built-in Spark session catalog.
This PR addresses the issue where *current* catalog usage is not followed as describe above.
### Why are the changes needed?
It is a bug as described in the previous section.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Unit tests added.
Closes#26120 from imback82/cleanup_catalog.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `AnalyzeTableStatement` and `AnalyzeColumnStatement`, and make ANALYZE TABLE go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users. e.g.
```
USE my_catalog
DESC t // success and describe the table t from my_catalog
ANALYZE TABLE t // report table not found as there is no table t in the session catalog
```
### Does this PR introduce any user-facing change?
yes. When running ANALYZE TABLE, Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
new tests
Closes#26129 from cloud-fan/analyze-table.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
This PR proposes a few typos:
1. Sparks => Spark's
2. parallize => parallelize
3. doesnt => doesn't
Closes#26140 from plusplusjiajia/fix-typos.
Authored-by: Jiajia Li <jiajia.li@intel.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
BIT_COUNT(N) - Returns the number of bits that are set in the argument N as an unsigned 64-bit integer, or NULL if the argument is NULL
### Why are the changes needed?
Supported by MySQL，Microsoft SQL Server ，etc.
### Does this PR introduce any user-facing change?
add a built-in function
### How was this patch tested?
add uts
Closes#26139 from yaooqinn/SPARK-29491.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This PR takes over #19788. After we split the shuffle fetch protocol from `OpenBlock` in #24565, this optimization can be extended in the new shuffle protocol. Credit to yucai, closes#19788.
### What changes were proposed in this pull request?
This PR adds the support for continuous shuffle block fetching in batch:
- Shuffle client changes:
- Add new feature tag `spark.shuffle.fetchContinuousBlocksInBatch`, implement the decision logic in `BlockStoreShuffleReader`.
- Merge the continuous shuffle block ids in batch if needed in ShuffleBlockFetcherIterator.
- Shuffle server changes:
- Add support in `ExternalBlockHandler` for the external shuffle service side.
- Make `ShuffleBlockResolver.getBlockData` accept getting block data by range.
- Protocol changes:
- Add new block id type `ShuffleBlockBatchId` represent continuous shuffle block ids.
- Extend `FetchShuffleBlocks` and `OneForOneBlockFetcher`.
- After the new shuffle fetch protocol completed in #24565, the backward compatibility for external shuffle service can be controlled by `spark.shuffle.useOldFetchProtocol`.
### Why are the changes needed?
In adaptive execution, one reducer may fetch multiple continuous shuffle blocks from one map output file. However, as the original approach, each reducer needs to fetch those 10 reducer blocks one by one. This way needs many IO and impacts performance. This PR is to support fetching those continuous shuffle blocks in one IO (batch way). See below example:
The shuffle block is stored like below:
The ShuffleId format is s"shuffle_$shuffleId_$mapId_$reduceId", referring to BlockId.scala.
In adaptive execution, one reducer may want to read output for reducer 5 to 14, whose block Ids are from shuffle_0_x_5 to shuffle_0_x_14.
Before this PR, Spark needs 10 disk IOs + 10 network IOs for each output file.
After this PR, Spark only needs 1 disk IO and 1 network IO. This way can reduce IO dramatically.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Add new UT.
Integrate test with setting `spark.sql.adaptive.enabled=true`.
Closes#26040 from xuanyuanking/SPARK-9853.
Lead-authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Co-authored-by: yucai <yyu1@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
bool_or(x) <=> any/some(x) <=> max(x)
bool_and(x) <=> every(x) <=> min(x)
Args:
x: boolean
### Why are the changes needed?
PostgreSQL, Presto and Vertica, etc also support this feature:
### Does this PR introduce any user-facing change?
add new functions support
### How was this patch tested?
add ut
Closes#26126 from yaooqinn/SPARK-27880.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Proposed new expression `SubtractDates` which is used in `date1` - `date2`. It has the `INTERVAL` type, and returns the interval from `date1` (inclusive) and `date2` (exclusive). For example:
```sql
> select date'tomorrow' - date'yesterday';
interval 2 days
```
Closes#26034
### Why are the changes needed?
- To conform the SQL standard which states the result type of `date operand 1` - `date operand 2` must be the interval type. See [4.5.3 Operations involving datetimes and intervals](http://www.contrib.andrew.cmu.edu/~shadow/sql/sql1992.txt).
- Improve Spark SQL UX and allow mixing date and timestamp in subtractions. For example: `select timestamp'now' + (date'2019-10-01' - date'2019-09-15')`
### Does this PR introduce any user-facing change?
Before the query below returns number of days:
```sql
spark-sql> select date'2019-10-05' - date'2018-09-01';
399
```
After it returns an interval:
```sql
spark-sql> select date'2019-10-05' - date'2018-09-01';
interval 1 years 1 months 4 days
```
### How was this patch tested?
- by new tests in `DateExpressionsSuite` and `TypeCoercionSuite`.
- by existing tests in `date.sql`
Closes#26112 from MaxGekk/date-subtract.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
A followup of https://github.com/apache/spark/pull/25295
This PR proposes a few code cleanups:
1. rename the special `getMapSizesByExecutorId` to `getMapSizesByMapIndex`
2. rename the parameter `mapId` to `mapIndex` as that's really a mapper index.
3. `BlockStoreShuffleReader` should take `blocksByAddress` directly instead of a map id.
4. rename `getMapReader` to `getReaderForOneMapper` to be more clearer.
### Why are the changes needed?
make code easier to understand
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#26128 from cloud-fan/followup.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Updating univocity-parsers version to 2.8.3, which adds support for multiple character delimiters
Moving univocity-parsers version to spark-parent pom dependencyManagement section
Adding new utility method to build multi-char delimiter string, which delegates to existing one
Adding tests for multiple character delimited CSV
### What changes were proposed in this pull request?
Adds support for parsing CSV data using multiple-character delimiters. Existing logic for converting the input delimiter string to characters was kept and invoked in a loop. Project dependencies were updated to remove redundant declaration of `univocity-parsers` version, and also to change that version to the latest.
### Why are the changes needed?
It is quite common for people to have delimited data, where the delimiter is not a single character, but rather a sequence of characters. Currently, it is difficult to handle such data in Spark (typically needs pre-processing).
### Does this PR introduce any user-facing change?
Yes. Specifying the "delimiter" option for the DataFrame read, and providing more than one character, will no longer result in an exception. Instead, it will be converted as before and passed to the underlying library (Univocity), which has accepted multiple character delimiters since 2.8.0.
### How was this patch tested?
The `CSVSuite` tests were confirmed passing (including new methods), and `sbt` tests for `sql` were executed.
Closes#26027 from jeff303/SPARK-24540.
Authored-by: Jeff Evans <jeffrey.wayne.evans@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
When inserting a value into a column with the different data type, Spark performs type coercion. Currently, we support 3 policies for the store assignment rules: ANSI, legacy and strict, which can be set via the option "spark.sql.storeAssignmentPolicy":
1. ANSI: Spark performs the type coercion as per ANSI SQL. In practice, the behavior is mostly the same as PostgreSQL. It disallows certain unreasonable type conversions such as converting `string` to `int` and `double` to `boolean`. It will throw a runtime exception if the value is out-of-range(overflow).
2. Legacy: Spark allows the type coercion as long as it is a valid `Cast`, which is very loose. E.g., converting either `string` to `int` or `double` to `boolean` is allowed. It is the current behavior in Spark 2.x for compatibility with Hive. When inserting an out-of-range value to a integral field, the low-order bits of the value is inserted(the same as Java/Scala numeric type casting). For example, if 257 is inserted to a field of Byte type, the result is 1.
3. Strict: Spark doesn't allow any possible precision loss or data truncation in store assignment, e.g., converting either `double` to `int` or `decimal` to `double` is allowed. The rules are originally for Dataset encoder. As far as I know, no mainstream DBMS is using this policy by default.
Currently, the V1 data source uses "Legacy" policy by default, while V2 uses "Strict". This proposal is to use "ANSI" policy by default for both V1 and V2 in Spark 3.0.
### Why are the changes needed?
Following the ANSI SQL standard is most reasonable among the 3 policies.
### Does this PR introduce any user-facing change?
Yes.
The default store assignment policy is ANSI for both V1 and V2 data sources.
### How was this patch tested?
Unit test
Closes#26107 from gengliangwang/ansiPolicyAsDefault.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Implement a rule in the new adaptive execution framework introduced in [SPARK-23128](https://issues.apache.org/jira/browse/SPARK-23128). This rule is used to optimize the shuffle reader to local shuffle reader when smj is converted to bhj in adaptive execution.
## How was this patch tested?
Existing tests
Closes#25295 from JkSelf/localShuffleOptimization.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
move the statement logical plans that were created for v2 commands to a new file `statements.scala`, under the same package of `v2Commands.scala`.
This PR also includes some minor cleanups:
1. remove `private[sql]` from `ParsedStatement` as it's in the private package.
2. remove unnecessary override of `output` and `children`.
3. add missing classdoc.
### Why are the changes needed?
Similar to https://github.com/apache/spark/pull/26111 , this is to better organize the logical plans of data source v2.
It's a bit weird to put the statements in the package `org.apache.spark.sql.catalyst.plans.logical.sql` as `sql` is not a good sub-package name in Spark SQL.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#26125 from cloud-fan/statement.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
There will be 2 times unsafeProjection convert operation When we read a Parquet data file use non-vectorized mode:
1. `ParquetGroupConverter` call unsafeProjection function to covert `SpecificInternalRow` to `UnsafeRow` every times when read Parquet data file use `ParquetRecordReader`.
2. `ParquetFileFormat` will call unsafeProjection function to covert this `UnsafeRow` to another `UnsafeRow` again when partitionSchema is not empty in DataSourceV1 branch, and `PartitionReaderWithPartitionValues` will always do this convert operation in DataSourceV2 branch.
In this pr, remove `unsafeProjection` convert operation in `ParquetGroupConverter` and change `ParquetRecordReader` to produce `SpecificInternalRow` instead of `UnsafeRow`.
### Why are the changes needed?
The first time convert in `ParquetGroupConverter` is redundant and `ParquetRecordReader` return a `InternalRow(SpecificInternalRow)` is enough.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Unit Test
Closes#26106 from LuciferYang/spark-parquet-unsafe-projection.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Refine the document of v2 session catalog config, to clearly explain what it is, when it should be used and how to implement it.
### Why are the changes needed?
Make this config more understandable
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Pass the Jenkins with the newly updated test cases.
Closes#26071 from cloud-fan/config.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to fix the behavior of `mode("default")` to set `SaveMode.ErrorIfExists`. Also, this PR updates the exception message by adding `default` explicitly.
### Why are the changes needed?
This is reported during `GRAPH API` PR. This builder pattern should work like the documentation.
### Does this PR introduce any user-facing change?
Yes if the app has multiple `mode()` invocation including `mode("default")` and the `mode("default")` is the last invocation. This is really a corner case.
- Previously, the last invocation was handled as `No-Op`.
- After this bug fix, it will work like the documentation.
### How was this patch tested?
Pass the Jenkins with the newly added test case.
Closes#26094 from dongjoon-hyun/SPARK-29442.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose to move interval parsing to `CalendarInterval.fromCaseInsensitiveString()` which throws an `IllegalArgumentException` for invalid strings, and reuse it from `CalendarInterval.fromString()`. The former one handles `IllegalArgumentException` only and returns `NULL` for invalid interval strings. This will allow to support interval strings without the `interval` prefix in casting strings to intervals and in interval type constructor because they use `fromString()` for parsing string intervals.
For example:
```sql
spark-sql> select cast('1 year 10 days' as interval);
interval 1 years 1 weeks 3 days
spark-sql> SELECT INTERVAL '1 YEAR 10 DAYS';
interval 1 years 1 weeks 3 days
```
### Why are the changes needed?
To maintain feature parity with PostgreSQL which supports interval strings without prefix:
```sql
# select interval '2 months 1 microsecond';
interval
------------------------
2 mons 00:00:00.000001
```
and to improve Spark SQL UX.
### Does this PR introduce any user-facing change?
Yes, previously parsing of interval strings without `interval` gives `NULL`:
```sql
spark-sql> select interval '2 months 1 microsecond';
NULL
```
After:
```sql
spark-sql> select interval '2 months 1 microsecond';
interval 2 months 1 microseconds
```
### How was this patch tested?
- Added new tests to `CalendarIntervalSuite.java`
- A test for casting strings to intervals in `CastSuite`
- Test for interval type constructor from strings in `ExpressionParserSuite`
Closes#26079 from MaxGekk/interval-str-without-prefix.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, `SHOW NAMESPACES` and `SHOW DATABASES` are separate code paths. This PR merges two implementations.
### Why are the changes needed?
To remove code/behavior duplication
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added new unit tests.
Closes#26006 from imback82/combine_show.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds 2 changes regarding exception handling in `SQLQueryTestSuite` and `ThriftServerQueryTestSuite`
- fixes an expected output sorting issue in `ThriftServerQueryTestSuite` as if there is an exception then there is no need for sort
- introduces common exception handling in those 2 suites with a new `handleExceptions` method
### Why are the changes needed?
Currently `ThriftServerQueryTestSuite` passes on master, but it fails on one of my PRs (https://github.com/apache/spark/pull/23531) with this error (https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/111651/testReport/org.apache.spark.sql.hive.thriftserver/ThriftServerQueryTestSuite/sql_3/):
```
org.scalatest.exceptions.TestFailedException: Expected "
[Recursion level limit 100 reached but query has not exhausted, try increasing spark.sql.cte.recursion.level.limit
org.apache.spark.SparkException]
", but got "
[org.apache.spark.SparkException
Recursion level limit 100 reached but query has not exhausted, try increasing spark.sql.cte.recursion.level.limit]
" Result did not match for query #4 WITH RECURSIVE r(level) AS ( VALUES (0) UNION ALL SELECT level + 1 FROM r ) SELECT * FROM r
```
The unexpected reversed order of expected output (error message comes first, then the exception class) is due to this line: https://github.com/apache/spark/pull/26028/files#diff-b3ea3021602a88056e52bf83d8782de8L146. It should not sort the expected output if there was an error during execution.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UTs.
Closes#26028 from peter-toth/SPARK-29359-better-exception-handling.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Revert this commit 18b7ad2fc5.
### Why are the changes needed?
See https://github.com/apache/spark/pull/16304#discussion_r92753590
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
There is no test for that.
Closes#26101 from MaxGekk/revert-mean-seconds-per-month.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Use `.sameElements` to compare (non-nested) arrays, as `Arrays.deep` is removed in 2.13 and wasn't the best way to do this in the first place.
### Why are the changes needed?
To compile with 2.13.
### Does this PR introduce any user-facing change?
None.
### How was this patch tested?
Existing tests.
Closes#26073 from srowen/SPARK-29416.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Replace `Unit` with equivalent `()` where code refers to the `Unit` companion object.
### Why are the changes needed?
It doesn't compile otherwise in Scala 2.13.
- https://github.com/scala/scala/blob/v2.13.0/src/library/scala/Unit.scala#L30
### Does this PR introduce any user-facing change?
Should be no behavior change at all.
### How was this patch tested?
Existing tests.
Closes#26070 from srowen/SPARK-29411.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR adds an accumulator that computes a global aggregate over a number of rows. A user can define an arbitrary number of aggregate functions which can be computed at the same time.
The accumulator uses the standard technique for implementing (interpreted) aggregation in Spark. It uses projections and manual updates for each of the aggregation steps (initialize buffer, update buffer with new input row, merge two buffers and compute the final result on the buffer). Note that two of the steps (update and merge) use the aggregation buffer both as input and output.
Accumulators do not have an explicit point at which they get serialized. A somewhat surprising side effect is that the buffers of a `TypedImperativeAggregate` go over the wire as-is instead of serializing them. The merging logic for `TypedImperativeAggregate` assumes that the input buffer contains serialized buffers, this is violated by the accumulator's implicit serialization. In order to get around this I have added `mergeBuffersObjects` method that merges two unserialized buffers to `TypedImperativeAggregate`.
### Why are the changes needed?
This is the mechanism we are going to use to implement observable metrics.
### Does this PR introduce any user-facing change?
No, not yet.
### How was this patch tested?
Added `AggregatingAccumulator` test suite.
Closes#26012 from hvanhovell/SPARK-29346.
Authored-by: herman <herman@databricks.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
DataSourceV2 Exec classes (ShowTablesExec, ShowNamespacesExec, etc.) all extend LeafExecNode. This results in running a job when executeCollect() is called. This breaks the previous behavior [SPARK-19650](https://issues.apache.org/jira/browse/SPARK-19650).
A new command physical operator will be introduced form which all V2 Exec classes derive to avoid running a job.
### Why are the changes needed?
It is a bug since the current behavior runs a spark job, which breaks the existing behavior: [SPARK-19650](https://issues.apache.org/jira/browse/SPARK-19650).
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing unit tests.
Closes#26048 from imback82/dsv2_command.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Invocations like `sc.parallelize(Array((1,2)))` cause a compile error in 2.13, like:
```
[ERROR] [Error] /Users/seanowen/Documents/spark_2.13/core/src/test/scala/org/apache/spark/ShuffleSuite.scala:47: overloaded method value apply with alternatives:
(x: Unit,xs: Unit*)Array[Unit] <and>
(x: Double,xs: Double*)Array[Double] <and>
(x: Float,xs: Float*)Array[Float] <and>
(x: Long,xs: Long*)Array[Long] <and>
(x: Int,xs: Int*)Array[Int] <and>
(x: Char,xs: Char*)Array[Char] <and>
(x: Short,xs: Short*)Array[Short] <and>
(x: Byte,xs: Byte*)Array[Byte] <and>
(x: Boolean,xs: Boolean*)Array[Boolean]
cannot be applied to ((Int, Int), (Int, Int), (Int, Int), (Int, Int))
```
Using a `Seq` instead appears to resolve it, and is effectively equivalent.
### Why are the changes needed?
To better cross-build for 2.13.
### Does this PR introduce any user-facing change?
None.
### How was this patch tested?
Existing tests.
Closes#26062 from srowen/SPARK-29401.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Syntax like `'foo` is deprecated in Scala 2.13. Replace usages with `Symbol("foo")`
### Why are the changes needed?
Avoids ~50 deprecation warnings when attempting to build with 2.13.
### Does this PR introduce any user-facing change?
None, should be no functional change at all.
### How was this patch tested?
Existing tests.
Closes#26061 from srowen/SPARK-29392.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Reimplement `org.apache.spark.sql.catalyst.util.QuantileSummaries#merge` and add a test-case showing the previous bug.
### Why are the changes needed?
The original Greenwald-Khanna paper, from which the algorithm behind `approxQuantile` was taken, does not cover how to merge the result of multiple parallel QuantileSummaries. The current implementation violates some invariants and therefore the effective error can be larger than the specified.
### Does this PR introduce any user-facing change?
Yes, for same cases, the results from `approxQuantile` (`percentile_approx` in SQL) will now be within the expected error margin. For example:
```scala
var values = (1 to 100).toArray
val all_quantiles = values.indices.map(i => (i+1).toDouble / values.length).toArray
for (n <- 0 until 5) {
var df = spark.sparkContext.makeRDD(values).toDF("value").repartition(5)
val all_answers = df.stat.approxQuantile("value", all_quantiles, 0.1)
val all_answered_ranks = all_answers.map(ans => values.indexOf(ans)).toArray
val error = all_answered_ranks.zipWithIndex.map({ case (answer, expected) => Math.abs(expected - answer) }).toArray
val max_error = error.max
print(max_error + "\n")
}
```
In the current build it returns:
```
16
12
10
11
17
```
I couldn't run the code with this patch applied to double check the implementation. Can someone please confirm it now outputs at most `10`, please?
### How was this patch tested?
A new unit test was added to uncover the previous bug.
Closes#26029 from sitegui/SPARK-29336.
Authored-by: Guilherme <sitegui@sitegui.com.br>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Add back the resolved logical plan for UPDATE TABLE. It was in https://github.com/apache/spark/pull/25626 before but was removed later.
### Why are the changes needed?
In https://github.com/apache/spark/pull/25626 , we decided to not add the update API in DS v2, but we still want to implement UPDATE for builtin source like JDBC. We should at least add the resolved logical plan.
### Does this PR introduce any user-facing change?
no, UPDATE is still not supported yet.
### How was this patch tested?
new tests.
Closes#26025 from cloud-fan/update.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
This PR aims the followings.
- Refactor `TPCDSQueryBenchmark` to use main method to improve the usability.
- Reduce the number of iteration from 5 to 2 because it takes too long. (2 is okay because we have `Stdev` field now. If there is an irregular run, we can notice easily with that).
- Generate one result file for TPCDS scale factor 1. (Note that this test suite can be used for the other scale factors, too.)
- AWS EC2 `r3.xlarge` with `ami-06f2f779464715dc5 (ubuntu-bionic-18.04-amd64-server-20190722.1)` is used.
This PR adds a JDK8 result based on the TPCDS ScaleFactor 1G data generated by the following.
```
# `spark-tpcds-datagen` needs this. (JDK8)
$ git clone https://github.com/apache/spark.git -b branch-2.4 --depth 1 spark-2.4
$ export SPARK_HOME=$PWD
$ ./build/mvn clean package -DskipTests
# Generate data. (JDK8)
$ git clone gitgithub.com:maropu/spark-tpcds-datagen.git
$ cd spark-tpcds-datagen/
$ build/mvn clean package
$ mkdir -p /data/tpcds
$ ./bin/dsdgen --output-location /data/tpcds/s1 // This need `Spark 2.4`
```
### Why are the changes needed?
Although the generated TPCDS data is random, we can keep the record.
### Does this PR introduce any user-facing change?
No. (This is dev-only test benchmark).
### How was this patch tested?
Manually run the benchmark. Please note that you need to have TPCDS data.
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.TPCDSQueryBenchmark --data-location /data/tpcds/s1"
```
Closes#26049 from dongjoon-hyun/SPARK-25668.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In our PROD env, we have a pure Spark cluster, I think this is also pretty common, where computation is separated from storage layer. In such deploy mode, data locality is never reachable.
And there are some configurations in Spark scheduler to reduce waiting time for data locality(e.g. "spark.locality.wait"). While, problem is that, in listing file phase, the location informations of all the files, with all the blocks inside each file, are all fetched from the distributed file system. Actually, in a PROD environment, a table can be so huge that even fetching all these location informations need take tens of seconds.
To improve such scenario, Spark need provide an option, where data locality can be totally ignored, all we need in the listing file phase are the files locations, without any block location informations.
### Why are the changes needed?
And we made a benchmark in our PROD env, after ignore the block locations, we got a pretty huge improvement.
Table Size | Total File Number | Total Block Number | List File Duration(With Block Location) | List File Duration(Without Block Location)
-- | -- | -- | -- | --
22.6T | 30000 | 120000 | 16.841s | 1.730s
28.8 T | 42001 | 148964 | 10.099s | 2.858s
3.4 T | 20000 | 20000 | 5.833s | 4.881s
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Via ut.
Closes#25869 from wangshisan/SPARK-29189.
Authored-by: gwang3 <gwang3@ebay.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
### What changes were proposed in this pull request?
I introduced new constants `SECONDS_PER_MONTH` and `MILLIS_PER_MONTH`, and reused it in calculations of seconds/milliseconds per month. `SECONDS_PER_MONTH` is 2629746 because the average year of the Gregorian calendar is 365.2425 days long or 60 * 60 * 24 * 365.2425 = 31556952.0 = 12 * 2629746 seconds per year.
### Why are the changes needed?
Spark uses the proleptic Gregorian calendar (see https://issues.apache.org/jira/browse/SPARK-26651) in which the average year is 365.2425 days (see https://en.wikipedia.org/wiki/Gregorian_calendar) but existing implementation assumes 31 days per months or 12 * 31 = 372 days. That's far away from the the truth.
### Does this PR introduce any user-facing change?
Yes, the changes affect at least 3 methods in `GroupStateImpl`, `EventTimeWatermark` and `MonthsBetween`. For example, the `month_between()` function will return different result in some cases.
Before:
```sql
spark-sql> select months_between('2019-09-15', '1970-01-01');
596.4516129
```
After:
```sql
spark-sql> select months_between('2019-09-15', '1970-01-01');
596.45996838
```
### How was this patch tested?
By existing test suite `DateTimeUtilsSuite`, `DateFunctionsSuite` and `DateExpressionsSuite`.
Closes#25998 from MaxGekk/days-in-year.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Added new rules to `TypeCoercion.DateTimeOperations` for the `Subtract` expression which is replaced by existing `TimestampDiff` expression if one of its parameter has the `DATE` type and another one is the `TIMESTAMP` type. The date argument is casted to timestamp.
### Why are the changes needed?
- To maintain feature parity with PostgreSQL which supports subtraction of a date from a timestamp and a timestamp from a date:
```sql
maxim=# select timestamp'now' - date'epoch';
?column?
----------------------------
18175 days 21:07:33.412875
(1 row)
maxim=# select date'2020-01-01' - timestamp'now';
?column?
-------------------------
86 days 02:52:00.945296
(1 row)
```
- To conform to the SQL standard which defines datetime subtraction as an interval.
### Does this PR introduce any user-facing change?
Yes, currently the queries bellow fails with the error:
```sql
spark-sql> select timestamp'now' - date'2019-10-01';
Error in query: cannot resolve '(TIMESTAMP('2019-10-06 21:05:07.234') - DATE '2019-10-01')' due to data type mismatch: differing types in '(TIMESTAMP('2019-10-06 21:05:07.234') - DATE '2019-10-01')' (timestamp and date).; line 1 pos 7;
'Project [unresolvedalias((1570385107234000 - 18170), None)]
+- OneRowRelation
```
after the changes:
```sql
spark-sql> select timestamp'now' - date'2019-10-01';
interval 5 days 21 hours 4 minutes 55 seconds 878 milliseconds
```
### How was this patch tested?
- Add new cases to the `rule for date/timestamp operations` test in `TypeCoercionSuite`
- by 2 new test in `datetime.sql`
Closes#26036 from MaxGekk/date-timestamp-subtract.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Change to use AtomicLong instead of a var in the test.
### Why are the changes needed?
Fix flaky test.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#26020 from xuanyuanking/SPARK-25159.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Added new expression `TimestampDiff` for timestamp subtractions. It accepts 2 timestamp expressions and returns another one of the `CalendarIntervalType`. While creating an instance of `CalendarInterval`, it initializes only the microsecond field by difference of the given timestamps in microseconds, and set the `months` field to zero. Also I added an rule for conversion `Subtract` to `TimestampDiff`, and enabled already ported test queries in `postgreSQL/timestamp.sql`.
### Why are the changes needed?
To maintain feature parity with PostgreSQL which allows to get timestamp difference:
```sql
# select timestamp'today' - timestamp'yesterday';
?column?
----------
1 day
(1 row)
```
### Does this PR introduce any user-facing change?
Yes, previously users got the following error from timestamp subtraction:
```sql
spark-sql> select timestamp'today' - timestamp'yesterday';
Error in query: cannot resolve '(TIMESTAMP('2019-10-04 00:00:00') - TIMESTAMP('2019-10-03 00:00:00'))' due to data type mismatch: '(TIMESTAMP('2019-10-04 00:00:00') - TIMESTAMP('2019-10-03 00:00:00'))' requires (numeric or interval) type, not timestamp; line 1 pos 7;
'Project [unresolvedalias((1570136400000000 - 1570050000000000), None)]
+- OneRowRelation
```
after the changes they should get an interval:
```sql
spark-sql> select timestamp'today' - timestamp'yesterday';
interval 1 days
```
### How was this patch tested?
- Added tests for `TimestampDiff` to `DateExpressionsSuite`
- By new test in `TypeCoercionSuite`.
- Enabled tests in `postgreSQL/timestamp.sql`.
Closes#26022 from MaxGekk/timestamp-diff.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Currently we deal with different `ParsedStatement` in many places and write duplicated catalog/table lookup logic. In general the lookup logic is
1. try look up the catalog by name. If no such catalog, and default catalog is not set, convert `ParsedStatement` to v1 command like `ShowDatabasesCommand`. Otherwise, convert `ParsedStatement` to v2 command like `ShowNamespaces`.
2. try look up the table by name. If no such table, fail. If the table is a `V1Table`, convert `ParsedStatement` to v1 command like `CreateTable`. Otherwise, convert `ParsedStatement` to v2 command like `CreateV2Table`.
However, since the code is duplicated we don't apply this lookup logic consistently. For example, we forget to consider the v2 session catalog in several places.
This PR centralizes the catalog/table lookup logic by 3 rules.
1. `ResolveCatalogs` (in catalyst). This rule resolves v2 catalog from the multipart identifier in SQL statements, and convert the statement to v2 command if the resolved catalog is not session catalog. If the command needs to resolve the table (e.g. ALTER TABLE), put an `UnresolvedV2Table` in the command.
2. `ResolveTables` (in catalyst). It resolves `UnresolvedV2Table` to `DataSourceV2Relation`.
3. `ResolveSessionCatalog` (in sql/core). This rule is only effective if the resolved catalog is session catalog. For commands that don't need to resolve the table, this rule converts the statement to v1 command directly. Otherwise, it converts the statement to v1 command if the resolved table is v1 table, and convert to v2 command if the resolved table is v2 table. Hopefully we can remove this rule eventually when v1 fallback is not needed anymore.
### Why are the changes needed?
Reduce duplicated code and make the catalog/table lookup logic consistent.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#25747 from cloud-fan/lookup.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add access modifier `protected` for `sparkConf` in SQLQueryTestSuite, because in the parent trait SharedSparkSession, it is protected.
### Why are the changes needed?
Code consistency.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing UT.
Closes#26019 from xuanyuanking/SPARK-29203.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. With ANSI store assignment policy, an exception is thrown on casting failure
2. Introduce a new expression `AnsiCast` for the ANSI store assignment policy, so that the store assignment policy configuration won't affect the general `Cast`.
### Why are the changes needed?
As per ANSI SQL standard, ANSI store assignment policy should throw an exception on insertion failure, such as inserting out-of-range value to a numeric field.
### Does this PR introduce any user-facing change?
With ANSI store assignment policy, an exception is thrown on casting failure
### How was this patch tested?
Unit test
Closes#25997 from gengliangwang/newCast.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Dynamic partition pruning filters are added as an in-subquery containing a `BroadcastExchangeExec` in case of a broadcast hash join. This PR makes the `ReuseExchange` rule visit in-subquery nodes, to ensure the new `BroadcastExchangeExec` added by dynamic partition pruning can be reused.
### Why are the changes needed?
This initial dynamic partition pruning PR did not enable this reuse, which means a broadcast exchange would be executed twice, in the main query and in the DPP filter.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added broadcast exchange reuse check in `DynamicPartitionPruningSuite`
Closes#26015 from maryannxue/exchange-reuse.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Add an overload for the higher order function `filter` that takes array index as its second argument to `org.apache.spark.sql.functions`.
### Why are the changes needed?
See: SPARK-28962 and SPARK-27297. Specifically ueshin pointing out the discrepency here: https://github.com/apache/spark/pull/24232#issuecomment-533288653
### Does this PR introduce any user-facing change?
### How was this patch tested?
Updated the these test suites:
`test.org.apache.spark.sql.JavaHigherOrderFunctionsSuite`
and
`org.apache.spark.sql.DataFrameFunctionsSuite`
Closes#26007 from nvander1/add_index_overload_for_filter.
Authored-by: Nik Vanderhoof <nikolasrvanderhoof@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
This PR renames `object JSONBenchmark` to `object JsonBenchmark` and the benchmark result file `JSONBenchmark-results.txt` to `JsonBenchmark-results.txt`.
### Why are the changes needed?
Since the file name doesn't match with `object JSONBenchmark`, it makes a confusion when we run the benchmark. In addition, this makes the automation difficult.
```
$ find . -name JsonBenchmark.scala
./sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonBenchmark.scala
```
```
$ build/sbt "sql/test:runMain org.apache.spark.sql.execution.datasources.json.JsonBenchmark"
[info] Running org.apache.spark.sql.execution.datasources.json.JsonBenchmark
[error] Error: Could not find or load main class org.apache.spark.sql.execution.datasources.json.JsonBenchmark
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This is just renaming.
Closes#26008 from dongjoon-hyun/SPARK-RENAME-JSON.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR regenerates the `sql/core` benchmarks in JDK8/11 to compare the result. In general, we compare the ratio instead of the time. However, in this PR, the average time is compared. This PR should be considered as a rough comparison.
**A. EXPECTED CASES(JDK11 is faster in general)**
- [x] BloomFilterBenchmark (JDK11 is faster except one case)
- [x] BuiltInDataSourceWriteBenchmark (JDK11 is faster at CSV/ORC)
- [x] CSVBenchmark (JDK11 is faster except five cases)
- [x] ColumnarBatchBenchmark (JDK11 is faster at `boolean`/`string` and some cases in `int`/`array`)
- [x] DatasetBenchmark (JDK11 is faster with `string`, but is slower for `long` type)
- [x] ExternalAppendOnlyUnsafeRowArrayBenchmark (JDK11 is faster except two cases)
- [x] ExtractBenchmark (JDK11 is faster except HOUR/MINUTE/SECOND/MILLISECONDS/MICROSECONDS)
- [x] HashedRelationMetricsBenchmark (JDK11 is faster)
- [x] JSONBenchmark (JDK11 is much faster except eight cases)
- [x] JoinBenchmark (JDK11 is faster except five cases)
- [x] OrcNestedSchemaPruningBenchmark (JDK11 is faster in nine cases)
- [x] PrimitiveArrayBenchmark (JDK11 is faster)
- [x] SortBenchmark (JDK11 is faster except `Arrays.sort` case)
- [x] UDFBenchmark (N/A, values are too small)
- [x] UnsafeArrayDataBenchmark (JDK11 is faster except one case)
- [x] WideTableBenchmark (JDK11 is faster except two cases)
**B. CASES WE NEED TO INVESTIGATE MORE LATER**
- [x] AggregateBenchmark (JDK11 is slower in general)
- [x] CompressionSchemeBenchmark (JDK11 is slower in general except `string`)
- [x] DataSourceReadBenchmark (JDK11 is slower in general)
- [x] DateTimeBenchmark (JDK11 is slightly slower in general except `parsing`)
- [x] MakeDateTimeBenchmark (JDK11 is slower except two cases)
- [x] MiscBenchmark (JDK11 is slower except ten cases)
- [x] OrcV2NestedSchemaPruningBenchmark (JDK11 is slower)
- [x] ParquetNestedSchemaPruningBenchmark (JDK11 is slower except six cases)
- [x] RangeBenchmark (JDK11 is slower except one case)
`FilterPushdownBenchmark/InExpressionBenchmark/WideSchemaBenchmark` will be compared later because it took long timer.
### Why are the changes needed?
According to the result, there are some difference between JDK8/JDK11.
This will be a baseline for the future improvement and comparison. Also, as a reproducible environment, the following environment is used.
- Instance: `r3.xlarge`
- OS: `CentOS Linux release 7.5.1804 (Core)`
- JDK:
- `OpenJDK Runtime Environment (build 1.8.0_222-b10)`
- `OpenJDK Runtime Environment 18.9 (build 11.0.4+11-LTS)`
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This is a test-only PR. We need to run benchmark.
Closes#26003 from dongjoon-hyun/SPARK-29320.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Scala 2.13 removes the parallel collections classes to a separate library, so first, this establishes a `scala-2.13` profile to bring it back, for future use.
However the library enables use of `.par` implicit conversions via a new class that is not in 2.12, which makes cross-building hard. This implements a suggested workaround from https://github.com/scala/scala-parallel-collections/issues/22 to avoid `.par` entirely.
### Why are the changes needed?
To compile for 2.13 and later to work with 2.13.
### Does this PR introduce any user-facing change?
Should not, no.
### How was this patch tested?
Existing tests.
Closes#25980 from srowen/SPARK-29296.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
LOAD DATA command resolves the partition column name as case sensitive manner,
where as in insert commandthe partition column name will be resolved using
the SQLConf resolver where the names will be resolved based on `spark.sql.caseSensitive` property. Same logic can be applied for resolving the partition column names in LOAD COMMAND.
### Why are the changes needed?
It's to handle the partition column name correctly according to the configuration.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT and manual testing.
Closes#24903 from sujith71955/master_paritionColName.
Lead-authored-by: s71955 <sujithchacko.2010@gmail.com>
Co-authored-by: sujith71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR proposes to avoid abstract classes introduced at https://github.com/apache/spark/pull/24965 but instead uses trait and object.
- `abstract class BaseArrowPythonRunner` -> `trait PythonArrowOutput` to allow mix-in
**Before:**
```
BasePythonRunner
├── BaseArrowPythonRunner
│ ├── ArrowPythonRunner
│ └── CoGroupedArrowPythonRunner
├── PythonRunner
└── PythonUDFRunner
```
**After:**
```
└── BasePythonRunner
├── ArrowPythonRunner
├── CoGroupedArrowPythonRunner
├── PythonRunner
└── PythonUDFRunner
```
- `abstract class BasePandasGroupExec ` -> `object PandasGroupUtils` to decouple
**Before:**
```
└── BasePandasGroupExec
├── FlatMapGroupsInPandasExec
└── FlatMapCoGroupsInPandasExec
```
**After:**
```
├── FlatMapGroupsInPandasExec
└── FlatMapCoGroupsInPandasExec
```
### Why are the changes needed?
The problem is that R code path is being matched with Python side:
**Python:**
```
└── BasePythonRunner
├── ArrowPythonRunner
├── CoGroupedArrowPythonRunner
├── PythonRunner
└── PythonUDFRunner
```
**R:**
```
└── BaseRRunner
├── ArrowRRunner
└── RRunner
```
I would like to match the hierarchy and decouple other stuff for now if possible. Ideally we should deduplicate both code paths. Internal implementation is also similar intentionally.
`BasePandasGroupExec` case is similar as well. R (with Arrow optimization, in particular) has some duplicated codes with Pandas UDFs.
`FlatMapGroupsInRWithArrowExec` <> `FlatMapGroupsInPandasExec`
`MapPartitionsInRWithArrowExec` <> `ArrowEvalPythonExec`
In order to prepare deduplication here as well, it might better avoid changing hierarchy alone in Python side.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Locally tested existing tests. Jenkins tests should verify this too.
Closes#25989 from HyukjinKwon/SPARK-29317.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Lambda functions to array `filter` can now take as input the index as well as the element. This behavior matches array `transform`.
### Why are the changes needed?
See JIRA. It's generally useful, and particularly so if you're working with fixed length arrays.
### Does this PR introduce any user-facing change?
Previously filter lambdas had to look like
`filter(arr, el -> whatever)`
Now, lambdas can take an index argument as well
`filter(array, (el, idx) -> whatever)`
### How was this patch tested?
I added unit tests to `HigherOrderFunctionsSuite`.
Closes#25666 from henrydavidge/filter-idx.
Authored-by: Henry D <henrydavidge@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
This PR exposes USE CATALOG/USE SQL commands as described in this [SPIP](https://docs.google.com/document/d/1jEcvomPiTc5GtB9F7d2RTVVpMY64Qy7INCA_rFEd9HQ/edit#)
It also exposes `currentCatalog` in `CatalogManager`.
Finally, it changes `SHOW NAMESPACES` and `SHOW TABLES` to use the current catalog if no catalog is specified (instead of default catalog).
### Why are the changes needed?
There is currently no mechanism to change current catalog/namespace thru SQL commands.
### Does this PR introduce any user-facing change?
Yes, you can perform the following:
```scala
// Sets the current catalog to 'testcat'
spark.sql("USE CATALOG testcat")
// Sets the current catalog to 'testcat' and current namespace to 'ns1.ns2'.
spark.sql("USE ns1.ns2 IN testcat")
// Now, the following will use 'testcat' as the current catalog and 'ns1.ns2' as the current namespace.
spark.sql("SHOW NAMESPACES")
```
### How was this patch tested?
Added new unit tests.
Closes#25771 from imback82/use_namespace.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to specify the save mode explicitly while writing to the `noop` datasource in benchmarks. I set `Overwrite` mode in the following benchmarks:
- JsonBenchmark
- CSVBenchmark
- UDFBenchmark
- MakeDateTimeBenchmark
- ExtractBenchmark
- DateTimeBenchmark
- NestedSchemaPruningBenchmark
### Why are the changes needed?
Otherwise writing to `noop` fails with:
```
[error] Exception in thread "main" org.apache.spark.sql.AnalysisException: TableProvider implementation noop cannot be written with ErrorIfExists mode, please use Append or Overwrite modes instead.;
[error] at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:284)
```
most likely due to https://github.com/apache/spark/pull/25876
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
I generated results of `ExtractBenchmark` via the command:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.ExtractBenchmark"
```
Closes#25988 from MaxGekk/noop-overwrite-mode.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Added new expression `SecondWithFraction` which produces the `seconds` part of timestamps/dates with fractional part containing microseconds. This expression is used only in the `DatePart` expression. As the result, `date_part()` and `extract` return seconds and microseconds as the fractional part of the seconds part when `field` is `SECOND` (or synonyms).
### Why are the changes needed?
The `date_part()` and `extract` were added to maintain feature parity with PostgreSQL which has different behavior for the `SECOND` value of the `field` parameter. The fix is needed to behave in the same way. Here is PostgreSQL's output:
```sql
# SELECT date_part('SECONDS', timestamp'2019-10-01 00:00:01.000001');
date_part
-----------
1.000001
(1 row)
```
### Does this PR introduce any user-facing change?
Yes, type of `date_part('SECOND', ...)` is changed from `INT` to `DECIMAL(8, 6)`.
Before:
```sql
spark-sql> SELECT date_part('SECONDS', '2019-10-01 00:00:01.000001');
1
```
After:
```sql
spark-sql> SELECT date_part('SECONDS', '2019-10-01 00:00:01.000001');
1.000001
```
### How was this patch tested?
- Added new tests to `DateExpressionSuite` for the `SecondWithFraction` expression
- Regenerated results of `date_part.sql`, `extract.sql` and `timestamp.sql`
- Updated results of `ExtractBenchmark`
Closes#25986 from MaxGekk/extract-seconds-from-timestamp.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to remove `scalatest` deprecation warnings with the following changes.
- `org.scalatest.mockito.MockitoSugar` -> `org.scalatestplus.mockito.MockitoSugar`
- `org.scalatest.selenium.WebBrowser` -> `org.scalatestplus.selenium.WebBrowser`
- `org.scalatest.prop.Checkers` -> `org.scalatestplus.scalacheck.Checkers`
- `org.scalatest.prop.GeneratorDrivenPropertyChecks` -> `org.scalatestplus.scalacheck.ScalaCheckDrivenPropertyChecks`
### Why are the changes needed?
According to the Jenkins logs, there are 118 warnings about this.
```
grep "is deprecated" ~/consoleText | grep scalatest | wc -l
118
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
After Jenkins passes, we need to check the Jenkins log.
Closes#25982 from dongjoon-hyun/SPARK-29307.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Check schema fields to see if they contain the exact column name, add to error message in DataSet#resolve
Add test for extra error message piece
Adds an additional check in `DataSet#resolve`, in the else clause (i.e. column not resolved), that appends a suffix to the error message for the `AnalysisException` if that column name is literally found in the schema fields, to suggest to the user that it might need to be quoted via backticks.
### Why are the changes needed?
Forgetting to quote such column names is a common occurrence for new Spark users.
### Does this PR introduce any user-facing change?
No (other than the extra suffix on the error message).
### How was this patch tested?
`test` was run for `core` in `sbt`, and passed.
Closes#25807 from jeff303/SPARK-25153.
Authored-by: Jeff Evans <jeffrey.wayne.evans@gmail.com>
Signed-off-by: Holden Karau <hkarau@apple.com>
### What changes were proposed in this pull request?
Scala 2.13 emits a deprecation warning for procedure-like declarations:
```
def foo() {
...
```
This is equivalent to the following, so should be changed to avoid a warning:
```
def foo(): Unit = {
...
```
### Why are the changes needed?
It will avoid about a thousand compiler warnings when we start to support Scala 2.13. I wanted to make the change in 3.0 as there are less likely to be back-ports from 3.0 to 2.4 than 3.1 to 3.0, for example, minimizing that downside to touching so many files.
Unfortunately, that makes this quite a big change.
### Does this PR introduce any user-facing change?
No behavior change at all.
### How was this patch tested?
Existing tests.
Closes#25968 from srowen/SPARK-29291.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Follow up from https://github.com/apache/spark/pull/24981 incorporating some comments from HyukjinKwon.
Specifically:
- Adding `CoGroupedData` to `pyspark/sql/__init__.py __all__` so that documentation is generated.
- Added pydoc, including example, for the use case whereby the user supplies a cogrouping function including a key.
- Added the boilerplate for doctests to cogroup.py. Note that cogroup.py only contains the apply() function which has doctests disabled as per the other Pandas Udfs.
- Restricted the newly exposed RelationalGroupedDataset constructor parameters to access only by the sql package.
- Some minor formatting tweaks.
This was tested by running the appropriate unit tests. I'm unsure as to how to check that my change will cause the documentation to be generated correctly, but it someone can describe how I can do this I'd be happy to check.
Closes#25939 from d80tb7/SPARK-27463-fixes.
Authored-by: Chris Martin <chris@cmartinit.co.uk>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Support the syntax of `ALTER (DATABASE|SCHEMA) database_name SET LOCATION` path. Please note that only Hive 3.x metastore support this syntax.
Ref:
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDLhttps://issues.apache.org/jira/browse/HIVE-8472
### Why are the changes needed?
Support more syntax.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#25883 from wangyum/SPARK-28476.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Added try exception
### Why are the changes needed?
The behaviors of run commands during exception handling are different depends on explain command. I think it should be unified.
[ >spark.sql("explain cost select * from hoge").show(false) ]
[ >spark.sql("explain extended select * from hoge").show(false) ]
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
tested manually
Closes#25848 from TomokoKomiyama/fix-explain.
Authored-by: TomokoKomiyama <btkomiyamatm@oss.nttdata.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
The `SET` commands do not contain the `_FUNC_` pattern a priori. In the PR, I propose filter out such commands in the `using _FUNC_ instead of function names in examples` test.
### Why are the changes needed?
After the merge of https://github.com/apache/spark/pull/25942, examples will require particular settings. Currently, the whole expression example has to be ignored which is so much. It makes sense to ignore only `SET` commands in expression examples.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the `using _FUNC_ instead of function names in examples` test.
Closes#25958 from MaxGekk/dont-check-_FUNC_-in-set.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch fixes examples of Like/RLike to test its origin intention correctly. The example doesn't consider the default value of spark.sql.parser.escapedStringLiterals: it's false by default.
Please take a look at current example of Like:
d72f39897b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/regexpExpressions.scala (L97-L106)
If spark.sql.parser.escapedStringLiterals=false, then it should fail as there's `\U` in pattern (spark.sql.parser.escapedStringLiterals=false by default) but it doesn't fail.
```
The escape character is '\'. If an escape character precedes a special symbol or another
escape character, the following character is matched literally. It is invalid to escape
any other character.
```
For the query
```
SET spark.sql.parser.escapedStringLiterals=false;
SELECT '%SystemDrive%\Users\John' like '\%SystemDrive\%\Users%';
```
SQL parser removes single `\` (not sure that is intended) so the expressions of Like are constructed as following (I've printed out expression of left and right for Like/RLike):
> LIKE - left `%SystemDrive%UsersJohn` / right `\%SystemDrive\%Users%`
which are no longer having origin intention (see left).
Below query tests the origin intention:
```
SET spark.sql.parser.escapedStringLiterals=false;
SELECT '%SystemDrive%\\Users\\John' like '\%SystemDrive\%\\\\Users%';
```
> LIKE - left `%SystemDrive%\Users\John` / right `\%SystemDrive\%\\Users%`
Note that `\\\\` is needed in pattern as `StringUtils.escapeLikeRegex` requires `\\` to represent normal character of `\`.
Same for RLIKE:
```
SET spark.sql.parser.escapedStringLiterals=true;
SELECT '%SystemDrive%\Users\John' rlike '%SystemDrive%\\Users.*';
```
> RLIKE - left `%SystemDrive%\Users\John` / right `%SystemDrive%\\Users.*`
which is OK, but
```
SET spark.sql.parser.escapedStringLiterals=false;
SELECT '%SystemDrive%\Users\John' rlike '%SystemDrive%\Users.*';
```
> RLIKE - left `%SystemDrive%UsersJohn` / right `%SystemDrive%Users.*`
which no longer haves origin intention.
Below query tests the origin intention:
```
SET spark.sql.parser.escapedStringLiterals=true;
SELECT '%SystemDrive%\\Users\\John' rlike '%SystemDrive%\\\\Users.*';
```
> RLIKE - left `%SystemDrive%\Users\John` / right `%SystemDrive%\\Users.*`
### Why are the changes needed?
Because the example doesn't test the origin intention. Spark is now running automated tests from these examples, so now it's not only documentation issue but also test issue.
### Does this PR introduce any user-facing change?
No, as it only corrects documentation.
### How was this patch tested?
Added debug log (like above) and ran queries from `spark-sql`.
Closes#25957 from HeartSaVioR/SPARK-29281.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to clone Spark session per-each expression example. Examples can modify SQL settings, and can influence on each other if they run in the same Spark session in parallel.
### Why are the changes needed?
This should fix test failures like [this](https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-maven-hadoop-3.2-jdk-11/478/testReport/junit/org.apache.spark.sql/SQLQuerySuite/check_outputs_of_expression_examples/) checking of the `Like` example:
```
org.apache.spark.sql.AnalysisException: the pattern '\%SystemDrive\%\Users%' is invalid, the escape character is not allowed to precede 'U';
at org.apache.spark.sql.catalyst.util.StringUtils$.fail$1(StringUtils.scala:48)
at org.apache.spark.sql.catalyst.util.StringUtils$.escapeLikeRegex(StringUtils.scala:57)
at org.apache.spark.sql.catalyst.expressions.Like.escape(regexpExpressions.scala:108)
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running `check outputs of expression examples` in `org.apache.spark.sql.SQLQuerySuite`
Closes#25956 from MaxGekk/fix-expr-examples-checks.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
SPARK-27210 enables ManifestFileCommitProtocol to clean up incomplete output files in task level if task is aborted.
This patch extends the area of cleaning up, proposes ManifestFileCommitProtocol to clean up complete but invalid output files in job level if job aborts. Please note that this works as 'best-effort', not kind of guarantee, as we have in HadoopMapReduceCommitProtocol.
## How was this patch tested?
Added UT.
Closes#24186 from HeartSaVioR/SPARK-27254.
Lead-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Co-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
### What changes were proposed in this pull request?
New test compares outputs of expression examples in comments with results of `hiveResultString()`. Also I fixed existing examples where actual and expected outputs are different.
### Why are the changes needed?
This prevents mistakes in expression examples, and fixes existing mistakes in comments.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Add new test to `SQLQuerySuite`.
Closes#25942 from MaxGekk/run-expr-examples.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Call fs.exists only when necessary in InsertIntoHadoopFsRelationCommand.
### Why are the changes needed?
When saving a dataframe into Hadoop, spark first checks if the file exists before inspecting the SaveMode to determine if it should actually insert data. However, the pathExists variable is actually not used in the case of SaveMode.Append. In some file systems, the exists call can be expensive and hence this PR makes that call only when necessary.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing unit tests should cover it since this doesn't change the behavior.
Closes#25928 from rahij/rr/exists-upstream.
Authored-by: Rahij Ramsharan <rramsharan@palantir.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
After https://github.com/apache/spark/pull/25158 and https://github.com/apache/spark/pull/25458, SQL features of PostgreSQL are introduced into Spark. AFAIK, both features are implementation-defined behaviors, which are not specified in ANSI SQL.
In such a case, this proposal is to add a configuration `spark.sql.dialect` for choosing a database dialect.
After this PR, Spark supports two database dialects, `Spark` and `PostgreSQL`. With `PostgreSQL` dialect, Spark will:
1. perform integral division with the / operator if both sides are integral types;
2. accept "true", "yes", "1", "false", "no", "0", and unique prefixes as input and trim input for the boolean data type.
### Why are the changes needed?
Unify the external database dialect with one configuration, instead of small flags.
### Does this PR introduce any user-facing change?
A new configuration `spark.sql.dialect` for choosing a database dialect.
### How was this patch tested?
Existing tests.
Closes#25697 from gengliangwang/dialect.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Rename the package pgSQL to postgreSQL
### Why are the changes needed?
To address the comment in https://github.com/apache/spark/pull/25697#discussion_r328431070 . The official full name seems more reasonable.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#25936 from gengliangwang/renamePGSQL.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
It is very confusing that the default save mode is different between the internal implementation of a Data source. The reason that we had to have saveModeForDSV2 was that there was no easy way to check the existence of a Table in DataSource v2. Now, we have catalogs for that. Therefore we should be able to remove the different save modes. We also have a plan forward for `save`, where we can't really check the existence of a table, and therefore create one. That will come in a future PR.
### Why are the changes needed?
Because it is confusing that the internal implementation of a data source (which is generally non-obvious to users) decides which default save mode is used within Spark.
### Does this PR introduce any user-facing change?
It changes the default save mode for V2 Tables in the DataFrameWriter APIs
### How was this patch tested?
Existing tests
Closes#25876 from brkyvz/removeSM.
Lead-authored-by: Burak Yavuz <brkyvz@gmail.com>
Co-authored-by: Burak Yavuz <burak@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch proposes to skip PlanExpression when doing subexpression elimination on executors.
### Why are the changes needed?
Subexpression elimination can possibly cause NPE when applying on execution subquery expression like ScalarSubquery on executors. It is because PlanExpression wraps query plan. To compare query plan on executor when eliminating subexpression, can cause unexpected error, like NPE when accessing transient fields.
The NPE looks like:
```
[info] - SPARK-29239: Subquery should not cause NPE when eliminating subexpression *** FAILED *** (175 milliseconds)
[info] org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1395.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1395.0 (TID 3447, 10.0.0.196, executor driver): java.lang.NullPointerException
[info] at org.apache.spark.sql.execution.LocalTableScanExec.stringArgs(LocalTableScanExec.scala:62)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.argString(TreeNode.scala:506)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.simpleString(TreeNode.scala:534)
[info] at org.apache.spark.sql.catalyst.plans.QueryPlan.simpleString(QueryPlan.scala:179)
[info] at org.apache.spark.sql.catalyst.plans.QueryPlan.verboseString(QueryPlan.scala:181)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.generateTreeString(TreeNode.scala:647)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.generateTreeString(TreeNode.scala:675)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.generateTreeString(TreeNode.scala:675)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.treeString(TreeNode.scala:569)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.treeString(TreeNode.scala:559)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.treeString(TreeNode.scala:551)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.toString(TreeNode.scala:548)
[info] at org.apache.spark.sql.catalyst.errors.package$TreeNodeException.<init>(package.scala:36)
[info] at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.makeCopy(TreeNode.scala:436)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.makeCopy(TreeNode.scala:425)
[info] at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:102)
[info] at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:63)
[info] at org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:132)
[info] at org.apache.spark.sql.catalyst.plans.QueryPlan.doCanonicalize(QueryPlan.scala:261)
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added unit test.
Closes#25925 from viirya/SPARK-29239.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Don't allow calling append, overwrite, or overwritePartitions after tableProperty is used in DataFrameWriterV2 because table properties are not set as part of operations on existing tables. Only tables that are created or replaced can set table properties.
### Why are the changes needed?
The properties are discarded otherwise, so this avoids confusing behavior.
### Does this PR introduce any user-facing change?
Yes, but to a new API, DataFrameWriterV2.
### How was this patch tested?
Removed test cases that used this method and the append, etc. methods because they no longer compile.
Closes#25931 from rdblue/fix-dfw-v2-table-properties.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to replace function names in some expression examples by `_FUNC_`, and add a test to check that `_FUNC_` always present in all examples.
### Why are the changes needed?
Binding of a function name to an expression is performed in `FunctionRegistry` which is single source of truth. Expression examples should avoid using function name directly because this can make the examples invalid in the future.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added new test to `SQLQuerySuite` which analyses expression example, and check presence of `_FUNC_`.
Closes#25924 from MaxGekk/fix-func-examples.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
when the current catalog is session catalog, get/set the current namespace from/to the `SessionCatalog`.
### Why are the changes needed?
It's super confusing that we don't have a single source of truth for the current namespace of the session catalog. It can be in `CatalogManager` or `SessionCatalog`.
Ideally, we should always track the current catalog/namespace in `CatalogManager`. However, there are many commands that do not support v2 catalog API. They ignore the current catalog in `CatalogManager` and blindly go to `SessionCatalog`. This means, we must keep track of the current namespace of session catalog even if the current catalog is not session catalog.
Thus, we can't use `CatalogManager` to track the current namespace of session catalog because it changes when the current catalog is changed. To keep single source of truth, we should only track the current namespace of session catalog in `SessionCatalog`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Newly added and updated test cases.
Closes#25903 from cloud-fan/current.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
Move the rule `RemoveAllHints` after the batch `Resolution`.
### Why are the changes needed?
User-defined hints can be resolved by the rules injected via `extendedResolutionRules` or `postHocResolutionRules`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added a test case
Closes#25746 from gatorsmile/moveRemoveAllHints.
Authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
partition path should be qualified to store in catalog.
There are some scenes:
1. ALTER TABLE t PARTITION(b=1) SET LOCATION '/path/x'
should be qualified: file:/path/x
**Hive 2.0.0 does not support for location without schema here.**
```
FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. {0} is not absolute or has no scheme information. Please specify a complete absolute uri with scheme information.
```
2. ALTER TABLE t PARTITION(b=1) SET LOCATION 'x'
should be qualified: file:/tablelocation/x
**Hive 2.0.0 does not support for relative location here.**
3. ALTER TABLE t ADD PARTITION(b=1) LOCATION '/path/x'
should be qualified: file:/path/x
**the same with Hive 2.0.0**
4. ALTER TABLE t ADD PARTITION(b=1) LOCATION 'x'
should be qualified: file:/tablelocation/x
**the same with Hive 2.0.0**
Currently only ALTER TABLE t ADD PARTITION(b=1) LOCATION for hive serde table has the expected qualified path. we should make other scenes to be consist with it.
Another change is for alter table location.
## How was this patch tested?
add / modify existing TestCases
Closes#17254 from windpiger/qualifiedPartitionPath.
Authored-by: windpiger <songjun@outlook.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR reduce shuffle partitions from 200 to 4 in `SQLQueryTestSuite` to reduce testing time.
### Why are the changes needed?
Reduce testing time.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually tested in my local:
Before:
```
...
[info] - subquery/in-subquery/in-joins.sql (6 minutes, 19 seconds)
[info] - subquery/in-subquery/not-in-joins.sql (2 minutes, 17 seconds)
[info] - subquery/scalar-subquery/scalar-subquery-predicate.sql (45 seconds, 763 milliseconds)
...
Run completed in 1 hour, 22 minutes.
```
After:
```
...
[info] - subquery/in-subquery/in-joins.sql (1 minute, 12 seconds)
[info] - subquery/in-subquery/not-in-joins.sql (27 seconds, 541 milliseconds)
[info] - subquery/scalar-subquery/scalar-subquery-predicate.sql (17 seconds, 360 milliseconds)
...
Run completed in 47 minutes.
```
Closes#25891 from wangyum/SPARK-29203.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
This PR supports UPDATE in the parser and add the corresponding logical plan. The SQL syntax is a standard UPDATE statement:
```
UPDATE tableName tableAlias SET colName=value [, colName=value]+ WHERE predicate?
```
### Why are the changes needed?
With this change, we can start to implement UPDATE in builtin sources and think about how to design the update API in DS v2.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
New test cases added.
Closes#25626 from xianyinxin/SPARK-28892.
Authored-by: xy_xin <xianyin.xxy@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr proposes to check method bytecode size in `BenchmarkQueryTest`. This metric is critical for performance numbers.
### Why are the changes needed?
For performance checks
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#25788 from maropu/CheckMethodSize.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR aims to add tag `ExtendedSQLTest` for `SQLQueryTestSuite`.
This doesn't affect our Jenkins test coverage.
Instead, this tag gives us an ability to parallelize them by splitting this test suite and the other suites.
### Why are the changes needed?
`SQLQueryTestSuite` takes 45 mins alone because it has many SQL scripts to run.
<img width="906" alt="time" src="https://user-images.githubusercontent.com/9700541/65353553-4af0f100-dba2-11e9-9f2f-386742d28f92.png">
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
```
build/sbt "sql/test-only *.SQLQueryTestSuite" -Dtest.exclude.tags=org.apache.spark.tags.ExtendedSQLTest
...
[info] SQLQueryTestSuite:
[info] ScalaTest
[info] Run completed in 3 seconds, 147 milliseconds.
[info] Total number of tests run: 0
[info] Suites: completed 1, aborted 0
[info] Tests: succeeded 0, failed 0, canceled 0, ignored 0, pending 0
[info] No tests were executed.
[info] Passed: Total 0, Failed 0, Errors 0, Passed 0
[success] Total time: 22 s, completed Sep 20, 2019 12:23:13 PM
```
Closes#25872 from dongjoon-hyun/SPARK-29191.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Supported special string values for `DATE` type. They are simply notational shorthands that will be converted to ordinary date values when read. The following string values are supported:
- `epoch [zoneId]` - `1970-01-01`
- `today [zoneId]` - the current date in the time zone specified by `spark.sql.session.timeZone`.
- `yesterday [zoneId]` - the current date -1
- `tomorrow [zoneId]` - the current date + 1
- `now` - the date of running the current query. It has the same notion as `today`.
For example:
```sql
spark-sql> SELECT date 'tomorrow' - date 'yesterday';
2
```
### Why are the changes needed?
To maintain feature parity with PostgreSQL, see [8.5.1.4. Special Values](https://www.postgresql.org/docs/12/datatype-datetime.html)
### Does this PR introduce any user-facing change?
Previously, the parser fails on the special values with the error:
```sql
spark-sql> select date 'today';
Error in query:
Cannot parse the DATE value: today(line 1, pos 7)
```
After the changes, the special values are converted to appropriate dates:
```sql
spark-sql> select date 'today';
2019-09-06
```
### How was this patch tested?
- Added tests to `DateFormatterSuite` to check parsing special values from regular strings.
- Tests in `DateTimeUtilsSuite` check parsing those values from `UTF8String`
- Uncommented tests in `date.sql`
Closes#25708 from MaxGekk/datetime-special-values.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Refactoring of the `DateTimeUtils.getEpoch()` function by avoiding decimal operations that are pretty expensive, and converting the final result to the decimal type at the end.
### Why are the changes needed?
The changes improve performance of the `getEpoch()` method at least up to **20 times**.
Before:
```
Invoke extract for timestamp: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
cast to timestamp 256 277 33 39.0 25.6 1.0X
EPOCH of timestamp 23455 23550 131 0.4 2345.5 0.0X
```
After:
```
Invoke extract for timestamp: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
cast to timestamp 255 294 34 39.2 25.5 1.0X
EPOCH of timestamp 1049 1054 9 9.5 104.9 0.2X
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By existing test from `DateExpressionSuite`.
Closes#25881 from MaxGekk/optimize-extract-epoch.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Changed the `DateTimeUtils.getMilliseconds()` by avoiding the decimal division, and replacing it by setting scale and precision while converting microseconds to the decimal type.
### Why are the changes needed?
This improves performance of `extract` and `date_part()` by more than **50 times**:
Before:
```
Invoke extract for timestamp: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative Invoke extract for timestamp: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
cast to timestamp 397 428 45 25.2 39.7 1.0X
MILLISECONDS of timestamp 36723 36761 63 0.3 3672.3 0.0X
```
After:
```
Invoke extract for timestamp: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
cast to timestamp 278 284 6 36.0 27.8 1.0X
MILLISECONDS of timestamp 592 606 13 16.9 59.2 0.5X
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By existing test suite - `DateExpressionsSuite`
Closes#25871 from MaxGekk/optimize-epoch-millis.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch fixes the issue brought by [SPARK-21870](http://issues.apache.org/jira/browse/SPARK-21870): when generating code for parameter type, it doesn't consider array type in javaType. At least we have one, Spark should generate code for BinaryType as `byte[]`, but Spark create the code for BinaryType as `[B` and generated code fails compilation.
Below is the generated code which failed compilation (Line 380):
```
/* 380 */ private void agg_doAggregate_count_0([B agg_expr_1_1, boolean agg_exprIsNull_1_1, org.apache.spark.sql.catalyst.InternalRow agg_unsafeRowAggBuffer_1) throws java.io.IOException {
/* 381 */ // evaluate aggregate function for count
/* 382 */ boolean agg_isNull_26 = false;
/* 383 */ long agg_value_28 = -1L;
/* 384 */ if (!false && agg_exprIsNull_1_1) {
/* 385 */ long agg_value_31 = agg_unsafeRowAggBuffer_1.getLong(1);
/* 386 */ agg_isNull_26 = false;
/* 387 */ agg_value_28 = agg_value_31;
/* 388 */ } else {
/* 389 */ long agg_value_33 = agg_unsafeRowAggBuffer_1.getLong(1);
/* 390 */
/* 391 */ long agg_value_32 = -1L;
/* 392 */
/* 393 */ agg_value_32 = agg_value_33 + 1L;
/* 394 */ agg_isNull_26 = false;
/* 395 */ agg_value_28 = agg_value_32;
/* 396 */ }
/* 397 */ // update unsafe row buffer
/* 398 */ agg_unsafeRowAggBuffer_1.setLong(1, agg_value_28);
/* 399 */ }
```
There wasn't any test for HashAggregateExec specifically testing this, but randomized test in ObjectHashAggregateSuite could encounter this and that's why ObjectHashAggregateSuite is flaky.
### Why are the changes needed?
Without the fix, generated code from HashAggregateExec may fail compilation.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added new UT. Without the fix, newly added UT fails.
Closes#25830 from HeartSaVioR/SPARK-29140.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to change behavior of the `date_part()` function in handling `null` field, and make it the same as PostgreSQL has. If `field` parameter is `null`, the function should return `null` of the `double` type as PostgreSQL does:
```sql
# select date_part(null, date '2019-09-20');
date_part
-----------
(1 row)
# select pg_typeof(date_part(null, date '2019-09-20'));
pg_typeof
------------------
double precision
(1 row)
```
### Why are the changes needed?
The `date_part()` function was added to maintain feature parity with PostgreSQL but current behavior of the function is different in handling null as `field`.
### Does this PR introduce any user-facing change?
Yes.
Before:
```sql
spark-sql> select date_part(null, date'2019-09-20');
Error in query: null; line 1 pos 7
```
After:
```sql
spark-sql> select date_part(null, date'2019-09-20');
NULL
```
### How was this patch tested?
Add new tests to `DateFunctionsSuite for 2 cases:
- `field` = `null`, `source` = a date literal
- `field` = `null`, `source` = a date column
Closes#25865 from MaxGekk/date_part-null.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Modify the approach in `DataFrameNaFunctions.fillValue`, the new one uses `df.withColumns` which only address the columns need to be filled. After this change, there are no more ambiguous fileds detected for joined dataframe.
### Why are the changes needed?
Before this change, when you have a joined table that has the same field name from both original table, fillna will fail even if you specify a subset that does not include the 'ambiguous' fields.
```
scala> val df1 = Seq(("f1-1", "f2", null), ("f1-2", null, null), ("f1-3", "f2", "f3-1"), ("f1-4", "f2", "f3-1")).toDF("f1", "f2", "f3")
scala> val df2 = Seq(("f1-1", null, null), ("f1-2", "f2", null), ("f1-3", "f2", "f4-1")).toDF("f1", "f2", "f4")
scala> val df_join = df1.alias("df1").join(df2.alias("df2"), Seq("f1"), joinType="left_outer")
scala> df_join.na.fill("", cols=Seq("f4"))
org.apache.spark.sql.AnalysisException: Reference 'f2' is ambiguous, could be: df1.f2, df2.f2.;
```
### Does this PR introduce any user-facing change?
Yes, fillna operation will pass and give the right answer for a joined table.
### How was this patch tested?
Local test and newly added UT.
Closes#25768 from xuanyuanking/SPARK-29063.
Lead-authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR allows Python toLocalIterator to prefetch the next partition while the first partition is being collected. The PR also adds a demo micro bench mark in the examples directory, we may wish to keep this or not.
### Why are the changes needed?
In https://issues.apache.org/jira/browse/SPARK-23961 / 5e79ae3b40 we changed PySpark to only pull one partition at a time. This is memory efficient, but if partitions take time to compute this can mean we're spending more time blocking.
### Does this PR introduce any user-facing change?
A new param is added to toLocalIterator
### How was this patch tested?
New unit test inside of `test_rdd.py` checks the time that the elements are evaluated at. Another test that the results remain the same are added to `test_dataframe.py`.
I also ran a micro benchmark in the examples directory `prefetch.py` which shows an improvement of ~40% in this specific use case.
>
> 19/08/16 17:11:36 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
> Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
> Setting default log level to "WARN".
> To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
> Running timers:
>
> [Stage 32:> (0 + 1) / 1]
> Results:
>
> Prefetch time:
>
> 100.228110831
>
>
> Regular time:
>
> 188.341721614
>
>
>
Closes#25515 from holdenk/SPARK-27659-allow-pyspark-tolocalitr-to-prefetch.
Authored-by: Holden Karau <hkarau@apple.com>
Signed-off-by: Holden Karau <hkarau@apple.com>
### What changes were proposed in this pull request?
Currently the checks in the Analyzer require that V2 Tables have BATCH_WRITE defined for all tables that have V1 Write fallbacks. This is confusing as these tables may not have the V2 writer interface implemented yet. This PR adds this table capability to these checks.
In addition, this allows V2 tables to leverage the V1 APIs for DataFrameWriter.save if they do extend the V1_BATCH_WRITE capability. This way, these tables can continue to receive partitioning information and also perform checks for the existence of tables, and support all SaveModes.
### Why are the changes needed?
Partitioned saves through DataFrame.write are otherwise broken for V2 tables that support the V1
write API.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
V1WriteFallbackSuite
Closes#25767 from brkyvz/bwcheck.
Authored-by: Burak Yavuz <brkyvz@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr is to propagate all the SQL configurations to executors in `SQLQueryTestSuite`. When the propagation enabled in the tests, a potential bug below becomes apparent;
```
CREATE TABLE num_data (id int, val decimal(38,10)) USING parquet;
....
select sum(udf(CAST(null AS Decimal(38,0)))) from range(1,4): QueryOutput(select sum(udf(CAST(null AS Decimal(38,0)))) from range(1,4),struct<>,java.lang.IllegalArgumentException
[info] requirement failed: MutableProjection cannot use UnsafeRow for output data types: decimal(38,0)) (SQLQueryTestSuite.scala:380)
```
The root culprit is that `InterpretedMutableProjection` has incorrect validation in the interpreter mode: `validExprs.forall { case (e, _) => UnsafeRow.isFixedLength(e.dataType) }`. This validation should be the same with the condition (`isMutable`) in `HashAggregate.supportsAggregate`: https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala#L1126
### Why are the changes needed?
Bug fixes.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added tests in `AggregationQuerySuite`
Closes#25831 from maropu/SPARK-29122.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This is a follow-up of the [review comment](https://github.com/apache/spark/pull/25706#discussion_r321923311).
This patch unifies the default wait time to be 10 seconds as it would fit most of UTs (as they have smaller timeouts) and doesn't bring additional latency since it will return if the condition is met.
This patch doesn't touch the one which waits 100000 milliseconds (100 seconds), to not break anything unintentionally, though I'd rather questionable that we really need to wait for 100 seconds.
### Why are the changes needed?
It simplifies the test code and get rid of various heuristic values on timeout.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
CI build will test the patch, as it would be the best environment to test the patch (builds are running there).
Closes#25837 from HeartSaVioR/MINOR-unify-default-wait-time-for-wait-until-empty.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR aims to use `tryWithResource` for ORC file.
### Why are the changes needed?
This is a follow-up to address https://github.com/apache/spark/pull/25006#discussion_r298788206 .
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#25842 from dongjoon-hyun/SPARK-28208.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This adds a new write API as proposed in the [SPIP to standardize logical plans](https://issues.apache.org/jira/browse/SPARK-23521). This new API:
* Uses clear verbs to execute writes, like `append`, `overwrite`, `create`, and `replace` that correspond to the new logical plans.
* Only creates v2 logical plans so the behavior is always consistent.
* Does not allow table configuration options for operations that cannot change table configuration. For example, `partitionedBy` can only be called when the writer executes `create` or `replace`.
Here are a few example uses of the new API:
```scala
df.writeTo("catalog.db.table").append()
df.writeTo("catalog.db.table").overwrite($"date" === "2019-06-01")
df.writeTo("catalog.db.table").overwritePartitions()
df.writeTo("catalog.db.table").asParquet.create()
df.writeTo("catalog.db.table").partitionedBy(days($"ts")).createOrReplace()
df.writeTo("catalog.db.table").using("abc").replace()
```
## How was this patch tested?
Added `DataFrameWriterV2Suite` that tests the new write API. Existing tests for v2 plans.
Closes#25681 from rdblue/SPARK-28612-add-data-frame-writer-v2.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
### What changes were proposed in this pull request?
Currently, there are new configurations for compatibility with ANSI SQL:
* `spark.sql.parser.ansi.enabled`
* `spark.sql.decimalOperations.nullOnOverflow`
* `spark.sql.failOnIntegralTypeOverflow`
This PR is to add new configuration `spark.sql.ansi.enabled` and remove the 3 options above. When the configuration is true, Spark tries to conform to the ANSI SQL specification. It will be disabled by default.
### Why are the changes needed?
Make it simple and straightforward.
### Does this PR introduce any user-facing change?
The new features for ANSI compatibility will be set via one configuration `spark.sql.ansi.enabled`.
### How was this patch tested?
Existing unit tests.
Closes#25693 from gengliangwang/ansiEnabled.
Lead-authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Refactored SQL-related benchmark and made them depend on `SqlBasedBenchmark`. In particular, creation of Spark session are moved into `override def getSparkSession: SparkSession`.
### Why are the changes needed?
This should simplify maintenance of SQL-based benchmarks by reducing the number of dependencies. In the future, it should be easier to refactor & extend all SQL benchmarks by changing only one trait. Finally, all SQL-based benchmarks will look uniformly.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the modified benchmarks.
Closes#25828 from MaxGekk/sql-benchmarks-refactoring.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Previous comment was true for Apache Spark 2.3.0. The 2.4.0 release brought multiple watermark policy and therefore stating that the 'min' is always chosen is misleading.
This PR updates the comments about multiple watermark policy. They aren't true anymore since in case of multiple watermarks, we can configure which one will be applied to the query. This change was brought with Apache Spark 2.4.0 release.
### Why are the changes needed?
It introduces some confusion about the real execution of the commented code.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
The tests weren't added because the change is only about the documentation level. I affirm that the contribution is my original work and that I license the work to the project under the project's open source license.
Closes#25832 from bartosz25/fix_comments_multiple_watermark_policy.
Authored-by: bartosz25 <bartkonieczny@yahoo.fr>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
It upgrades ORC from 1.5.5 to 1.5.6 and adds closes the ORC readers when they aren't used to
create RecordReaders.
## How was this patch tested?
The changed unit tests were run.
Closes#25006 from omalley/spark-28208.
Lead-authored-by: Owen O'Malley <omalley@apache.org>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
#DataSet
fruit,color,price,quantity
apple,red,1,3
banana,yellow,2,4
orange,orange,3,5
xxx
This PR aims to fix the below
```
scala> spark.conf.set("spark.sql.csv.parser.columnPruning.enabled", false)
scala> spark.read.option("header", "true").option("mode", "DROPMALFORMED").csv("fruit.csv").count
res1: Long = 4
```
This is caused by the issue [SPARK-24645](https://issues.apache.org/jira/browse/SPARK-24645).
SPARK-24645 issue can also be solved by [SPARK-25387](https://issues.apache.org/jira/browse/SPARK-25387)
### Why are the changes needed?
SPARK-24645 caused this regression, so reverted the code as it can also be solved by SPARK-25387
### Does this PR introduce any user-facing change?
No,
### How was this patch tested?
Added UT, and also tested the bug SPARK-24645
**SPARK-24645 regression**
Closes#25820 from sandeep-katta/SPARK-29101.
Authored-by: sandeep katta <sandeep.katta2007@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Supported special string values for `TIMESTAMP` type. They are simply notational shorthands that will be converted to ordinary timestamp values when read. The following string values are supported:
- `epoch [zoneId]` - `1970-01-01 00:00:00+00 (Unix system time zero)`
- `today [zoneId]` - midnight today.
- `yesterday [zoneId]` -midnight yesterday
- `tomorrow [zoneId]` - midnight tomorrow
- `now` - current query start time.
For example:
```sql
spark-sql> SELECT timestamp 'tomorrow';
2019-09-07 00:00:00
```
### Why are the changes needed?
To maintain feature parity with PostgreSQL, see [8.5.1.4. Special Values](https://www.postgresql.org/docs/12/datatype-datetime.html)
### Does this PR introduce any user-facing change?
Previously, the parser fails on the special values with the error:
```sql
spark-sql> select timestamp 'today';
Error in query:
Cannot parse the TIMESTAMP value: today(line 1, pos 7)
```
After the changes, the special values are converted to appropriate dates:
```sql
spark-sql> select timestamp 'today';
2019-09-06 00:00:00
```
### How was this patch tested?
- Added tests to `TimestampFormatterSuite` to check parsing special values from regular strings.
- Tests in `DateTimeUtilsSuite` check parsing those values from `UTF8String`
- Uncommented tests in `timestamp.sql`
Closes#25716 from MaxGekk/timestamp-special-values.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. After https://github.com/apache/spark/pull/21599, if the option "spark.sql.failOnIntegralTypeOverflow" is enabled, all the Binary Arithmetic operator will used the exact version function.
However, only `Add`/`Substract`/`Multiply` has a corresponding exact function in java.lang.Math . When the option "spark.sql.failOnIntegralTypeOverflow" is enabled, a runtime exception "BinaryArithmetics must override either exactMathMethod or genCode" is thrown if the other Binary Arithmetic operators are used, such as "Divide", "Remainder".
The exact math method should be called only when there is a corresponding function in `java.lang.Math`
2. Revise the log output of casting to `Int`/`Short`
3. Enable `spark.sql.failOnIntegralTypeOverflow` for pgSQL tests in `SQLQueryTestSuite`.
### Why are the changes needed?
1. Fix the bugs of https://github.com/apache/spark/pull/21599
2. The test case of pgSQL intends to check the overflow of integer/long type. We should enable `spark.sql.failOnIntegralTypeOverflow`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Unit test.
Closes#25804 from gengliangwang/enableIntegerOverflowInSQLTest.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In this PR, I fix some annotation errors and remove meaningless annotations in project.
### Why are the changes needed?
There are some annotation errors and meaningless annotations in project.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Verified manually.
Closes#25809 from turboFei/SPARK-29113.
Authored-by: turbofei <fwang12@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Added new benchmarks for `make_date()` and `make_timestamp()` to detect performance issues, and figure out functions speed on foldable arguments.
- `make_date()` is benchmarked on fully foldable arguments.
- `make_timestamp()` is benchmarked on corner case `60.0`, foldable time fields and foldable date.
### Why are the changes needed?
To find out inputs where `make_date()` and `make_timestamp()` have performance problems. This should be useful in the future optimizations of the functions and users apps.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the benchmark and manually checking generated dates/timestamps.
Closes#25813 from MaxGekk/make_datetime-benchmark.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This pr refines the code of DELETE, including, 1, make `whereClause` to be optional, in which case DELETE will delete all of the data of a table; 2, add more test cases; 3, some other refines.
This is a following-up of SPARK-28351.
### Why are the changes needed?
An optional where clause in DELETE respects the SQL standard.
### Does this PR introduce any user-facing change?
Yes. But since this is a non-released feature, this change does not have any end-user affects.
### How was this patch tested?
New case is added.
Closes#25652 from xianyinxin/SPARK-28950.
Authored-by: xy_xin <xianyin.xxy@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to create an instance of `TimestampFormatter` only once at the initialization, and reuse it inside of `nullSafeEval()` and `doGenCode()` in the case when the `fmt` parameter is foldable.
### Why are the changes needed?
The changes improve performance of the `date_format()` function.
Before:
```
format date: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
format date wholestage off 7180 / 7181 1.4 718.0 1.0X
format date wholestage on 7051 / 7194 1.4 705.1 1.0X
```
After:
```
format date: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
format date wholestage off 4787 / 4839 2.1 478.7 1.0X
format date wholestage on 4736 / 4802 2.1 473.6 1.0X
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
By existing test suites `DateExpressionsSuite` and `DateFunctionsSuite`.
Closes#25782 from MaxGekk/date_format-foldable.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pr proposes to define an individual method for each common subexpression in HashAggregateExec. In the current master, the common subexpr elimination code in HashAggregateExec is expanded in a single method; 4664a082c2/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala (L397)
The method size can be too big for JIT compilation, so I believe splitting it is beneficial for performance. For example, in a query `SELECT SUM(a + b), AVG(a + b + c) FROM VALUES (1, 1, 1) t(a, b, c)`,
the current master generates;
```
/* 098 */ private void agg_doConsume_0(InternalRow localtablescan_row_0, int agg_expr_0_0, int agg_expr_1_0, int agg_expr_2_0) throws java.io.IOException {
/* 099 */ // do aggregate
/* 100 */ // common sub-expressions
/* 101 */ int agg_value_6 = -1;
/* 102 */
/* 103 */ agg_value_6 = agg_expr_0_0 + agg_expr_1_0;
/* 104 */
/* 105 */ int agg_value_5 = -1;
/* 106 */
/* 107 */ agg_value_5 = agg_value_6 + agg_expr_2_0;
/* 108 */ boolean agg_isNull_4 = false;
/* 109 */ long agg_value_4 = -1L;
/* 110 */ if (!false) {
/* 111 */ agg_value_4 = (long) agg_value_5;
/* 112 */ }
/* 113 */ int agg_value_10 = -1;
/* 114 */
/* 115 */ agg_value_10 = agg_expr_0_0 + agg_expr_1_0;
/* 116 */ // evaluate aggregate functions and update aggregation buffers
/* 117 */ agg_doAggregate_sum_0(agg_value_10);
/* 118 */ agg_doAggregate_avg_0(agg_value_4, agg_isNull_4);
/* 119 */
/* 120 */ }
```
On the other hand, this pr generates;
```
/* 121 */ private void agg_doConsume_0(InternalRow localtablescan_row_0, int agg_expr_0_0, int agg_expr_1_0, int agg_expr_2_0) throws java.io.IOException {
/* 122 */ // do aggregate
/* 123 */ // common sub-expressions
/* 124 */ long agg_subExprValue_0 = agg_subExpr_0(agg_expr_2_0, agg_expr_0_0, agg_expr_1_0);
/* 125 */ int agg_subExprValue_1 = agg_subExpr_1(agg_expr_0_0, agg_expr_1_0);
/* 126 */ // evaluate aggregate functions and update aggregation buffers
/* 127 */ agg_doAggregate_sum_0(agg_subExprValue_1);
/* 128 */ agg_doAggregate_avg_0(agg_subExprValue_0);
/* 129 */
/* 130 */ }
```
I run some micro benchmarks for this pr;
```
(base) maropu~:$system_profiler SPHardwareDataType
Hardware:
Hardware Overview:
Processor Name: Intel Core i5
Processor Speed: 2 GHz
Number of Processors: 1
Total Number of Cores: 2
L2 Cache (per Core): 256 KB
L3 Cache: 4 MB
Memory: 8 GB
(base) maropu~:$java -version
java version "1.8.0_181"
Java(TM) SE Runtime Environment (build 1.8.0_181-b13)
Java HotSpot(TM) 64-Bit Server VM (build 25.181-b13, mixed mode)
(base) maropu~:$ /bin/spark-shell --master=local[1] --conf spark.driver.memory=8g --conf spark.sql.shurtitions=1 -v
val numCols = 40
val colExprs = "id AS key" +: (0 until numCols).map { i => s"id AS _c$i" }
spark.range(3000000).selectExpr(colExprs: _*).createOrReplaceTempView("t")
val aggExprs = (2 until numCols).map { i =>
(0 until i).map(d => s"_c$d")
.mkString("AVG(", " + ", ")")
}
// Drops the time of a first run then pick that of a second run
timer { sql(s"SELECT ${aggExprs.mkString(", ")} FROM t").write.format("noop").save() }
// the master
maxCodeGen: 12957
Elapsed time: 36.309858661s
// this pr
maxCodeGen=4184
Elapsed time: 2.399490285s
```
### Why are the changes needed?
To avoid the too-long-function issue in JVMs.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added tests in `WholeStageCodegenSuite`
Closes#25710 from maropu/SplitSubexpr.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This pr proposes to print bytecode statistics (max class bytecode size, max method bytecode size, max constant pool size, and # of inner classes) for generated classes in debug prints, `debugCodegen`. Since these metrics are critical for codegen framework developments, I think its worth printing there. This pr intends to enable `debugCodegen` to print these metrics as following;
```
scala> sql("SELECT sum(v) FROM VALUES(1) t(v)").debugCodegen
Found 2 WholeStageCodegen subtrees.
== Subtree 1 / 2 (maxClassCodeSize:2693; maxMethodCodeSize:124; maxConstantPoolSize:130(0.20% used); numInnerClasses:0) ==
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
*(1) HashAggregate(keys=[], functions=[partial_sum(cast(v#0 as bigint))], output=[sum#5L])
+- *(1) LocalTableScan [v#0]
Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIteratorForCodegenStage1(references);
/* 003 */ }
...
```
### Why are the changes needed?
For efficient developments
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manually tested
Closes#25766 from maropu/PrintBytecodeStats.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to fix comments of date-time expressions, and replace the `yyyy` pattern by `uuuu` when the implementation supposes the former one.
### Why are the changes needed?
To make comments consistent to implementations.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running Scala Style checker.
Closes#25796 from MaxGekk/year-pattern-uuuu-followup.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
The `Column.isInCollection()` with a large size collection will generate an expression with large size children expressions. This make analyzer and optimizer take a long time to run.
In this PR, in `isInCollection()` function, directly generate `InSet` expression, avoid generating too many children expressions.
### Why are the changes needed?
`Column.isInCollection()` with a large size collection sometimes become a bottleneck when running sql.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually benchmark it in spark-shell:
```
def testExplainTime(collectionSize: Int) = {
val df = spark.range(10).withColumn("id2", col("id") + 1)
val list = Range(0, collectionSize).toList
val startTime = System.currentTimeMillis()
df.where(col("id").isInCollection(list)).where(col("id2").isInCollection(list)).explain()
val elapsedTime = System.currentTimeMillis() - startTime
println(s"cost time: ${elapsedTime}ms")
}
```
Then test on collection size 5, 10, 100, 1000, 10000, test result is:
collection size | explain time (before) | explain time (after)
------ | ------ | ------
5 | 26ms | 29ms
10 | 30ms | 48ms
100 | 104ms | 50ms
1000 | 1202ms | 58ms
10000 | 10012ms | 523ms
Closes#25754 from WeichenXu123/improve_in_collection.
Lead-authored-by: WeichenXu <weichen.xu@databricks.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
This PR adds a utility class `AdaptiveSparkPlanHelper` which provides methods related to tree traversal of an `AdaptiveSparkPlanExec` plan. Unlike their counterparts in `TreeNode` or
`QueryPlan`, these methods traverse down leaf nodes of adaptive plans, i.e., `AdaptiveSparkPlanExec` and `QueryStageExec`.
### Why are the changes needed?
This utility class can greatly simplify tree traversal code for adaptive spark plans.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Refined `AdaptiveQueryExecSuite` with the help of the new utility methods.
Closes#25764 from maryannxue/aqe-utils.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to extend `ExtractBenchmark` and add new ones for:
- `EXTRACT` and `DATE` as input column
- the `DATE_PART` function and `DATE`/`TIMESTAMP` input column
### Why are the changes needed?
The `EXTRACT` expression is rebased on the `DATE_PART` expression by the PR https://github.com/apache/spark/pull/25410 where some of sub-expressions take `DATE` column as the input (`Millennium`, `Year` and etc.) but others require `TIMESTAMP` column (`Hour`, `Minute`). Separate benchmarks for `DATE` should exclude overhead of implicit conversions `DATE` <-> `TIMESTAMP`.
### Does this PR introduce any user-facing change?
No, it doesn't.
### How was this patch tested?
- Regenerated results of `ExtractBenchmark`
Closes#25772 from MaxGekk/date_part-benchmark.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
reorganize the packages of DS v2 interfaces/classes:
1. `org.spark.sql.connector.catalog`: put `TableCatalog`, `Table` and other related interfaces/classes
2. `org.spark.sql.connector.expression`: put `Expression`, `Transform` and other related interfaces/classes
3. `org.spark.sql.connector.read`: put `ScanBuilder`, `Scan` and other related interfaces/classes
4. `org.spark.sql.connector.write`: put `WriteBuilder`, `BatchWrite` and other related interfaces/classes
### Why are the changes needed?
Data Source V2 has evolved a lot. It's a bit weird that `Expression` is in `org.spark.sql.catalog.v2` and `Table` is in `org.spark.sql.sources.v2`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#25700 from cloud-fan/package.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In method `SQLMetricsTestUtils.testMetricsDynamicPartition()`, there is a CREATE TABLE sentence without `withTable` block. It causes test failure if use same table name in other unit tests.
### Why are the changes needed?
To avoid "table already exists" in tests.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Exist UT
Closes#25752 from LantaoJin/SPARK-29045.
Authored-by: LantaoJin <jinlantao@gmail.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
# What changes were proposed in this pull request?
This patch fixes the bug regarding NPE in SQLConf.get, which is only possible when SparkContext._dagScheduler is null due to stopping SparkContext. The logic doesn't seem to consider active SparkContext could be in progress of stopping.
Note that it can't be encountered easily as `SparkContext.stop()` blocks the main thread, but there're many cases which SQLConf.get is accessed concurrently while SparkContext.stop() is executing - users run another threads, or listener is accessing SQLConf.get after dagScheduler is set to null (this is the case what I encountered.)
### Why are the changes needed?
The bug brings NPE.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added new UT to verify NPE doesn't occur. Without patch, the test fails with throwing NPE.
Closes#25753 from HeartSaVioR/SPARK-29046.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
JIRA :https://issues.apache.org/jira/browse/SPARK-29050
'a hdfs' change into 'an hdfs'
'an unique' change into 'a unique'
'an url' change into 'a url'
'a error' change into 'an error'
Closes#25756 from dengziming/feature_fix_typos.
Authored-by: dengziming <dengziming@growingio.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Remove `InsertIntoTable` and replace it's usage by `InsertIntoStatement`
### Why are the changes needed?
`InsertIntoTable` and `InsertIntoStatement` are almost identical (except some namings). It doesn't make sense to keep 2 identical plans. After the removal of `InsertIntoTable`, the analysis process becomes:
1. parser creates `InsertIntoStatement`
2. v2 rule `ResolveInsertInto` converts `InsertIntoStatement` to v2 commands.
3. v1 rules like `DataSourceAnalysis` and `HiveAnalysis` convert `InsertIntoStatement` to v1 commands.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#25763 from cloud-fan/remove.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Implement the SHOW DATABASES logical and physical plans for data source v2 tables.
### Why are the changes needed?
To support `SHOW DATABASES` SQL commands for v2 tables.
### Does this PR introduce any user-facing change?
`spark.sql("SHOW DATABASES")` will return namespaces if the default catalog is set:
```
+---------------+
| namespace|
+---------------+
| ns1|
| ns1.ns1_1|
|ns1.ns1_1.ns1_2|
+---------------+
```
### How was this patch tested?
Added unit tests to `DataSourceV2SQLSuite`.
Closes#25601 from imback82/show_databases.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Removes useless `Properties` created according to hvanhovell 's suggestion.
### Why are the changes needed?
Avoid useless code.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
existing UTs
Closes#25742 from mgaido91/SPARK-28939_followup.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR aims to recover the JDK11 compilation with a workaround.
For now, the master branch is broken like the following due to a [Scala bug](https://github.com/scala/bug/issues/10418) which is fixed in `2.13.0-RC2`.
```
[ERROR] [Error] /spark/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecutionRDD.scala:42: ambiguous reference to overloaded definition,
both method putAll in class Properties of type (x$1: java.util.Map[_, _])Unit
and method putAll in class Hashtable of type (x$1: java.util.Map[_ <: Object, _ <: Object])Unit
match argument types (java.util.Map[String,String])
```
- https://github.com/apache/spark/actions (JDK11 build monitoring)
### Why are the changes needed?
This workaround recovers JDK11 compilation.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual build with JDK11 because this is JDK11 compilation fix.
- Jenkins builds with JDK8 and tests with JDK11.
- GitHub action will verify this after merging.
Closes#25738 from dongjoon-hyun/SPARK-28939.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Remove the project node if the streaming scan is columnar
### Why are the changes needed?
This is a followup of https://github.com/apache/spark/pull/25586. Batch and streaming share the same DS v2 read API so both can support columnar reads. We should apply #25586 to streaming scan as well.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#25727 from cloud-fan/follow.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a ANSI SQL and feature id is `T312`
```
<binary overlay function> ::=
OVERLAY <left paren> <binary value expression> PLACING <binary value expression>
FROM <start position> [ FOR <string length> ] <right paren>
```
This PR related to https://github.com/apache/spark/pull/24918 and support treat byte array.
ref: https://www.postgresql.org/docs/11/functions-binarystring.html
## How was this patch tested?
new UT.
There are some show of the PR on my production environment.
```
spark-sql> select overlay(encode('Spark SQL', 'utf-8') PLACING encode('_', 'utf-8') FROM 6);
Spark_SQL
Time taken: 0.285 s
spark-sql> select overlay(encode('Spark SQL', 'utf-8') PLACING encode('CORE', 'utf-8') FROM 7);
Spark CORE
Time taken: 0.202 s
spark-sql> select overlay(encode('Spark SQL', 'utf-8') PLACING encode('ANSI ', 'utf-8') FROM 7 FOR 0);
Spark ANSI SQL
Time taken: 0.165 s
spark-sql> select overlay(encode('Spark SQL', 'utf-8') PLACING encode('tructured', 'utf-8') FROM 2 FOR 4);
Structured SQL
Time taken: 0.141 s
```
Closes#25172 from beliefer/ansi-overlay-byte-array.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
- Remove SQLContext.createExternalTable and Catalog.createExternalTable, deprecated in favor of createTable since 2.2.0, plus tests of deprecated methods
- Remove HiveContext, deprecated in 2.0.0, in favor of `SparkSession.builder.enableHiveSupport`
- Remove deprecated KinesisUtils.createStream methods, plus tests of deprecated methods, deprecate in 2.2.0
- Remove deprecated MLlib (not Spark ML) linear method support, mostly utility constructors and 'train' methods, and associated docs. This includes methods in LinearRegression, LogisticRegression, Lasso, RidgeRegression. These have been deprecated since 2.0.0
- Remove deprecated Pyspark MLlib linear method support, including LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD
- Remove 'runs' argument in KMeans.train() method, which has been a no-op since 2.0.0
- Remove deprecated ChiSqSelector isSorted protected method
- Remove deprecated 'yarn-cluster' and 'yarn-client' master argument in favor of 'yarn' and deploy mode 'cluster', etc
Notes:
- I was not able to remove deprecated DataFrameReader.json(RDD) in favor of DataFrameReader.json(Dataset); the former was deprecated in 2.2.0, but, it is still needed to support Pyspark's .json() method, which can't use a Dataset.
- Looks like SQLContext.createExternalTable was not actually deprecated in Pyspark, but, almost certainly was meant to be? Catalog.createExternalTable was.
- I afterwards noted that the toDegrees, toRadians functions were almost removed fully in SPARK-25908, but Felix suggested keeping just the R version as they hadn't been technically deprecated. I'd like to revisit that. Do we really want the inconsistency? I'm not against reverting it again, but then that implies leaving SQLContext.createExternalTable just in Pyspark too, which seems weird.
- I *kept* LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD, RidgeRegressionWithSGD in Pyspark, though deprecated, as it is hard to remove them (still used by StreamingLogisticRegressionWithSGD?) and they are not fully removed in Scala. Maybe should not have been deprecated.
### Why are the changes needed?
Deprecated items are easiest to remove in a major release, so we should do so as much as possible for Spark 3. This does not target items deprecated 'recently' as of Spark 2.3, which is still 18 months old.
### Does this PR introduce any user-facing change?
Yes, in that deprecated items are removed from some public APIs.
### How was this patch tested?
Existing tests.
Closes#25684 from srowen/SPARK-28980.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
The PR proposes to create a custom `RDD` which enables to propagate `SQLConf` also in cases not tracked by SQL execution, as it happens when a `Dataset` is converted to and RDD either using `.rdd` or `.queryExecution.toRdd` and then the returned RDD is used to invoke actions on it.
In this way, SQL configs are effective also in these cases, while earlier they were ignored.
### Why are the changes needed?
Without this patch, all the times `.rdd` or `.queryExecution.toRdd` are used, all the SQL configs set are ignored. An example of a reproducer can be:
```
withSQLConf(SQLConf.SUBEXPRESSION_ELIMINATION_ENABLED.key, "false") {
val df = spark.range(2).selectExpr((0 to 5000).map(i => s"id as field_$i"): _*)
df.createOrReplaceTempView("spark64kb")
val data = spark.sql("select * from spark64kb limit 10")
// Subexpression elimination is used here, despite it should have been disabled
data.describe()
}
```
### Does this PR introduce any user-facing change?
When a user calls `.queryExecution.toRdd`, a `SQLExecutionRDD` is returned wrapping the `RDD` of the execute. When `.rdd` is used, an additional `SQLExecutionRDD` is present in the hierarchy.
### How was this patch tested?
added UT
Closes#25643 from mgaido91/SPARK-28939.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The `V2SessionCatalog` has 2 functionalities:
1. work as an adapter: provide v2 APIs and translate calls to the `SessionCatalog`.
2. allow users to extend it, so that they can add hooks to apply custom logic before calling methods of the builtin catalog (session catalog).
To leverage the second functionality, users must extend `V2SessionCatalog` which is an internal class. There is no doc to explain this usage.
This PR does 2 things:
1. refine the document of the config `spark.sql.catalog.session`.
2. add a public abstract class `CatalogExtension` for users to write implementations.
TODOs for followup PRs:
1. discuss if we should allow users to completely overwrite the v2 session catalog with a new one.
2. discuss to change the name of session catalog, so that it's less likely to conflict with existing namespace names.
## How was this patch tested?
existing tests
Closes#25104 from cloud-fan/session-catalog.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When we set spark.sql.decimalOperations.allowPrecisionLoss to false.
For the sql below, the result will overflow and return null.
Case a:
`select case when 1=2 then 1 else 1.000000000000000000000001 end * 1`
Similar with the division operation.
This sql below will lost precision.
Case b:
`select case when 1=2 then 1 else 1.000000000000000000000001 end / 1`
Let us check the code of TypeCoercion.scala.
a75467432e/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala (L864-L875).
For binaryOperator, if the two operands have differnt datatype, rule ImplicitTypeCasts will find a common type and cast both operands to common type.
So, for these cases menthioned, their left operand is Decimal(34, 24) and right operand is Literal.
Their common type is Decimal(34,24), and Literal(1) will be casted to Decimal(34,24).
Then both operands are decimal type and they will be processed by decimalAndDecimal method of DecimalPrecision class.
Let's check the relative code.
a75467432e/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecision.scala (L123-L153)
When we don't allow precision loss, the result type of multiply operation in case a is Decimal(38, 38), and that of division operation in case b is Decimal(38, 20).
Then the multi operation in case a will overflow and division operation in case b will lost precision.
In this PR, we skip to handle the binaryOperator if DecimalType operands are involved and rule `DecimalPrecision` will handle it.
### Why are the changes needed?
Data will corrupt without this change.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#25701 from turboFei/SPARK-29000.
Authored-by: turbofei <fwang12@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr cleans up string template formats for generated code in HashAggregateExec. This changes comes from rednaxelafx comment: https://github.com/apache/spark/pull/20965#discussion_r316418729
### Why are the changes needed?
To improve code-readability.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#25714 from maropu/SPARK-21870-FOLLOWUP.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR aims to avoid AQE regressions by avoiding changing a sort merge join to a broadcast hash join when the expected build plan has a high ratio of empty partitions, in which case sort merge join can actually perform faster. This PR achieves this by adding an internal join hint in order to let the planner know which side has this high ratio of empty partitions and it should avoid planning it as a build plan of a BHJ. Still, it won't affect the other side if the other side qualifies for a build plan of a BHJ.
### Why are the changes needed?
It is a performance improvement for AQE.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT.
Closes#25703 from maryannxue/aqe-demote-bhj.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In the PR, I propose new function `date_part()`. The function is modeled on the traditional Ingres equivalent to the SQL-standard function `extract`:
```
date_part('field', source)
```
and added for feature parity with PostgreSQL (https://www.postgresql.org/docs/11/functions-datetime.html#FUNCTIONS-DATETIME-EXTRACT).
The `source` can have `DATE` or `TIMESTAMP` type. Supported string values of `'field'` are:
- `millennium` - the current millennium for given date (or a timestamp implicitly casted to a date). For example, years in the 1900s are in the second millennium. The third millennium started _January 1, 2001_.
- `century` - the current millennium for given date (or timestamp). The first century starts at 0001-01-01 AD.
- `decade` - the current decade for given date (or timestamp). Actually, this is the year field divided by 10.
- isoyear` - the ISO 8601 week-numbering year that the date falls in. Each ISO 8601 week-numbering year begins with the Monday of the week containing the 4th of January.
- `year`, `month`, `day`, `hour`, `minute`, `second`
- `week` - the number of the ISO 8601 week-numbering week of the year. By definition, ISO weeks start on Mondays and the first week of a year contains January 4 of that year.
- `quarter` - the quarter of the year (1 - 4)
- `dayofweek` - the day of the week for date/timestamp (1 = Sunday, 2 = Monday, ..., 7 = Saturday)
- `dow` - the day of the week as Sunday (0) to Saturday (6)
- `isodow` - the day of the week as Monday (1) to Sunday (7)
- `doy` - the day of the year (1 - 365/366)
- `milliseconds` - the seconds field including fractional parts multiplied by 1,000.
- `microseconds` - the seconds field including fractional parts multiplied by 1,000,000.
- `epoch` - the number of seconds since 1970-01-01 00:00:00 local time in microsecond precision.
Here are examples:
```sql
spark-sql> select date_part('year', timestamp'2019-08-12 01:00:00.123456');
2019
spark-sql> select date_part('week', timestamp'2019-08-12 01:00:00.123456');
33
spark-sql> select date_part('doy', timestamp'2019-08-12 01:00:00.123456');
224
```
I changed implementation of `extract` to re-use `date_part()` internally.
## How was this patch tested?
Added `date_part.sql` and regenerated results of `extract.sql`.
Closes#25410 from MaxGekk/date_part.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Add a listListeners() method to StreamingQueryManager that lists all StreamingQueryListeners that have been added to that manager.
### Why are the changes needed?
While it's best practice to keep handles on all listeners added, it's still nice to have an API to be able to list what listeners have been added to a StreamingQueryManager.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Modified existing unit tests to use the new API instead of using reflection.
Closes#25518 from mukulmurthy/26046-listener.
Authored-by: Mukul Murthy <mukul.murthy@gmail.com>
Signed-off-by: Jose Torres <torres.joseph.f+github@gmail.com>
### What changes were proposed in this pull request?
merge the `V2WriteSupportCheck` and `V2StreamingScanSupportCheck` to one rule: `TableCapabilityCheck`.
### Why are the changes needed?
It's a little confusing to have 2 rules to check DS v2 table capability, while one rule says it checks write and another rule says it checks streaming scan. We can clearly tell it from the rule names that the batch scan check is missing.
It's better to have a centralized place for this check, with a name that clearly says it checks table capability.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#25679 from cloud-fan/dsv2-check.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to port `pgSQL/aggregates_part3.sql` into UDF test base.
<details><summary>Diff comparing to 'pgSQL/aggregates_part3.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/pgSQL/aggregates_part3.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-aggregates_part3.sql.out
index f102383cb4d..eff33f280cf 100644
--- a/sql/core/src/test/resources/sql-tests/results/pgSQL/aggregates_part3.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-aggregates_part3.sql.out
-3,7 +3,7
-- !query 0
-select max(min(unique1)) from tenk1
+select udf(max(min(unique1))) from tenk1
-- !query 0 schema
struct<>
-- !query 0 output
-12,11 +12,11 It is not allowed to use an aggregate function in the argument of another aggreg
-- !query 1
-select (select count(*)
- from (values (1)) t0(inner_c))
+select udf((select udf(count(*))
+ from (values (1)) t0(inner_c))) as col
from (values (2),(3)) t1(outer_c)
-- !query 1 schema
-struct<scalarsubquery():bigint>
+struct<col:bigint>
-- !query 1 output
1
1
```
</p>
</details>
### Why are the changes needed?
To improve test coverage in UDFs.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually tested via:
```bash
build/sbt "sql/test-only *SQLQueryTestSuite -- -z udf/pgSQL/udf-aggregates_part3.sql"
```
as guided in https://issues.apache.org/jira/browse/SPARK-27921Closes#25676 from HyukjinKwon/SPARK-28272.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to port `pgSQL/aggregates_part4.sql` into UDF test base.
<details><summary>Diff comparing to 'pgSQL/aggregates_part3.sql'</summary>
<p>
```diff
```
</p>
</details>
### Why are the changes needed?
To improve test coverage in UDFs.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually tested via:
```bash
build/sbt "sql/test-only *SQLQueryTestSuite -- -z udf/pgSQL/udf-aggregates_part4.sql"
```
as guided in https://issues.apache.org/jira/browse/SPARK-27921Closes#25677 from HyukjinKwon/SPARK-28971.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
`DataFrameReader.json()` accepts a partition column that is of numeric, date or timestamp type, according to the implementation in `JDBCRelation.scala`. Update the scaladoc accordingly, to match the documentation in `sql-data-sources-jdbc.md` too.
### Why are the changes needed?
scaladoc is incorrect.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#25687 from srowen/SPARK-28977.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Support generator in aggregate expressions.
In this PR, I check the aggregate logical plan, if its aggregateExpressions include generator, then convert this agg plan into "normal agg plan + generator plan + projection plan". I.e:
```
aggregate(with generator)
|--child_plan
```
===>
```
project
|--generator(resolved)
|--aggregate
|--child_plan
```
### Why are the changes needed?
We should support sql like:
```
select explode(array(min(a), max(a))) from t
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Unit test added.
Closes#25512 from WeichenXu123/explode_bug.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Remove unnecessary physical projection added to ensure rows are `UnsafeRow` when the DSv2 scan is columnar. This is not needed because conversions are automatically added to convert from columnar operators to `UnsafeRow` when the next operator does not support columnar execution.
### Why are the changes needed?
Removes an extra projection and copy.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#25586 from rdblue/SPARK-28878-remove-dsv2-project-with-columnar.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Adds the provider information to the table properties in saveAsTable.
### Why are the changes needed?
Otherwise, catalog implementations don't know what kind of Table definition to create.
### Does this PR introduce any user-facing change?
nope
### How was this patch tested?
Existing unit tests check the existence of the provider now.
Closes#25669 from brkyvz/provider.
Authored-by: Burak Yavuz <brkyvz@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Rename `UnresolvedTable` to `V1Table` because it is not unresolved.
### Why are the changes needed?
The class name is inaccurate. This should be fixed before it is in a release.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#25683 from rdblue/SPARK-28979-rename-unresolved-table.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Replaces some incorrect usage of `new Configuration()` as it will load default configs defined in Hadoop
### Why are the changes needed?
Unexpected config could be accessed instead of the expected config, see SPARK-28203 for example
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existed tests.
Closes#25616 from advancedxy/remove_invalid_configuration.
Authored-by: Xianjin YE <advancedxy@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This patch implements dynamic partition pruning by adding a dynamic-partition-pruning filter if there is a partitioned table and a filter on the dimension table. The filter is then planned using a heuristic approach:
1. As a broadcast relation if it is a broadcast hash join. The broadcast relation will then be transformed into a reused broadcast exchange by the `ReuseExchange` rule; or
2. As a subquery duplicate if the estimated benefit of partition table scan being saved is greater than the estimated cost of the extra scan of the duplicated subquery; otherwise
3. As a bypassed condition (`true`).
### Why are the changes needed?
This is an important performance feature.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT
- Testing DPP by enabling / disabling the reuse broadcast results feature and / or the subquery duplication feature.
- Testing DPP with reused broadcast results.
- Testing the key iterators on different HashedRelation types.
- Testing the packing and unpacking of the broadcast keys in a LongType.
Closes#25600 from maryannxue/dpp.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This adds namespace support to V2SessionCatalog.
## How was this patch tested?
WIP: will add tests for v2 session catalog namespace methods.
Closes#25363 from rdblue/SPARK-28628-support-namespaces-in-v2-session-catalog.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
## What changes were proposed in this pull request?
Datasource table now supports partition tables long ago. This commit adds the ability to translate
the InsertIntoTable(HiveTableRelation) to datasource table insertion.
## How was this patch tested?
Existing tests with some modification
Closes#25306 from advancedxy/SPARK-28573.
Authored-by: Xianjin YE <advancedxy@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The Experimental and Evolving annotations are both (like Unstable) used to express that a an API may change. However there are many things in the code that have been marked that way since even Spark 1.x. Per the dev thread, anything introduced at or before Spark 2.3.0 is pretty much 'stable' in that it would not change without a deprecation cycle. Therefore I'd like to remove most of these annotations. And, remove the `:: Experimental ::` scaladoc tag too. And likewise for Python, R.
The changes below can be summarized as:
- Generally, anything introduced at or before Spark 2.3.0 has been unmarked as neither Evolving nor Experimental
- Obviously experimental items like DSv2, Barrier mode, ExperimentalMethods are untouched
- I _did_ unmark a few MLlib classes introduced in 2.4, as I am quite confident they're not going to change (e.g. KolmogorovSmirnovTest, PowerIterationClustering)
It's a big change to review, so I'd suggest scanning the list of _files_ changed to see if any area seems like it should remain partly experimental and examine those.
### Why are the changes needed?
Many of these annotations are incorrect; the APIs are de facto stable. Leaving them also makes legitimate usages of the annotations less meaningful.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#25558 from srowen/SPARK-28855.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This adds a new write API as proposed in the [SPIP to standardize logical plans](https://issues.apache.org/jira/browse/SPARK-23521). This new API:
* Uses clear verbs to execute writes, like `append`, `overwrite`, `create`, and `replace` that correspond to the new logical plans.
* Only creates v2 logical plans so the behavior is always consistent.
* Does not allow table configuration options for operations that cannot change table configuration. For example, `partitionedBy` can only be called when the writer executes `create` or `replace`.
Here are a few example uses of the new API:
```scala
df.writeTo("catalog.db.table").append()
df.writeTo("catalog.db.table").overwrite($"date" === "2019-06-01")
df.writeTo("catalog.db.table").overwritePartitions()
df.writeTo("catalog.db.table").asParquet.create()
df.writeTo("catalog.db.table").partitionedBy(days($"ts")).createOrReplace()
df.writeTo("catalog.db.table").using("abc").replace()
```
## How was this patch tested?
Added `DataFrameWriterV2Suite` that tests the new write API. Existing tests for v2 plans.
Closes#25354 from rdblue/SPARK-28612-add-data-frame-writer-v2.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
### What changes were proposed in this pull request?
See https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/109834/testReport/junit/org.apache.spark.sql/SQLQueryTestSuite/
```xml
<?xml version="1.0" encoding="UTF-8"?>
<testsuite hostname="C02Y52ZLJGH5" name="org.apache.spark.sql.SQLQueryTestSuite" tests="3" errors="0" failures="0" skipped="0" time="14.475">
...
<testcase classname="org.apache.spark.sql.SQLQueryTestSuite" name="sql - Scala UDF" time="6.703">
</testcase>
<testcase classname="org.apache.spark.sql.SQLQueryTestSuite" name="sql - Regular Python UDF" time="4.442">
</testcase>
<testcase classname="org.apache.spark.sql.SQLQueryTestSuite" name="sql - Scalar Pandas UDF" time="3.33">
</testcase>
<system-out/>
<system-err/>
</testsuite>
```
Root cause seems a bug in SBT - it truncates the test name based on the last dot.
https://github.com/sbt/sbt/issues/2949https://github.com/sbt/sbt/blob/v0.13.18/testing/src/main/scala/sbt/JUnitXmlTestsListener.scala#L71-L79
I tried to find a better way but couldn't find. Therefore, this PR proposes a workaround by appending the test file name into the assert log:
```diff
[info] - inner-join.sql *** FAILED *** (4 seconds, 306 milliseconds)
+ [info] inner-join.sql
[info] Expected "1 a
[info] 1 a
[info] 1 b
[info] 1[]", but got "1 a
[info] 1 a
[info] 1 b
[info] 1[ b]" Result did not match for query #6
[info] SELECT tb.* FROM ta INNER JOIN tb ON ta.a = tb.a AND ta.tag = tb.tag (SQLQueryTestSuite.scala:377)
[info] org.scalatest.exceptions.TestFailedException:
[info] at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:528)
```
It will at least prevent us to search full logs to identify which test file is failed by clicking filed test.
Note that this PR does not fully fix the issue but only fix the logs on its failed tests.
### Why are the changes needed?
To debug Jenkins logs easier. Otherwise, we should open full logs and search which test was failed.
### Does this PR introduce any user-facing change?
It will print out the file name of failed tests in Jenkins' test reports.
### How was this patch tested?
Manually tested but Jenkins tests are required in this PR.
Now it at least shows which file it is:
Closes#25630 from HyukjinKwon/SPARK-28894-1.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR aims to add "true", "yes", "1", "false", "no", "0", and unique prefixes as input for the boolean data type and ignore input whitespace. Please see the following what string representations are using for the boolean type in other databases.
https://www.postgresql.org/docs/devel/datatype-boolean.htmlhttps://docs.aws.amazon.com/redshift/latest/dg/r_Boolean_type.html
## How was this patch tested?
Added new tests to CastSuite.
Closes#25458 from younggyuchun/SPARK-27931.
Authored-by: younggyu chun <younggyuchun@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Adds support for the V2SessionCatalog for ALTER TABLE statements.
Implementation changes are ~50 loc. The rest is just test refactoring.
### Why are the changes needed?
To allow V2 DataSources to plug in through a configurable plugin interface without requiring the explicit use of catalog identifiers, and leverage ALTER TABLE statements.
### How was this patch tested?
By re-using existing tests in DataSourceV2SQLSuite.
Closes#25502 from brkyvz/alterV3.
Authored-by: Burak Yavuz <brkyvz@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
There are 2 in-memory `TableCatalog` and `Table` implementations for testing, in sql/catalyst and sql/core. This PR merges them.
After merging, there are 3 classes:
1. `InMemoryTable`
2. `InMemoryTableCatalog`
3. `StagingInMemoryTableCatalog`
For better maintainability, these 3 classes are put in 3 different files.
### Why are the changes needed?
reduce duplicated code
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
N/A
Closes#25610 from cloud-fan/dsv2-test.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Ryan Blue <blue@apache.org>
### What changes were proposed in this pull request?
This PR replaces the hard-coded non-nullability of the array elements returned by `freqItems()` with a nullability that reflects the original schema. Essentially [the functional change](https://github.com/apache/spark/pull/25575/files#diff-bf59bb9f3dc351f5bf6624e5edd2dcf4R122) to the schema generation is:
```
StructField(name + "_freqItems", ArrayType(dataType, false))
```
Becomes:
```
StructField(name + "_freqItems", ArrayType(dataType, originalField.nullable))
```
Respecting the original nullability prevents issues when Spark depends on `ArrayType`'s `containsNull` being accurate. The example that uncovered this is calling `collect()` on the dataframe (see [ticket](https://issues.apache.org/jira/browse/SPARK-28818) for full repro). Though it's likely that there a several places where this could cause a problem.
I've also refactored a small amount of the surrounding code to remove some unnecessary steps and group together related operations.
### Why are the changes needed?
I think it's pretty clear why this change is needed. It fixes a bug that currently prevents users from calling `df.freqItems.collect()` along with potentially causing other, as yet unknown, issues.
### Does this PR introduce any user-facing change?
Nullability of columns when calling freqItems on them is now respected after the change.
### How was this patch tested?
I added a test that specifically tests the carry-through of the nullability as well as explicitly calling `collect()` to catch the exact regression that was observed. I also ran the test against the old version of the code and it fails as expected.
Closes#25575 from MGHawes/mhawes/SPARK-28818.
Authored-by: Matt Hawes <mhawes@palantir.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
**What changes were proposed in this pull request?**
Moving the call for checkColumnNameDuplication out of generateViewProperties. This way we can choose ifcheckColumnNameDuplication will be performed on analyzed or aliased plan without having to pass an additional argument(aliasedPlan) to generateViewProperties.
Before the pr column name duplication was performed on the query output of below sql(c1, c1) and the pr makes it perform check on the user provided schema of view definition(c1, c2)
**Why are the changes needed?**
Changes are to fix SPARK-23519 bug. Below queries would cause an exception. This pr fixes them and also added a test case.
`CREATE TABLE t23519 AS SELECT 1 AS c1
CREATE VIEW v23519 (c1, c2) AS SELECT c1, c1 FROM t23519`
**Does this PR introduce any user-facing change?**
No
**How was this patch tested?**
new unit test added in SQLViewSuite
Closes#25570 from hem1891/SPARK-23519.
Lead-authored-by: hemanth meka <hmeka@tibco.com>
Co-authored-by: hem1891 <hem1891@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This is caused by 2 PRs that were merged at the same time:
cb06209fc92b24a71fecCloses#25597 from cloud-fan/hot-fix.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Introduce ANSI store assignment policy for table insertion.
With ANSI policy, Spark performs the type coercion of table insertion as per ANSI SQL.
### Why are the changes needed?
In Spark version 2.4 and earlier, when inserting into a table, Spark will cast the data type of input query to the data type of target table by coercion. This can be super confusing, e.g. users make a mistake and write string values to an int column.
In data source V2, by default, only upcasting is allowed when inserting data into a table. E.g. int -> long and int -> string are allowed, while decimal -> double or long -> int are not allowed. The rules of UpCast was originally created for Dataset type coercion. They are quite strict and different from the behavior of all existing popular DBMS. This is breaking change. It is possible that existing queries are broken after 3.0 releases.
Following ANSI SQL standard makes Spark consistent with the table insertion behaviors of popular DBMS like PostgreSQL/Oracle/Mysql.
### Does this PR introduce any user-facing change?
A new optional mode for table insertion.
### How was this patch tested?
Unit test
Closes#25581 from gengliangwang/ANSImode.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Make `spark.sql.crossJoin.enabled` default value true
### Why are the changes needed?
For implicit cross join, we can set up a watchdog to cancel it if running for a long time.
When "spark.sql.crossJoin.enabled" is false, because `CheckCartesianProducts` is implemented in logical plan stage, it may generate some mismatching error which may confuse end user:
* it's done in logical phase, so we may fail queries that can be executed via broadcast join, which is very fast.
* if we move the check to the physical phase, then a query may success at the beginning, and begin to fail when the table size gets larger (other people insert data to the table). This can be quite confusing.
* the CROSS JOIN syntax doesn't work well if join reorder happens.
* some non-equi-join will generate plan using cartesian product, but `CheckCartesianProducts` do not detect it and raise error.
So that in order to address this in simpler way, we can turn off showing this cross-join error by default.
For reference, I list some cases raising mismatching error here:
Providing:
```
spark.range(2).createOrReplaceTempView("sm1") // can be broadcast
spark.range(50000000).createOrReplaceTempView("bg1") // cannot be broadcast
spark.range(60000000).createOrReplaceTempView("bg2") // cannot be broadcast
```
1) Some join could be convert to broadcast nested loop join, but CheckCartesianProducts raise error. e.g.
```
select sm1.id, bg1.id from bg1 join sm1 where sm1.id < bg1.id
```
2) Some join will run by CartesianJoin but CheckCartesianProducts DO NOT raise error. e.g.
```
select bg1.id, bg2.id from bg1 join bg2 where bg1.id < bg2.id
```
### Does this PR introduce any user-facing change?
### How was this patch tested?
Closes#25520 from WeichenXu123/SPARK-28621.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently we have 2 configs to specify which v2 sources should fallback to v1 code path. One config for read path, and one config for write path.
However, I found it's awkward to work with these 2 configs:
1. for `CREATE TABLE USING format`, should this be read path or write path?
2. for `V2SessionCatalog.loadTable`, we need to return `UnresolvedTable` if it's a DS v1 or we need to fallback to v1 code path. However, at that time, we don't know if the returned table will be used for read or write.
We don't have any new features or perf improvement in file source v2. The fallback API is just a safeguard if we have bugs in v2 implementations. There are not many benefits to support falling back to v1 for read and write path separately.
This PR proposes to merge these 2 configs into one.
## How was this patch tested?
existing tests
Closes#25465 from cloud-fan/merge-conf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds support for INSERT INTO through both the SQL and DataFrameWriter APIs through the V2SessionCatalog.
### Why are the changes needed?
This will allow V2 tables to be plugged in through the V2SessionCatalog, and be used seamlessly with existing APIs.
### Does this PR introduce any user-facing change?
No behavior changes.
### How was this patch tested?
Pulled out a lot of tests so that they can be shared across the DataFrameWriter and SQL code paths.
Closes#25507 from brkyvz/insertSesh.
Lead-authored-by: Burak Yavuz <brkyvz@gmail.com>
Co-authored-by: Burak Yavuz <burak@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR aims at improving the way physical plans are explained in spark.
Currently, the explain output for physical plan may look very cluttered and each operator's
string representation can be very wide and wraps around in the display making it little
hard to follow. This especially happens when explaining a query 1) Operating on wide tables
2) Has complex expressions etc.
This PR attempts to split the output into two sections. In the header section, we display
the basic operator tree with a number associated with each operator. In this section, we strictly
control what we output for each operator. In the footer section, each operator is verbosely
displayed. Based on the feedback from Maryann, the uncorrelated subqueries (SubqueryExecs) are not included in the main plan. They are printed separately after the main plan and can be
correlated by the originating expression id from its parent plan.
To illustrate, here is a simple plan displayed in old vs new way.
Example query1 :
```
EXPLAIN SELECT key, Max(val) FROM explain_temp1 WHERE key > 0 GROUP BY key HAVING max(val) > 0
```
Old :
```
*(2) Project [key#2, max(val)#15]
+- *(2) Filter (isnotnull(max(val#3)#18) AND (max(val#3)#18 > 0))
+- *(2) HashAggregate(keys=[key#2], functions=[max(val#3)], output=[key#2, max(val)#15, max(val#3)#18])
+- Exchange hashpartitioning(key#2, 200)
+- *(1) HashAggregate(keys=[key#2], functions=[partial_max(val#3)], output=[key#2, max#21])
+- *(1) Project [key#2, val#3]
+- *(1) Filter (isnotnull(key#2) AND (key#2 > 0))
+- *(1) FileScan parquet default.explain_temp1[key#2,val#3] Batched: true, DataFilters: [isnotnull(key#2), (key#2 > 0)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp1], PartitionFilters: [], PushedFilters: [IsNotNull(key), GreaterThan(key,0)], ReadSchema: struct<key:int,val:int>
```
New :
```
Project (8)
+- Filter (7)
+- HashAggregate (6)
+- Exchange (5)
+- HashAggregate (4)
+- Project (3)
+- Filter (2)
+- Scan parquet default.explain_temp1 (1)
(1) Scan parquet default.explain_temp1 [codegen id : 1]
Output: [key#2, val#3]
(2) Filter [codegen id : 1]
Input : [key#2, val#3]
Condition : (isnotnull(key#2) AND (key#2 > 0))
(3) Project [codegen id : 1]
Output : [key#2, val#3]
Input : [key#2, val#3]
(4) HashAggregate [codegen id : 1]
Input: [key#2, val#3]
(5) Exchange
Input: [key#2, max#11]
(6) HashAggregate [codegen id : 2]
Input: [key#2, max#11]
(7) Filter [codegen id : 2]
Input : [key#2, max(val)#5, max(val#3)#8]
Condition : (isnotnull(max(val#3)#8) AND (max(val#3)#8 > 0))
(8) Project [codegen id : 2]
Output : [key#2, max(val)#5]
Input : [key#2, max(val)#5, max(val#3)#8]
```
Example Query2 (subquery):
```
SELECT * FROM explain_temp1 WHERE KEY = (SELECT Max(KEY) FROM explain_temp2 WHERE KEY = (SELECT Max(KEY) FROM explain_temp3 WHERE val > 0) AND val = 2) AND val > 3
```
Old:
```
*(1) Project [key#2, val#3]
+- *(1) Filter (((isnotnull(KEY#2) AND isnotnull(val#3)) AND (KEY#2 = Subquery scalar-subquery#39)) AND (val#3 > 3))
: +- Subquery scalar-subquery#39
: +- *(2) HashAggregate(keys=[], functions=[max(KEY#26)], output=[max(KEY)#45])
: +- Exchange SinglePartition
: +- *(1) HashAggregate(keys=[], functions=[partial_max(KEY#26)], output=[max#47])
: +- *(1) Project [key#26]
: +- *(1) Filter (((isnotnull(KEY#26) AND isnotnull(val#27)) AND (KEY#26 = Subquery scalar-subquery#38)) AND (val#27 = 2))
: : +- Subquery scalar-subquery#38
: : +- *(2) HashAggregate(keys=[], functions=[max(KEY#28)], output=[max(KEY)#43])
: : +- Exchange SinglePartition
: : +- *(1) HashAggregate(keys=[], functions=[partial_max(KEY#28)], output=[max#49])
: : +- *(1) Project [key#28]
: : +- *(1) Filter (isnotnull(val#29) AND (val#29 > 0))
: : +- *(1) FileScan parquet default.explain_temp3[key#28,val#29] Batched: true, DataFilters: [isnotnull(val#29), (val#29 > 0)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp3], PartitionFilters: [], PushedFilters: [IsNotNull(val), GreaterThan(val,0)], ReadSchema: struct<key:int,val:int>
: +- *(1) FileScan parquet default.explain_temp2[key#26,val#27] Batched: true, DataFilters: [isnotnull(key#26), isnotnull(val#27), (val#27 = 2)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp2], PartitionFilters: [], PushedFilters: [IsNotNull(key), IsNotNull(val), EqualTo(val,2)], ReadSchema: struct<key:int,val:int>
+- *(1) FileScan parquet default.explain_temp1[key#2,val#3] Batched: true, DataFilters: [isnotnull(key#2), isnotnull(val#3), (val#3 > 3)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp1], PartitionFilters: [], PushedFilters: [IsNotNull(key), IsNotNull(val), GreaterThan(val,3)], ReadSchema: struct<key:int,val:int>
```
New:
```
Project (3)
+- Filter (2)
+- Scan parquet default.explain_temp1 (1)
(1) Scan parquet default.explain_temp1 [codegen id : 1]
Output: [key#2, val#3]
(2) Filter [codegen id : 1]
Input : [key#2, val#3]
Condition : (((isnotnull(KEY#2) AND isnotnull(val#3)) AND (KEY#2 = Subquery scalar-subquery#23)) AND (val#3 > 3))
(3) Project [codegen id : 1]
Output : [key#2, val#3]
Input : [key#2, val#3]
===== Subqueries =====
Subquery:1 Hosting operator id = 2 Hosting Expression = Subquery scalar-subquery#23
HashAggregate (9)
+- Exchange (8)
+- HashAggregate (7)
+- Project (6)
+- Filter (5)
+- Scan parquet default.explain_temp2 (4)
(4) Scan parquet default.explain_temp2 [codegen id : 1]
Output: [key#26, val#27]
(5) Filter [codegen id : 1]
Input : [key#26, val#27]
Condition : (((isnotnull(KEY#26) AND isnotnull(val#27)) AND (KEY#26 = Subquery scalar-subquery#22)) AND (val#27 = 2))
(6) Project [codegen id : 1]
Output : [key#26]
Input : [key#26, val#27]
(7) HashAggregate [codegen id : 1]
Input: [key#26]
(8) Exchange
Input: [max#35]
(9) HashAggregate [codegen id : 2]
Input: [max#35]
Subquery:2 Hosting operator id = 5 Hosting Expression = Subquery scalar-subquery#22
HashAggregate (15)
+- Exchange (14)
+- HashAggregate (13)
+- Project (12)
+- Filter (11)
+- Scan parquet default.explain_temp3 (10)
(10) Scan parquet default.explain_temp3 [codegen id : 1]
Output: [key#28, val#29]
(11) Filter [codegen id : 1]
Input : [key#28, val#29]
Condition : (isnotnull(val#29) AND (val#29 > 0))
(12) Project [codegen id : 1]
Output : [key#28]
Input : [key#28, val#29]
(13) HashAggregate [codegen id : 1]
Input: [key#28]
(14) Exchange
Input: [max#37]
(15) HashAggregate [codegen id : 2]
Input: [max#37]
```
Note:
I opened this PR as a WIP to start getting feedback. I will be on vacation starting tomorrow
would not be able to immediately incorporate the feedback. I will start to
work on them as soon as i can. Also, currently this PR provides a basic infrastructure
for explain enhancement. The details about individual operators will be implemented
in follow-up prs
## How was this patch tested?
Added a new test `explain.sql` that tests basic scenarios. Need to add more tests.
Closes#24759 from dilipbiswal/explain_feature.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Test `spark.sql.redaction.options.regex` with and without default values.
### Why are the changes needed?
Normally, we do not rely on the default value of `spark.sql.redaction.options.regex`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#25579 from wangyum/SPARK-28642-f1.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
1. Fix the physical plan (`DescribeTableExec`) to have the same output attributes as the corresponding logical plan.
2. Remove `output` in statements since they are unresolved plans.
### Why are the changes needed?
Correctness of how output attributes should work.
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
Existing tests
Closes#25568 from imback82/describe_table.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This reverts commit 485ae6d181.
Closes#25563 from gatorsmile/revert.
Authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Add id to Exchange and Subquery's stringArgs method for easier identifying their reuses in query plans, for example:
```
ReusedExchange d_date_sk#827, BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint))) [id=#2710]
```
Where `2710` is the id of the reused exchange.
## How was this patch tested?
Passes existing tests
Closes#25434 from dbaliafroozeh/ImplementStringArgsExchangeSubqueryExec.
Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
This PR removes the `canonicalize(attrs: AttributeSeq)` from `PlanExpression` and taking care of normalizing expressions in `QueryPlan`.
### Why are the changes needed?
`Expression` has already a `canonicalized` method and having the `canonicalize` method in `PlanExpression` is confusing.
### Does this PR introduce any user-facing change?
Removes the `canonicalize` plan from `PlanExpression`. Also renames the `normalizeExprId` to `normalizeExpressions` in query plan.
### How was this patch tested?
This PR is a refactoring and passes the existing tests
Closes#25534 from dbaliafroozeh/ImproveCanonicalizeAPI.
Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com>
Signed-off-by: herman <herman@databricks.com>
## What changes were proposed in this pull request?
Implements the SHOW TABLES logical and physical plans for data source v2 tables.
## How was this patch tested?
Added unit tests to `DataSourceV2SQLSuite`.
Closes#25247 from imback82/dsv2_show_tables.
Lead-authored-by: terryk <yuminkim@gmail.com>
Co-authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR extracts the schema information of TPCDS tables into a separate class called `TPCDSSchema` which can be reused for other testing purposes
### How was this patch tested?
This PR is only a refactoring for tests and passes existing tests
Closes#25535 from dbaliafroozeh/IntroduceTPCDSSchema.
Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR fixes the leak of crc files from CheckpointFileManager when FileContextBasedCheckpointFileManager is being used.
Spark hits the Hadoop bug, [HADOOP-16255](https://issues.apache.org/jira/browse/HADOOP-16255) which seems to be a long-standing issue.
This is there're two `renameInternal` methods:
```
public void renameInternal(Path src, Path dst)
public void renameInternal(final Path src, final Path dst, boolean overwrite)
```
which should be overridden to handle all cases but ChecksumFs only overrides method with 2 params, so when latter is called FilterFs.renameInternal(...) is called instead, and it will do rename with RawLocalFs as underlying filesystem.
The bug is related to FileContext, so FileSystemBasedCheckpointFileManager is not affected.
[SPARK-17475](https://issues.apache.org/jira/browse/SPARK-17475) took a workaround for this bug, but [SPARK-23966](https://issues.apache.org/jira/browse/SPARK-23966) seemed to bring regression.
This PR deletes crc file as "best-effort" when renaming, as failing to delete crc file is not that critical to fail the task.
### Why are the changes needed?
This PR prevents crc files not being cleaned up even purging batches. Too many files in same directory often hurts performance, as well as each crc file occupies more space than its own size so possible to occupy nontrivial amount of space when batches go up to 100000+.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Some unit tests are modified to check leakage of crc files.
Closes#25488 from HeartSaVioR/SPARK-28025.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
After all the discussions in the dev list: http://apache-spark-developers-list.1001551.n3.nabble.com/Discuss-Follow-ANSI-SQL-on-table-insertion-td27531.html#a27562.
Here I propose that we can make the store assignment rules in the analyzer configurable, and the behavior of V1 and V2 should be consistent.
When inserting a value into a column with a different data type, Spark will perform type coercion. After this PR, we support 2 policies for the type coercion rules:
legacy and strict.
1. With legacy policy, Spark allows casting any value to any data type. The legacy policy is the only behavior in Spark 2.x and it is compatible with Hive.
2. With strict policy, Spark doesn't allow any possible precision loss or data truncation in type coercion, e.g. `int` and `long`, `float` -> `double` are not allowed.
Eventually, the "legacy" mode will be removed, so it is disallowed in data source V2.
To ensure backward compatibility with existing queries, the default store assignment policy for data source V1 is "legacy".
## How was this patch tested?
Unit test
Closes#25453 from gengliangwang/tableInsertRule.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Added proper message instead of NPE for invalid Dataset operations (e.g. calling actions inside of transformations) similar to SPARK-5063 for RDD
### Why are the changes needed?
To report the user about the exact issue instead of NPE
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manually tested
```scala
test code snap
"import spark.implicits._
val ds1 = spark.sparkContext.parallelize(1 to 100, 100).toDS()
val ds2 = spark.sparkContext.parallelize(1 to 100, 100).toDS()
ds1.map(x => {
// scalastyle:off
println(ds2.count + x)
x
}).collect()"
```
Closes#25503 from shivusondur/jira28702.
Authored-by: shivusondur <shivusondur@gmail.com>
Signed-off-by: Josh Rosen <rosenville@gmail.com>
### What changes were proposed in this pull request?
Fix minor typo in SQLConf.
`FILE_COMRESSION_FACTOR` -> `FILE_COMPRESSION_FACTOR`
### Why are the changes needed?
Make conf more understandable.
### Does this PR introduce any user-facing change?
No. (`spark.sql.sources.fileCompressionFactor` is unchanged.)
### How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#25538 from triplesheep/TYPO-FIX.
Authored-by: triplesheep <triplesheep0419@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR adds a simple cost model and a mechanism to compare the costs of the before and after plans of each re-optimization in Adaptive Query Execution. Now the workflow of AQE re-optimization is changed to: If the cost of the plan after re-optimization is lower than or equal to that of the plan before re-optimization and the plan has been changed after re-optimization (if equal), the current physical plan will be updated to the plan after re-optimization, otherwise it will remain unchanged until the next re-optimization.
### Why are the changes needed?
This new mechanism is to prevent regressions in Adaptive Query Execution caused by change of the plan introducing extra cost, in this PR specifically, change of SMJ to BHJ leading to extra `ShuffleExchangeExec`s.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT.
Closes#25456 from maryannxue/aqe-cost.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
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### What changes were proposed in this pull request?
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When running CTAS/RTAS, use the nullable schema of the input query to create the table.
### Why are the changes needed?
<!--
Please clarify why the changes are needed. For instance,
1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
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It's very likely to run CTAS/RTAS with non-nullable input query, e.g. `CREATE TABLE t AS SELECT 1`. However, it's surprising to users if they can't write null to this table later. Non-nullable is kind of a constraint of the column and should be specified by users explicitly.
For reference, Postgres also use nullable schema for CTAS:
```
> create table t1(i int not null);
> insert into t1 values (1);
> create table t2 as select i from t1;
> \d+ t1;
Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
--------+---------+-----------+----------+---------+---------+--------------+-------------
i | integer | | not null | | plain | |
> \d+ t2;
Column | Type | Collation | Nullable | Default | Storage | Stats target | Description
--------+---------+-----------+----------+---------+---------+--------------+-------------
i | integer | | | | plain | |
```
File source V1 has the same behavior.
### Does this PR introduce any user-facing change?
<!--
If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible.
If no, write 'No'.
-->
Yes, after this PR CTAS/RTAS creates tables with nullable schema, then users can insert null values later.
### How was this patch tested?
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new test
Closes#25536 from cloud-fan/ctas.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Disable using radix sort in ShuffleExchangeExec when we do repartition.
In #20393, we fixed the indeterministic result in the shuffle repartition case by performing a local sort before repartitioning.
But for the newly added sort operation, we use radix sort which is wrong because binary data can't be compared by only the prefix. This makes the sort unstable and fails to solve the indeterminate shuffle output problem.
### Why are the changes needed?
Fix the correctness bug caused by repartition after a shuffle.
### Does this PR introduce any user-facing change?
Yes, user will get the right result in the case of repartition stage rerun.
## How was this patch tested?
Test with `local-cluster[5, 2, 5120]`, use the integrated test below, it can return a right answer 100000000.
```
import scala.sys.process._
import org.apache.spark.TaskContext
val res = spark.range(0, 10000 * 10000, 1).map{ x => (x % 1000, x)}
// kill an executor in the stage that performs repartition(239)
val df = res.repartition(113).map{ x => (x._1 + 1, x._2)}.repartition(239).map { x =>
if (TaskContext.get.attemptNumber == 0 && TaskContext.get.partitionId < 1 && TaskContext.get.stageAttemptNumber == 0) {
throw new Exception("pkill -f -n java".!!)
}
x
}
val r2 = df.distinct.count()
```
Closes#25491 from xuanyuanking/SPARK-28699-fix.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
The rule ReuseExchange optimization rule will look for instances of Exchange that have the same plan and convert dedupe them to them to a ReuseExchangeExec instance. In the current Spark codebase all Exchange instances are row based, but if we use the spark.sql.extensions config to put in our own columnar based exchange implementation reuse will throw an exception saying that there was a columnar mismatch.
### Why are the changes needed?
Without it Reused Columnar Exchanges throw an exception
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
I tested this patch by running it against a query that was showing this exact issue and it fixed it.
I also added a very simple unit test that shows the issue.
Closes#25499 from revans2/reused-columnar-exchange.
Authored-by: Robert (Bobby) Evans <bobby@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR adds a V1 fallback interface for writing to V2 Tables using V1 Writer interfaces. The only supported SaveMode that will be called on the target table will be an Append. The target table must use V2 interfaces such as `SupportsOverwrite` or `SupportsTruncate` to support Overwrite operations. It is up to the target DataSource implementation if this operation can be atomic or not.
We do not support dynamicPartitionOverwrite, as we cannot call a `commit` method that actually cleans up the data in the partitions that were touched through this fallback.
## How was this patch tested?
Will add tests and example implementation after comments + feedback. This is a proposal at this point.
Closes#25348 from brkyvz/v1WriteFallback.
Lead-authored-by: Burak Yavuz <brkyvz@gmail.com>
Co-authored-by: Burak Yavuz <burak@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The expression `IntegralDivide`, which corresponds to the `div` operator, support only integral type. Postgres, though, allows it to work also with decimals.
The PR adds the support to decimal operands for this operation in order to have feature parity with postgres.
## How was this patch tested?
added UTs
Closes#25136 from mgaido91/SPARK-28322.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR changes subquery reuse in Adaptive Query Execution from compile-time static reuse to execution-time dynamic reuse. This PR adds a `ReuseAdaptiveSubquery` rule that applies to a query stage after it is created and before it is executed. The new dynamic reuse enables subqueries to be reused across all different subquery levels.
### Why are the changes needed?
This is an improvement to the current subquery reuse in Adaptive Query Execution, which allows subquery reuse to happen in a lazy fashion as well as at different subquery levels.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Passed existing tests.
Closes#25471 from maryannxue/aqe-dynamic-sub-reuse.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a pure refactor PR, which creates a new class `CatalogManager` to track the registered v2 catalogs, and provide the catalog up functionality.
`CatalogManager` also tracks the current catalog/namespace. We will implement corresponding commands in other PRs, like `USE CATALOG my_catalog`
## How was this patch tested?
existing tests
Closes#25368 from cloud-fan/refactor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
# What changes were proposed in this pull request?
This patch modifies the explanation of guarantee for ForeachWriter as it doesn't guarantee same output for `(partitionId, epochId)`. Refer the description of [SPARK-28650](https://issues.apache.org/jira/browse/SPARK-28650) for more details.
Spark itself still guarantees same output for same epochId (batch) if the preconditions are met, 1) source is always providing the same input records for same offset request. 2) the query is idempotent in overall (indeterministic calculation like now(), random() can break this).
Assuming breaking preconditions as an exceptional case (the preconditions are implicitly required even before), we still can describe the guarantee with `epochId`, though it will be harder to leverage the guarantee: 1) ForeachWriter should implement a feature to track whether all the partitions are written successfully for given `epochId` 2) There's pretty less chance to leverage the fact, as the chance for Spark to successfully write all partitions and fail to checkpoint the batch is small.
Credit to zsxwing on discovering the broken guarantee.
## How was this patch tested?
This is just a documentation change, both on javadoc and guide doc.
Closes#25407 from HeartSaVioR/SPARK-28650.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
Create Hive Partitioned Table without specifying data type for partition column will success unexpectedly.
```HiveQL
// create a hive table partition by b, but the data type of b isn't specified.
CREATE TABLE tbl(a int) PARTITIONED BY (b) STORED AS parquet
```
In https://issues.apache.org/jira/browse/SPARK-26435 , PARTITIONED BY clause are extended to support Hive CTAS as following:
```ANTLR
// Before
(PARTITIONED BY '(' partitionColumns=colTypeList ')'
// After
(PARTITIONED BY '(' partitionColumns=colTypeList ')'|
PARTITIONED BY partitionColumnNames=identifierList) |
```
Create Table Statement like above case will pass the syntax check, and recognized as (PARTITIONED BY partitionColumnNames=identifierList) 。
This PR will check this case in visitCreateHiveTable and throw a exception which contains explicit error message to user.
## How was this patch tested?
Added tests.
Closes#25390 from lidinghao/hive-ddl-fix.
Authored-by: lihao <lihaowhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR adds some tests converted from ```pgSQL/join.sql``` to test UDFs. Please see contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
<details><summary>Diff comparing to 'join.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/pgSQL/join.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-join.sql.out
index f75fe05196..ad2b5dd0db 100644
--- a/sql/core/src/test/resources/sql-tests/results/pgSQL/join.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-join.sql.out
-240,10 +240,10 struct<>
-- !query 27
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, udf(i), udf(j), udf(t)
FROM J1_TBL AS tx
-- !query 27 schema
-struct<xxx:string,i:int,j:int,t:string>
+struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string>
-- !query 27 output
0 NULL zero
1 4 one
-259,10 +259,10 struct<xxx:string,i:int,j:int,t:string>
-- !query 28
-SELECT '' AS `xxx`, *
+SELECT udf(udf('')) AS `xxx`, udf(udf(i)), udf(j), udf(t)
FROM J1_TBL tx
-- !query 28 schema
-struct<xxx:string,i:int,j:int,t:string>
+struct<xxx:string,CAST(udf(cast(cast(udf(cast(i as string)) as int) as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string>
-- !query 28 output
0 NULL zero
1 4 one
-278,10 +278,10 struct<xxx:string,i:int,j:int,t:string>
-- !query 29
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, a, udf(udf(b)), c
FROM J1_TBL AS t1 (a, b, c)
-- !query 29 schema
-struct<xxx:string,a:int,b:int,c:string>
+struct<xxx:string,a:int,CAST(udf(cast(cast(udf(cast(b as string)) as int) as string)) AS INT):int,c:string>
-- !query 29 output
0 NULL zero
1 4 one
-297,10 +297,10 struct<xxx:string,a:int,b:int,c:string>
-- !query 30
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, udf(a), udf(b), udf(udf(c))
FROM J1_TBL t1 (a, b, c)
-- !query 30 schema
-struct<xxx:string,a:int,b:int,c:string>
+struct<xxx:string,CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(c as string)) as string) as string)) AS STRING):string>
-- !query 30 output
0 NULL zero
1 4 one
-316,10 +316,10 struct<xxx:string,a:int,b:int,c:string>
-- !query 31
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, udf(a), b, udf(c), udf(d), e
FROM J1_TBL t1 (a, b, c), J2_TBL t2 (d, e)
-- !query 31 schema
-struct<xxx:string,a:int,b:int,c:string,d:int,e:int>
+struct<xxx:string,CAST(udf(cast(a as string)) AS INT):int,b:int,CAST(udf(cast(c as string)) AS STRING):string,CAST(udf(cast(d as string)) AS INT):int,e:int>
-- !query 31 output
0 NULL zero 0 NULL
0 NULL zero 1 -1
-423,7 +423,7 struct<xxx:string,a:int,b:int,c:string,d:int,e:int>
-- !query 32
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, *
FROM J1_TBL CROSS JOIN J2_TBL
-- !query 32 schema
struct<xxx:string,i:int,j:int,t:string,i:int,k:int>
-530,20 +530,20 struct<xxx:string,i:int,j:int,t:string,i:int,k:int>
-- !query 33
-SELECT '' AS `xxx`, i, k, t
+SELECT udf('') AS `xxx`, udf(i) AS i, udf(k), udf(t) AS t
FROM J1_TBL CROSS JOIN J2_TBL
-- !query 33 schema
struct<>
-- !query 33 output
org.apache.spark.sql.AnalysisException
-Reference 'i' is ambiguous, could be: default.j1_tbl.i, default.j2_tbl.i.; line 1 pos 20
+Reference 'i' is ambiguous, could be: default.j1_tbl.i, default.j2_tbl.i.; line 1 pos 29
-- !query 34
-SELECT '' AS `xxx`, t1.i, k, t
+SELECT udf('') AS `xxx`, udf(t1.i) AS i, udf(k), udf(t)
FROM J1_TBL t1 CROSS JOIN J2_TBL t2
-- !query 34 schema
-struct<xxx:string,i:int,k:int,t:string>
+struct<xxx:string,i:int,CAST(udf(cast(k as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string>
-- !query 34 output
0 -1 zero
0 -3 zero
-647,11 +647,11 struct<xxx:string,i:int,k:int,t:string>
-- !query 35
-SELECT '' AS `xxx`, ii, tt, kk
+SELECT udf(udf('')) AS `xxx`, udf(udf(ii)) AS ii, udf(udf(tt)) AS tt, udf(udf(kk))
FROM (J1_TBL CROSS JOIN J2_TBL)
AS tx (ii, jj, tt, ii2, kk)
-- !query 35 schema
-struct<xxx:string,ii:int,tt:string,kk:int>
+struct<xxx:string,ii:int,tt:string,CAST(udf(cast(cast(udf(cast(kk as string)) as int) as string)) AS INT):int>
-- !query 35 output
0 zero -1
0 zero -3
-755,10 +755,10 struct<xxx:string,ii:int,tt:string,kk:int>
-- !query 36
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, udf(udf(j1_tbl.i)), udf(j), udf(t), udf(a.i), udf(a.k), udf(b.i), udf(b.k)
FROM J1_TBL CROSS JOIN J2_TBL a CROSS JOIN J2_TBL b
-- !query 36 schema
-struct<xxx:string,i:int,j:int,t:string,i:int,k:int,i:int,k:int>
+struct<xxx:string,CAST(udf(cast(cast(udf(cast(i as string)) as int) as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(k as string)) AS INT):int,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(k as string)) AS INT):int>
-- !query 36 output
0 NULL zero 0 NULL 0 NULL
0 NULL zero 0 NULL 1 -1
-1654,10 +1654,10 struct<xxx:string,i:int,j:int,t:string,i:int,k:int,i:int,k:int>
-- !query 37
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, udf(i) AS i, udf(j), udf(t) AS t, udf(k)
FROM J1_TBL INNER JOIN J2_TBL USING (i)
-- !query 37 schema
-struct<xxx:string,i:int,j:int,t:string,k:int>
+struct<xxx:string,i:int,CAST(udf(cast(j as string)) AS INT):int,t:string,CAST(udf(cast(k as string)) AS INT):int>
-- !query 37 output
0 NULL zero NULL
1 4 one -1
-1669,10 +1669,10 struct<xxx:string,i:int,j:int,t:string,k:int>
-- !query 38
-SELECT '' AS `xxx`, *
+SELECT udf(udf('')) AS `xxx`, udf(i), udf(j) AS j, udf(t), udf(k) AS k
FROM J1_TBL JOIN J2_TBL USING (i)
-- !query 38 schema
-struct<xxx:string,i:int,j:int,t:string,k:int>
+struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,j:int,CAST(udf(cast(t as string)) AS STRING):string,k:int>
-- !query 38 output
0 NULL zero NULL
1 4 one -1
-1684,9 +1684,9 struct<xxx:string,i:int,j:int,t:string,k:int>
-- !query 39
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, *
FROM J1_TBL t1 (a, b, c) JOIN J2_TBL t2 (a, d) USING (a)
- ORDER BY a, d
+ ORDER BY udf(udf(a)), udf(d)
-- !query 39 schema
struct<xxx:string,a:int,b:int,c:string,d:int>
-- !query 39 output
-1700,10 +1700,10 struct<xxx:string,a:int,b:int,c:string,d:int>
-- !query 40
-SELECT '' AS `xxx`, *
+SELECT udf(udf('')) AS `xxx`, udf(i), udf(j), udf(t), udf(k)
FROM J1_TBL NATURAL JOIN J2_TBL
-- !query 40 schema
-struct<xxx:string,i:int,j:int,t:string,k:int>
+struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int>
-- !query 40 output
0 NULL zero NULL
1 4 one -1
-1715,10 +1715,10 struct<xxx:string,i:int,j:int,t:string,k:int>
-- !query 41
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, udf(udf(udf(a))) AS a, udf(b), udf(c), udf(d)
FROM J1_TBL t1 (a, b, c) NATURAL JOIN J2_TBL t2 (a, d)
-- !query 41 schema
-struct<xxx:string,a:int,b:int,c:string,d:int>
+struct<xxx:string,a:int,CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(c as string)) AS STRING):string,CAST(udf(cast(d as string)) AS INT):int>
-- !query 41 output
0 NULL zero NULL
1 4 one -1
-1730,10 +1730,10 struct<xxx:string,a:int,b:int,c:string,d:int>
-- !query 42
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, udf(udf(a)), udf(udf(b)), udf(udf(c)) AS c, udf(udf(udf(d))) AS d
FROM J1_TBL t1 (a, b, c) NATURAL JOIN J2_TBL t2 (d, a)
-- !query 42 schema
-struct<xxx:string,a:int,b:int,c:string,d:int>
+struct<xxx:string,CAST(udf(cast(cast(udf(cast(a as string)) as int) as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(b as string)) as int) as string)) AS INT):int,c:string,d:int>
-- !query 42 output
0 NULL zero NULL
2 3 two 2
-1741,10 +1741,10 struct<xxx:string,a:int,b:int,c:string,d:int>
-- !query 43
-SELECT '' AS `xxx`, *
- FROM J1_TBL JOIN J2_TBL ON (J1_TBL.i = J2_TBL.i)
+SELECT udf('') AS `xxx`, udf(J1_TBL.i), udf(udf(J1_TBL.j)), udf(J1_TBL.t), udf(J2_TBL.i), udf(J2_TBL.k)
+ FROM J1_TBL JOIN J2_TBL ON (udf(J1_TBL.i) = J2_TBL.i)
-- !query 43 schema
-struct<xxx:string,i:int,j:int,t:string,i:int,k:int>
+struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(j as string)) as int) as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(k as string)) AS INT):int>
-- !query 43 output
0 NULL zero 0 NULL
1 4 one 1 -1
-1756,10 +1756,10 struct<xxx:string,i:int,j:int,t:string,i:int,k:int>
-- !query 44
-SELECT '' AS `xxx`, *
- FROM J1_TBL JOIN J2_TBL ON (J1_TBL.i = J2_TBL.k)
+SELECT udf('') AS `xxx`, udf(udf(J1_TBL.i)), udf(udf(J1_TBL.j)), udf(udf(J1_TBL.t)), J2_TBL.i, J2_TBL.k
+ FROM J1_TBL JOIN J2_TBL ON (J1_TBL.i = udf(J2_TBL.k))
-- !query 44 schema
-struct<xxx:string,i:int,j:int,t:string,i:int,k:int>
+struct<xxx:string,CAST(udf(cast(cast(udf(cast(i as string)) as int) as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(j as string)) as int) as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(t as string)) as string) as string)) AS STRING):string,i:int,k:int>
-- !query 44 output
0 NULL zero NULL 0
2 3 two 2 2
-1767,10 +1767,10 struct<xxx:string,i:int,j:int,t:string,i:int,k:int>
-- !query 45
-SELECT '' AS `xxx`, *
- FROM J1_TBL JOIN J2_TBL ON (J1_TBL.i <= J2_TBL.k)
+SELECT udf('') AS `xxx`, udf(J1_TBL.i), udf(J1_TBL.j), udf(J1_TBL.t), udf(J2_TBL.i), udf(J2_TBL.k)
+ FROM J1_TBL JOIN J2_TBL ON (udf(J1_TBL.i) <= udf(udf(J2_TBL.k)))
-- !query 45 schema
-struct<xxx:string,i:int,j:int,t:string,i:int,k:int>
+struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(k as string)) AS INT):int>
-- !query 45 output
0 NULL zero 2 2
0 NULL zero 2 4
-1784,11 +1784,11 struct<xxx:string,i:int,j:int,t:string,i:int,k:int>
-- !query 46
-SELECT '' AS `xxx`, *
+SELECT udf(udf('')) AS `xxx`, udf(i), udf(j), udf(t), udf(k)
FROM J1_TBL LEFT OUTER JOIN J2_TBL USING (i)
- ORDER BY i, k, t
+ ORDER BY udf(udf(i)), udf(k), udf(t)
-- !query 46 schema
-struct<xxx:string,i:int,j:int,t:string,k:int>
+struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int>
-- !query 46 output
NULL NULL null NULL
NULL 0 zero NULL
-1806,11 +1806,11 struct<xxx:string,i:int,j:int,t:string,k:int>
-- !query 47
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, udf(i), udf(j), udf(t), udf(k)
FROM J1_TBL LEFT JOIN J2_TBL USING (i)
- ORDER BY i, k, t
+ ORDER BY udf(i), udf(udf(k)), udf(t)
-- !query 47 schema
-struct<xxx:string,i:int,j:int,t:string,k:int>
+struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int>
-- !query 47 output
NULL NULL null NULL
NULL 0 zero NULL
-1828,10 +1828,10 struct<xxx:string,i:int,j:int,t:string,k:int>
-- !query 48
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, udf(udf(i)), udf(j), udf(t), udf(k)
FROM J1_TBL RIGHT OUTER JOIN J2_TBL USING (i)
-- !query 48 schema
-struct<xxx:string,i:int,j:int,t:string,k:int>
+struct<xxx:string,CAST(udf(cast(cast(udf(cast(i as string)) as int) as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int>
-- !query 48 output
0 NULL zero NULL
1 4 one -1
-1845,10 +1845,10 struct<xxx:string,i:int,j:int,t:string,k:int>
-- !query 49
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, udf(i), udf(udf(j)), udf(t), udf(k)
FROM J1_TBL RIGHT JOIN J2_TBL USING (i)
-- !query 49 schema
-struct<xxx:string,i:int,j:int,t:string,k:int>
+struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(j as string)) as int) as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int>
-- !query 49 output
0 NULL zero NULL
1 4 one -1
-1862,11 +1862,11 struct<xxx:string,i:int,j:int,t:string,k:int>
-- !query 50
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, udf(i), udf(j), udf(udf(t)), udf(k)
FROM J1_TBL FULL OUTER JOIN J2_TBL USING (i)
- ORDER BY i, k, t
+ ORDER BY udf(udf(i)), udf(k), udf(t)
-- !query 50 schema
-struct<xxx:string,i:int,j:int,t:string,k:int>
+struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(t as string)) as string) as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int>
-- !query 50 output
NULL NULL NULL NULL
NULL NULL null NULL
-1886,11 +1886,11 struct<xxx:string,i:int,j:int,t:string,k:int>
-- !query 51
-SELECT '' AS `xxx`, *
+SELECT udf('') AS `xxx`, udf(i), udf(j), t, udf(udf(k))
FROM J1_TBL FULL JOIN J2_TBL USING (i)
- ORDER BY i, k, t
+ ORDER BY udf(udf(i)), udf(k), udf(udf(t))
-- !query 51 schema
-struct<xxx:string,i:int,j:int,t:string,k:int>
+struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,t:string,CAST(udf(cast(cast(udf(cast(k as string)) as int) as string)) AS INT):int>
-- !query 51 output
NULL NULL NULL NULL
NULL NULL null NULL
-1910,19 +1910,19 struct<xxx:string,i:int,j:int,t:string,k:int>
-- !query 52
-SELECT '' AS `xxx`, *
- FROM J1_TBL LEFT JOIN J2_TBL USING (i) WHERE (k = 1)
+SELECT udf('') AS `xxx`, udf(i), udf(j), udf(t), udf(udf(k))
+ FROM J1_TBL LEFT JOIN J2_TBL USING (i) WHERE (udf(k) = 1)
-- !query 52 schema
-struct<xxx:string,i:int,j:int,t:string,k:int>
+struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(cast(udf(cast(k as string)) as int) as string)) AS INT):int>
-- !query 52 output
-- !query 53
-SELECT '' AS `xxx`, *
- FROM J1_TBL LEFT JOIN J2_TBL USING (i) WHERE (i = 1)
+SELECT udf('') AS `xxx`, udf(i), udf(j), udf(t), udf(k)
+ FROM J1_TBL LEFT JOIN J2_TBL USING (i) WHERE (udf(udf(i)) = udf(1))
-- !query 53 schema
-struct<xxx:string,i:int,j:int,t:string,k:int>
+struct<xxx:string,CAST(udf(cast(i as string)) AS INT):int,CAST(udf(cast(j as string)) AS INT):int,CAST(udf(cast(t as string)) AS STRING):string,CAST(udf(cast(k as string)) AS INT):int>
-- !query 53 output
1 4 one -1
-2020,9 +2020,9 ee NULL 42 NULL
-- !query 65
SELECT * FROM
-(SELECT * FROM t2) as s2
+(SELECT udf(name) as name, t2.n FROM t2) as s2
INNER JOIN
-(SELECT * FROM t3) s3
+(SELECT udf(udf(name)) as name, t3.n FROM t3) s3
USING (name)
-- !query 65 schema
struct<name:string,n:int,n:int>
-2033,9 +2033,9 cc 22 23
-- !query 66
SELECT * FROM
-(SELECT * FROM t2) as s2
+(SELECT udf(udf(name)) as name, t2.n FROM t2) as s2
LEFT JOIN
-(SELECT * FROM t3) s3
+(SELECT udf(name) as name, t3.n FROM t3) s3
USING (name)
-- !query 66 schema
struct<name:string,n:int,n:int>
-2046,13 +2046,13 ee 42 NULL
-- !query 67
-SELECT * FROM
+SELECT udf(name), udf(udf(s2.n)), udf(s3.n) FROM
(SELECT * FROM t2) as s2
FULL JOIN
(SELECT * FROM t3) s3
USING (name)
-- !query 67 schema
-struct<name:string,n:int,n:int>
+struct<CAST(udf(cast(name as string)) AS STRING):string,CAST(udf(cast(cast(udf(cast(n as string)) as int) as string)) AS INT):int,CAST(udf(cast(n as string)) AS INT):int>
-- !query 67 output
bb 12 13
cc 22 23
-2062,9 +2062,9 ee 42 NULL
-- !query 68
SELECT * FROM
-(SELECT name, n as s2_n, 2 as s2_2 FROM t2) as s2
+(SELECT udf(udf(name)) as name, udf(n) as s2_n, udf(2) as s2_2 FROM t2) as s2
NATURAL INNER JOIN
-(SELECT name, n as s3_n, 3 as s3_2 FROM t3) s3
+(SELECT udf(name) as name, udf(udf(n)) as s3_n, udf(3) as s3_2 FROM t3) s3
-- !query 68 schema
struct<name:string,s2_n:int,s2_2:int,s3_n:int,s3_2:int>
-- !query 68 output
-2074,9 +2074,9 cc 22 2 23 3
-- !query 69
SELECT * FROM
-(SELECT name, n as s2_n, 2 as s2_2 FROM t2) as s2
+(SELECT udf(name) as name, udf(udf(n)) as s2_n, 2 as s2_2 FROM t2) as s2
NATURAL LEFT JOIN
-(SELECT name, n as s3_n, 3 as s3_2 FROM t3) s3
+(SELECT udf(udf(name)) as name, udf(n) as s3_n, 3 as s3_2 FROM t3) s3
-- !query 69 schema
struct<name:string,s2_n:int,s2_2:int,s3_n:int,s3_2:int>
-- !query 69 output
-2087,9 +2087,9 ee 42 2 NULL NULL
-- !query 70
SELECT * FROM
-(SELECT name, n as s2_n, 2 as s2_2 FROM t2) as s2
+(SELECT udf(name) as name, udf(n) as s2_n, 2 as s2_2 FROM t2) as s2
NATURAL FULL JOIN
-(SELECT name, n as s3_n, 3 as s3_2 FROM t3) s3
+(SELECT udf(udf(name)) as name, udf(udf(n)) as s3_n, 3 as s3_2 FROM t3) s3
-- !query 70 schema
struct<name:string,s2_n:int,s2_2:int,s3_n:int,s3_2:int>
-- !query 70 output
-2101,11 +2101,11 ee 42 2 NULL NULL
-- !query 71
SELECT * FROM
-(SELECT name, n as s1_n, 1 as s1_1 FROM t1) as s1
+(SELECT udf(udf(name)) as name, udf(n) as s1_n, 1 as s1_1 FROM t1) as s1
NATURAL INNER JOIN
-(SELECT name, n as s2_n, 2 as s2_2 FROM t2) as s2
+(SELECT udf(name) as name, udf(n) as s2_n, 2 as s2_2 FROM t2) as s2
NATURAL INNER JOIN
-(SELECT name, n as s3_n, 3 as s3_2 FROM t3) s3
+(SELECT udf(udf(udf(name))) as name, udf(n) as s3_n, 3 as s3_2 FROM t3) s3
-- !query 71 schema
struct<name:string,s1_n:int,s1_1:int,s2_n:int,s2_2:int,s3_n:int,s3_2:int>
-- !query 71 output
-2114,11 +2114,11 bb 11 1 12 2 13 3
-- !query 72
SELECT * FROM
-(SELECT name, n as s1_n, 1 as s1_1 FROM t1) as s1
+(SELECT udf(name) as name, udf(n) as s1_n, udf(udf(1)) as s1_1 FROM t1) as s1
NATURAL FULL JOIN
-(SELECT name, n as s2_n, 2 as s2_2 FROM t2) as s2
+(SELECT udf(name) as name, udf(udf(n)) as s2_n, udf(2) as s2_2 FROM t2) as s2
NATURAL FULL JOIN
-(SELECT name, n as s3_n, 3 as s3_2 FROM t3) s3
+(SELECT udf(udf(name)) as name, udf(n) as s3_n, udf(3) as s3_2 FROM t3) s3
-- !query 72 schema
struct<name:string,s1_n:int,s1_1:int,s2_n:int,s2_2:int,s3_n:int,s3_2:int>
-- !query 72 output
-2129,16 +2129,16 ee NULL NULL 42 2 NULL NULL
-- !query 73
-SELECT * FROM
-(SELECT name, n as s1_n FROM t1) as s1
+SELECT name, udf(udf(s1_n)), udf(s2_n), udf(s3_n) FROM
+(SELECT name, udf(udf(n)) as s1_n FROM t1) as s1
NATURAL FULL JOIN
(SELECT * FROM
- (SELECT name, n as s2_n FROM t2) as s2
+ (SELECT name, udf(n) as s2_n FROM t2) as s2
NATURAL FULL JOIN
- (SELECT name, n as s3_n FROM t3) as s3
+ (SELECT name, udf(udf(n)) as s3_n FROM t3) as s3
) ss2
-- !query 73 schema
-struct<name:string,s1_n:int,s2_n:int,s3_n:int>
+struct<name:string,CAST(udf(cast(cast(udf(cast(s1_n as string)) as int) as string)) AS INT):int,CAST(udf(cast(s2_n as string)) AS INT):int,CAST(udf(cast(s3_n as string)) AS INT):int>
-- !query 73 output
bb 11 12 13
cc NULL 22 23
-2151,9 +2151,9 SELECT * FROM
(SELECT name, n as s1_n FROM t1) as s1
NATURAL FULL JOIN
(SELECT * FROM
- (SELECT name, n as s2_n, 2 as s2_2 FROM t2) as s2
+ (SELECT name, udf(udf(n)) as s2_n, 2 as s2_2 FROM t2) as s2
NATURAL FULL JOIN
- (SELECT name, n as s3_n FROM t3) as s3
+ (SELECT name, udf(n) as s3_n FROM t3) as s3
) ss2
-- !query 74 schema
struct<name:string,s1_n:int,s2_n:int,s2_2:int,s3_n:int>
-2165,13 +2165,13 ee NULL 42 2 NULL
-- !query 75
-SELECT * FROM
- (SELECT name, n as s1_n FROM t1) as s1
+SELECT s1.name, udf(s1_n), s2.name, udf(udf(s2_n)) FROM
+ (SELECT name, udf(n) as s1_n FROM t1) as s1
FULL JOIN
(SELECT name, 2 as s2_n FROM t2) as s2
-ON (s1_n = s2_n)
+ON (udf(udf(s1_n)) = udf(s2_n))
-- !query 75 schema
-struct<name:string,s1_n:int,name:string,s2_n:int>
+struct<name:string,CAST(udf(cast(s1_n as string)) AS INT):int,name:string,CAST(udf(cast(cast(udf(cast(s2_n as string)) as int) as string)) AS INT):int>
-- !query 75 output
NULL NULL bb 2
NULL NULL cc 2
-2200,9 +2200,9 struct<>
-- !query 78
-select * from x
+select udf(udf(x1)), udf(x2) from x
-- !query 78 schema
-struct<x1:int,x2:int>
+struct<CAST(udf(cast(cast(udf(cast(x1 as string)) as int) as string)) AS INT):int,CAST(udf(cast(x2 as string)) AS INT):int>
-- !query 78 output
1 11
2 22
-2212,9 +2212,9 struct<x1:int,x2:int>
-- !query 79
-select * from y
+select udf(y1), udf(udf(y2)) from y
-- !query 79 schema
-struct<y1:int,y2:int>
+struct<CAST(udf(cast(y1 as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(y2 as string)) as int) as string)) AS INT):int>
-- !query 79 output
1 111
2 222
-2223,7 +2223,7 struct<y1:int,y2:int>
-- !query 80
-select * from x left join y on (x1 = y1 and x2 is not null)
+select * from x left join y on (udf(x1) = udf(udf(y1)) and udf(x2) is not null)
-- !query 80 schema
struct<x1:int,x2:int,y1:int,y2:int>
-- !query 80 output
-2235,7 +2235,7 struct<x1:int,x2:int,y1:int,y2:int>
-- !query 81
-select * from x left join y on (x1 = y1 and y2 is not null)
+select * from x left join y on (udf(udf(x1)) = udf(y1) and udf(y2) is not null)
-- !query 81 schema
struct<x1:int,x2:int,y1:int,y2:int>
-- !query 81 output
-2247,8 +2247,8 struct<x1:int,x2:int,y1:int,y2:int>
-- !query 82
-select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2)
-on (x1 = xx1)
+select * from (x left join y on (udf(x1) = udf(udf(y1)))) left join x xx(xx1,xx2)
+on (udf(udf(x1)) = udf(xx1))
-- !query 82 schema
struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 82 output
-2260,8 +2260,8 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 83
-select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2)
-on (x1 = xx1 and x2 is not null)
+select * from (x left join y on (udf(x1) = udf(y1))) left join x xx(xx1,xx2)
+on (udf(x1) = xx1 and udf(x2) is not null)
-- !query 83 schema
struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 83 output
-2273,8 +2273,8 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 84
-select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2)
-on (x1 = xx1 and y2 is not null)
+select * from (x left join y on (x1 = udf(y1))) left join x xx(xx1,xx2)
+on (udf(x1) = udf(udf(xx1)) and udf(y2) is not null)
-- !query 84 schema
struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 84 output
-2286,8 +2286,8 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 85
-select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2)
-on (x1 = xx1 and xx2 is not null)
+select * from (x left join y on (udf(x1) = y1)) left join x xx(xx1,xx2)
+on (udf(udf(x1)) = udf(xx1) and udf(udf(xx2)) is not null)
-- !query 85 schema
struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 85 output
-2299,8 +2299,8 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 86
-select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2)
-on (x1 = xx1) where (x2 is not null)
+select * from (x left join y on (udf(udf(x1)) = udf(udf(y1)))) left join x xx(xx1,xx2)
+on (udf(x1) = udf(xx1)) where (udf(x2) is not null)
-- !query 86 schema
struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 86 output
-2310,8 +2310,8 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 87
-select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2)
-on (x1 = xx1) where (y2 is not null)
+select * from (x left join y on (udf(x1) = udf(y1))) left join x xx(xx1,xx2)
+on (udf(x1) = xx1) where (udf(y2) is not null)
-- !query 87 schema
struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 87 output
-2321,8 +2321,8 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 88
-select * from (x left join y on (x1 = y1)) left join x xx(xx1,xx2)
-on (x1 = xx1) where (xx2 is not null)
+select * from (x left join y on (udf(x1) = udf(y1))) left join x xx(xx1,xx2)
+on (x1 = udf(xx1)) where (xx2 is not null)
-- !query 88 schema
struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 88 output
-2332,75 +2332,75 struct<x1:int,x2:int,y1:int,y2:int,xx1:int,xx2:int>
-- !query 89
-select count(*) from tenk1 a where unique1 in
- (select unique1 from tenk1 b join tenk1 c using (unique1)
- where b.unique2 = 42)
+select udf(udf(count(*))) from tenk1 a where udf(udf(unique1)) in
+ (select udf(unique1) from tenk1 b join tenk1 c using (unique1)
+ where udf(udf(b.unique2)) = udf(42))
-- !query 89 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(cast(udf(cast(count(1) as string)) as bigint) as string)) AS BIGINT):bigint>
-- !query 89 output
1
-- !query 90
-select count(*) from tenk1 x where
- x.unique1 in (select a.f1 from int4_tbl a,float8_tbl b where a.f1=b.f1) and
- x.unique1 = 0 and
- x.unique1 in (select aa.f1 from int4_tbl aa,float8_tbl bb where aa.f1=bb.f1)
+select udf(count(*)) from tenk1 x where
+ udf(x.unique1) in (select udf(a.f1) from int4_tbl a,float8_tbl b where udf(udf(a.f1))=b.f1) and
+ udf(x.unique1) = 0 and
+ udf(x.unique1) in (select aa.f1 from int4_tbl aa,float8_tbl bb where aa.f1=udf(udf(bb.f1)))
-- !query 90 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 90 output
1
-- !query 91
-select count(*) from tenk1 x where
- x.unique1 in (select a.f1 from int4_tbl a,float8_tbl b where a.f1=b.f1) and
- x.unique1 = 0 and
- x.unique1 in (select aa.f1 from int4_tbl aa,float8_tbl bb where aa.f1=bb.f1)
+select udf(udf(count(*))) from tenk1 x where
+ udf(x.unique1) in (select udf(a.f1) from int4_tbl a,float8_tbl b where udf(udf(a.f1))=b.f1) and
+ udf(x.unique1) = 0 and
+ udf(udf(x.unique1)) in (select udf(aa.f1) from int4_tbl aa,float8_tbl bb where udf(aa.f1)=udf(udf(bb.f1)))
-- !query 91 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(cast(udf(cast(count(1) as string)) as bigint) as string)) AS BIGINT):bigint>
-- !query 91 output
1
-- !query 92
select * from int8_tbl i1 left join (int8_tbl i2 join
- (select 123 as x) ss on i2.q1 = x) on i1.q2 = i2.q2
-order by 1, 2
+ (select udf(123) as x) ss on udf(udf(i2.q1)) = udf(x)) on udf(udf(i1.q2)) = udf(udf(i2.q2))
+order by udf(udf(1)), 2
-- !query 92 schema
struct<q1:bigint,q2:bigint,q1:bigint,q2:bigint,x:int>
-- !query 92 output
-123 456 123 456 123
-123 4567890123456789 123 4567890123456789 123
4567890123456789 -4567890123456789 NULL NULL NULL
4567890123456789 123 NULL NULL NULL
+123 456 123 456 123
+123 4567890123456789 123 4567890123456789 123
4567890123456789 4567890123456789 123 4567890123456789 123
-- !query 93
-select count(*)
+select udf(count(*))
from
- (select t3.tenthous as x1, coalesce(t1.stringu1, t2.stringu1) as x2
+ (select udf(t3.tenthous) as x1, udf(coalesce(udf(t1.stringu1), udf(t2.stringu1))) as x2
from tenk1 t1
- left join tenk1 t2 on t1.unique1 = t2.unique1
- join tenk1 t3 on t1.unique2 = t3.unique2) ss,
+ left join tenk1 t2 on udf(t1.unique1) = udf(t2.unique1)
+ join tenk1 t3 on t1.unique2 = udf(t3.unique2)) ss,
tenk1 t4,
tenk1 t5
-where t4.thousand = t5.unique1 and ss.x1 = t4.tenthous and ss.x2 = t5.stringu1
+where udf(t4.thousand) = udf(t5.unique1) and udf(udf(ss.x1)) = t4.tenthous and udf(ss.x2) = udf(udf(t5.stringu1))
-- !query 93 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 93 output
1000
-- !query 94
-select a.f1, b.f1, t.thousand, t.tenthous from
+select udf(a.f1), udf(b.f1), udf(t.thousand), udf(t.tenthous) from
tenk1 t,
- (select sum(f1)+1 as f1 from int4_tbl i4a) a,
- (select sum(f1) as f1 from int4_tbl i4b) b
-where b.f1 = t.thousand and a.f1 = b.f1 and (a.f1+b.f1+999) = t.tenthous
+ (select udf(udf(sum(udf(f1))+1)) as f1 from int4_tbl i4a) a,
+ (select udf(sum(udf(f1))) as f1 from int4_tbl i4b) b
+where b.f1 = udf(t.thousand) and udf(a.f1) = udf(b.f1) and udf((udf(a.f1)+udf(b.f1)+999)) = udf(udf(t.tenthous))
-- !query 94 schema
-struct<f1:bigint,f1:bigint,thousand:int,tenthous:int>
+struct<CAST(udf(cast(f1 as string)) AS BIGINT):bigint,CAST(udf(cast(f1 as string)) AS BIGINT):bigint,CAST(udf(cast(thousand as string)) AS INT):int,CAST(udf(cast(tenthous as string)) AS INT):int>
-- !query 94 output
-2408,8 +2408,8 struct<f1:bigint,f1:bigint,thousand:int,tenthous:int>
-- !query 95
select * from
j1_tbl full join
- (select * from j2_tbl order by j2_tbl.i desc, j2_tbl.k asc) j2_tbl
- on j1_tbl.i = j2_tbl.i and j1_tbl.i = j2_tbl.k
+ (select * from j2_tbl order by udf(udf(j2_tbl.i)) desc, udf(j2_tbl.k) asc) j2_tbl
+ on udf(j1_tbl.i) = udf(j2_tbl.i) and udf(j1_tbl.i) = udf(j2_tbl.k)
-- !query 95 schema
struct<i:int,j:int,t:string,i:int,k:int>
-- !query 95 output
-2435,13 +2435,13 NULL NULL null NULL NULL
-- !query 96
-select count(*) from
- (select * from tenk1 x order by x.thousand, x.twothousand, x.fivethous) x
+select udf(count(*)) from
+ (select * from tenk1 x order by udf(x.thousand), udf(udf(x.twothousand)), x.fivethous) x
left join
- (select * from tenk1 y order by y.unique2) y
- on x.thousand = y.unique2 and x.twothousand = y.hundred and x.fivethous = y.unique2
+ (select * from tenk1 y order by udf(y.unique2)) y
+ on udf(x.thousand) = y.unique2 and x.twothousand = udf(y.hundred) and x.fivethous = y.unique2
-- !query 96 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 96 output
10000
-2507,7 +2507,7 struct<>
-- !query 104
-select tt1.*, tt2.* from tt1 left join tt2 on tt1.joincol = tt2.joincol
+select tt1.*, tt2.* from tt1 left join tt2 on udf(udf(tt1.joincol)) = udf(tt2.joincol)
-- !query 104 schema
struct<tt1_id:int,joincol:int,tt2_id:int,joincol:int>
-- !query 104 output
-2517,7 +2517,7 struct<tt1_id:int,joincol:int,tt2_id:int,joincol:int>
-- !query 105
-select tt1.*, tt2.* from tt2 right join tt1 on tt1.joincol = tt2.joincol
+select tt1.*, tt2.* from tt2 right join tt1 on udf(udf(tt1.joincol)) = udf(udf(tt2.joincol))
-- !query 105 schema
struct<tt1_id:int,joincol:int,tt2_id:int,joincol:int>
-- !query 105 output
-2527,10 +2527,10 struct<tt1_id:int,joincol:int,tt2_id:int,joincol:int>
-- !query 106
-select count(*) from tenk1 a, tenk1 b
- where a.hundred = b.thousand and (b.fivethous % 10) < 10
+select udf(count(*)) from tenk1 a, tenk1 b
+ where udf(a.hundred) = b.thousand and udf(udf((b.fivethous % 10)) < 10)
-- !query 106 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 106 output
100000
-2584,14 +2584,14 struct<>
-- !query 113
-SELECT a.f1
+SELECT udf(udf(a.f1)) as f1
FROM tt4 a
LEFT JOIN (
SELECT b.f1
- FROM tt3 b LEFT JOIN tt3 c ON (b.f1 = c.f1)
- WHERE c.f1 IS NULL
-) AS d ON (a.f1 = d.f1)
-WHERE d.f1 IS NULL
+ FROM tt3 b LEFT JOIN tt3 c ON udf(b.f1) = udf(c.f1)
+ WHERE udf(c.f1) IS NULL
+) AS d ON udf(a.f1) = d.f1
+WHERE udf(udf(d.f1)) IS NULL
-- !query 113 schema
struct<f1:int>
-- !query 113 output
-2621,7 +2621,7 struct<>
-- !query 116
-select * from tt5,tt6 where tt5.f1 = tt6.f1 and tt5.f1 = tt5.f2 - tt6.f2
+select * from tt5,tt6 where udf(tt5.f1) = udf(tt6.f1) and udf(tt5.f1) = udf(udf(tt5.f2) - udf(tt6.f2))
-- !query 116 schema
struct<f1:int,f2:int,f1:int,f2:int>
-- !query 116 output
-2649,12 +2649,12 struct<>
-- !query 119
-select yy.pkyy as yy_pkyy, yy.pkxx as yy_pkxx, yya.pkyy as yya_pkyy,
- xxa.pkxx as xxa_pkxx, xxb.pkxx as xxb_pkxx
+select udf(udf(yy.pkyy)) as yy_pkyy, udf(yy.pkxx) as yy_pkxx, udf(yya.pkyy) as yya_pkyy,
+ udf(xxa.pkxx) as xxa_pkxx, udf(xxb.pkxx) as xxb_pkxx
from yy
- left join (SELECT * FROM yy where pkyy = 101) as yya ON yy.pkyy = yya.pkyy
- left join xx xxa on yya.pkxx = xxa.pkxx
- left join xx xxb on coalesce (xxa.pkxx, 1) = xxb.pkxx
+ left join (SELECT * FROM yy where pkyy = 101) as yya ON udf(yy.pkyy) = udf(yya.pkyy)
+ left join xx xxa on udf(yya.pkxx) = udf(udf(xxa.pkxx))
+ left join xx xxb on udf(udf(coalesce (xxa.pkxx, 1))) = udf(xxb.pkxx)
-- !query 119 schema
struct<yy_pkyy:int,yy_pkxx:int,yya_pkyy:int,xxa_pkxx:int,xxb_pkxx:int>
-- !query 119 output
-2693,9 +2693,9 struct<>
-- !query 123
select * from
- zt2 left join zt3 on (f2 = f3)
- left join zt1 on (f3 = f1)
-where f2 = 53
+ zt2 left join zt3 on (udf(f2) = udf(udf(f3)))
+ left join zt1 on (udf(udf(f3)) = udf(f1))
+where udf(f2) = 53
-- !query 123 schema
struct<f2:int,f3:int,f1:int>
-- !query 123 output
-2712,9 +2712,9 struct<>
-- !query 125
select * from
- zt2 left join zt3 on (f2 = f3)
- left join zv1 on (f3 = f1)
-where f2 = 53
+ zt2 left join zt3 on (f2 = udf(f3))
+ left join zv1 on (udf(f3) = f1)
+where udf(udf(f2)) = 53
-- !query 125 schema
struct<f2:int,f3:int,f1:int,junk:string>
-- !query 125 output
-2722,12 +2722,12 struct<f2:int,f3:int,f1:int,junk:string>
-- !query 126
-select a.unique2, a.ten, b.tenthous, b.unique2, b.hundred
-from tenk1 a left join tenk1 b on a.unique2 = b.tenthous
-where a.unique1 = 42 and
- ((b.unique2 is null and a.ten = 2) or b.hundred = 3)
+select udf(a.unique2), udf(a.ten), udf(b.tenthous), udf(b.unique2), udf(b.hundred)
+from tenk1 a left join tenk1 b on a.unique2 = udf(b.tenthous)
+where udf(a.unique1) = 42 and
+ ((udf(b.unique2) is null and udf(a.ten) = 2) or udf(udf(b.hundred)) = udf(udf(3)))
-- !query 126 schema
-struct<unique2:int,ten:int,tenthous:int,unique2:int,hundred:int>
+struct<CAST(udf(cast(unique2 as string)) AS INT):int,CAST(udf(cast(ten as string)) AS INT):int,CAST(udf(cast(tenthous as string)) AS INT):int,CAST(udf(cast(unique2 as string)) AS INT):int,CAST(udf(cast(hundred as string)) AS INT):int>
-- !query 126 output
-2749,7 +2749,7 struct<>
-- !query 129
-select * from a left join b on i = x and i = y and x = i
+select * from a left join b on udf(i) = x and i = udf(y) and udf(x) = udf(i)
-- !query 129 schema
struct<i:int,x:int,y:int>
-- !query 129 output
-2757,11 +2757,11 struct<i:int,x:int,y:int>
-- !query 130
-select t1.q2, count(t2.*)
-from int8_tbl t1 left join int8_tbl t2 on (t1.q2 = t2.q1)
-group by t1.q2 order by 1
+select udf(t1.q2), udf(count(t2.*))
+from int8_tbl t1 left join int8_tbl t2 on (udf(udf(t1.q2)) = t2.q1)
+group by udf(t1.q2) order by 1
-- !query 130 schema
-struct<q2:bigint,count(q1, q2):bigint>
+struct<CAST(udf(cast(q2 as string)) AS BIGINT):bigint,CAST(udf(cast(count(q1, q2) as string)) AS BIGINT):bigint>
-- !query 130 output
-4567890123456789 0
123 2
-2770,11 +2770,11 struct<q2:bigint,count(q1, q2):bigint>
-- !query 131
-select t1.q2, count(t2.*)
-from int8_tbl t1 left join (select * from int8_tbl) t2 on (t1.q2 = t2.q1)
-group by t1.q2 order by 1
+select udf(udf(t1.q2)), udf(count(t2.*))
+from int8_tbl t1 left join (select * from int8_tbl) t2 on (udf(udf(t1.q2)) = udf(t2.q1))
+group by udf(udf(t1.q2)) order by 1
-- !query 131 schema
-struct<q2:bigint,count(q1, q2):bigint>
+struct<CAST(udf(cast(cast(udf(cast(q2 as string)) as bigint) as string)) AS BIGINT):bigint,CAST(udf(cast(count(q1, q2) as string)) AS BIGINT):bigint>
-- !query 131 output
-4567890123456789 0
123 2
-2783,13 +2783,13 struct<q2:bigint,count(q1, q2):bigint>
-- !query 132
-select t1.q2, count(t2.*)
+select udf(t1.q2) as q2, udf(udf(count(t2.*)))
from int8_tbl t1 left join
- (select q1, case when q2=1 then 1 else q2 end as q2 from int8_tbl) t2
- on (t1.q2 = t2.q1)
+ (select udf(q1) as q1, case when q2=1 then 1 else q2 end as q2 from int8_tbl) t2
+ on (udf(t1.q2) = udf(t2.q1))
group by t1.q2 order by 1
-- !query 132 schema
-struct<q2:bigint,count(q1, q2):bigint>
+struct<q2:bigint,CAST(udf(cast(cast(udf(cast(count(q1, q2) as string)) as bigint) as string)) AS BIGINT):bigint>
-- !query 132 output
-4567890123456789 0
123 2
-2828,17 +2828,17 struct<>
-- !query 136
-select c.name, ss.code, ss.b_cnt, ss.const
+select udf(c.name), udf(ss.code), udf(ss.b_cnt), udf(ss.const)
from c left join
(select a.code, coalesce(b_grp.cnt, 0) as b_cnt, -1 as const
from a left join
- (select count(1) as cnt, b.a from b group by b.a) as b_grp
- on a.code = b_grp.a
+ (select udf(count(1)) as cnt, b.a as a from b group by b.a) as b_grp
+ on udf(a.code) = udf(udf(b_grp.a))
) as ss
- on (c.a = ss.code)
+ on (udf(udf(c.a)) = udf(ss.code))
order by c.name
-- !query 136 schema
-struct<name:string,code:string,b_cnt:bigint,const:int>
+struct<CAST(udf(cast(name as string)) AS STRING):string,CAST(udf(cast(code as string)) AS STRING):string,CAST(udf(cast(b_cnt as string)) AS BIGINT):bigint,CAST(udf(cast(const as string)) AS INT):int>
-- !query 136 output
A p 2 -1
B q 0 -1
-2852,15 +2852,15 LEFT JOIN
( SELECT sub3.key3, sub4.value2, COALESCE(sub4.value2, 66) as value3 FROM
( SELECT 1 as key3 ) sub3
LEFT JOIN
- ( SELECT sub5.key5, COALESCE(sub6.value1, 1) as value2 FROM
+ ( SELECT udf(sub5.key5) as key5, udf(udf(COALESCE(sub6.value1, 1))) as value2 FROM
( SELECT 1 as key5 ) sub5
LEFT JOIN
( SELECT 2 as key6, 42 as value1 ) sub6
- ON sub5.key5 = sub6.key6
+ ON sub5.key5 = udf(sub6.key6)
) sub4
- ON sub4.key5 = sub3.key3
+ ON udf(sub4.key5) = sub3.key3
) sub2
-ON sub1.key1 = sub2.key3
+ON udf(udf(sub1.key1)) = udf(udf(sub2.key3))
-- !query 137 schema
struct<key1:int,key3:int,value2:int,value3:int>
-- !query 137 output
-2871,34 +2871,34 struct<key1:int,key3:int,value2:int,value3:int>
SELECT * FROM
( SELECT 1 as key1 ) sub1
LEFT JOIN
-( SELECT sub3.key3, value2, COALESCE(value2, 66) as value3 FROM
+( SELECT udf(sub3.key3) as key3, udf(value2), udf(COALESCE(value2, 66)) as value3 FROM
( SELECT 1 as key3 ) sub3
LEFT JOIN
( SELECT sub5.key5, COALESCE(sub6.value1, 1) as value2 FROM
( SELECT 1 as key5 ) sub5
LEFT JOIN
( SELECT 2 as key6, 42 as value1 ) sub6
- ON sub5.key5 = sub6.key6
+ ON udf(udf(sub5.key5)) = sub6.key6
) sub4
ON sub4.key5 = sub3.key3
) sub2
-ON sub1.key1 = sub2.key3
+ON sub1.key1 = udf(udf(sub2.key3))
-- !query 138 schema
-struct<key1:int,key3:int,value2:int,value3:int>
+struct<key1:int,key3:int,CAST(udf(cast(value2 as string)) AS INT):int,value3:int>
-- !query 138 output
1 1 1 1
-- !query 139
-SELECT qq, unique1
+SELECT udf(qq), udf(udf(unique1))
FROM
- ( SELECT COALESCE(q1, 0) AS qq FROM int8_tbl a ) AS ss1
+ ( SELECT udf(COALESCE(q1, 0)) AS qq FROM int8_tbl a ) AS ss1
FULL OUTER JOIN
- ( SELECT COALESCE(q2, -1) AS qq FROM int8_tbl b ) AS ss2
+ ( SELECT udf(udf(COALESCE(q2, -1))) AS qq FROM int8_tbl b ) AS ss2
USING (qq)
- INNER JOIN tenk1 c ON qq = unique2
+ INNER JOIN tenk1 c ON udf(qq) = udf(unique2)
-- !query 139 schema
-struct<qq:bigint,unique1:int>
+struct<CAST(udf(cast(qq as string)) AS BIGINT):bigint,CAST(udf(cast(cast(udf(cast(unique1 as string)) as int) as string)) AS INT):int>
-- !query 139 output
123 4596
123 4596
-2936,19 +2936,19 struct<>
-- !query 143
-select nt3.id
+select udf(nt3.id)
from nt3 as nt3
left join
- (select nt2.*, (nt2.b1 and ss1.a3) AS b3
+ (select nt2.*, (udf(nt2.b1) and udf(ss1.a3)) AS b3
from nt2 as nt2
left join
- (select nt1.*, (nt1.id is not null) as a3 from nt1) as ss1
- on ss1.id = nt2.nt1_id
+ (select nt1.*, (udf(nt1.id) is not null) as a3 from nt1) as ss1
+ on ss1.id = udf(udf(nt2.nt1_id))
) as ss2
- on ss2.id = nt3.nt2_id
-where nt3.id = 1 and ss2.b3
+ on udf(ss2.id) = nt3.nt2_id
+where udf(nt3.id) = 1 and udf(ss2.b3)
-- !query 143 schema
-struct<id:int>
+struct<CAST(udf(cast(id as string)) AS INT):int>
-- !query 143 output
1
-3003,73 +3003,73 NULL 2147483647
-- !query 146
-select count(*) from
- tenk1 a join tenk1 b on a.unique1 = b.unique2
- left join tenk1 c on a.unique2 = b.unique1 and c.thousand = a.thousand
- join int4_tbl on b.thousand = f1
+select udf(count(*)) from
+ tenk1 a join tenk1 b on udf(a.unique1) = udf(b.unique2)
+ left join tenk1 c on udf(a.unique2) = udf(b.unique1) and udf(c.thousand) = udf(udf(a.thousand))
+ join int4_tbl on udf(b.thousand) = f1
-- !query 146 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 146 output
10
-- !query 147
-select b.unique1 from
- tenk1 a join tenk1 b on a.unique1 = b.unique2
- left join tenk1 c on b.unique1 = 42 and c.thousand = a.thousand
- join int4_tbl i1 on b.thousand = f1
- right join int4_tbl i2 on i2.f1 = b.tenthous
- order by 1
+select udf(b.unique1) from
+ tenk1 a join tenk1 b on udf(a.unique1) = udf(b.unique2)
+ left join tenk1 c on udf(b.unique1) = 42 and c.thousand = udf(a.thousand)
+ join int4_tbl i1 on udf(b.thousand) = udf(udf(f1))
+ right join int4_tbl i2 on udf(udf(i2.f1)) = udf(b.tenthous)
+ order by udf(1)
-- !query 147 schema
-struct<unique1:int>
+struct<CAST(udf(cast(unique1 as string)) AS INT):int>
-- !query 147 output
NULL
NULL
+0
NULL
NULL
-0
-- !query 148
select * from
(
- select unique1, q1, coalesce(unique1, -1) + q1 as fault
- from int8_tbl left join tenk1 on (q2 = unique2)
+ select udf(unique1), udf(q1), udf(udf(coalesce(unique1, -1)) + udf(q1)) as fault
+ from int8_tbl left join tenk1 on (udf(q2) = udf(unique2))
) ss
-where fault = 122
-order by fault
+where udf(fault) = udf(122)
+order by udf(fault)
-- !query 148 schema
-struct<unique1:int,q1:bigint,fault:bigint>
+struct<CAST(udf(cast(unique1 as string)) AS INT):int,CAST(udf(cast(q1 as string)) AS BIGINT):bigint,fault:bigint>
-- !query 148 output
NULL 123 122
-- !query 149
-select q1, unique2, thousand, hundred
- from int8_tbl a left join tenk1 b on q1 = unique2
- where coalesce(thousand,123) = q1 and q1 = coalesce(hundred,123)
+select udf(q1), udf(unique2), udf(thousand), udf(hundred)
+ from int8_tbl a left join tenk1 b on udf(q1) = udf(unique2)
+ where udf(coalesce(thousand,123)) = udf(q1) and udf(q1) = udf(udf(coalesce(hundred,123)))
-- !query 149 schema
-struct<q1:bigint,unique2:int,thousand:int,hundred:int>
+struct<CAST(udf(cast(q1 as string)) AS BIGINT):bigint,CAST(udf(cast(unique2 as string)) AS INT):int,CAST(udf(cast(thousand as string)) AS INT):int,CAST(udf(cast(hundred as string)) AS INT):int>
-- !query 149 output
-- !query 150
-select f1, unique2, case when unique2 is null then f1 else 0 end
- from int4_tbl a left join tenk1 b on f1 = unique2
- where (case when unique2 is null then f1 else 0 end) = 0
+select udf(f1), udf(unique2), case when udf(udf(unique2)) is null then udf(f1) else 0 end
+ from int4_tbl a left join tenk1 b on udf(f1) = udf(udf(unique2))
+ where (case when udf(unique2) is null then udf(f1) else 0 end) = 0
-- !query 150 schema
-struct<f1:int,unique2:int,CASE WHEN (unique2 IS NULL) THEN f1 ELSE 0 END:int>
+struct<CAST(udf(cast(f1 as string)) AS INT):int,CAST(udf(cast(unique2 as string)) AS INT):int,CASE WHEN (CAST(udf(cast(cast(udf(cast(unique2 as string)) as int) as string)) AS INT) IS NULL) THEN CAST(udf(cast(f1 as string)) AS INT) ELSE 0 END:int>
-- !query 150 output
0 0 0
-- !query 151
-select a.unique1, b.unique1, c.unique1, coalesce(b.twothousand, a.twothousand)
- from tenk1 a left join tenk1 b on b.thousand = a.unique1 left join tenk1 c on c.unique2 = coalesce(b.twothousand, a.twothousand)
- where a.unique2 < 10 and coalesce(b.twothousand, a.twothousand) = 44
+select udf(a.unique1), udf(b.unique1), udf(c.unique1), udf(coalesce(b.twothousand, a.twothousand))
+ from tenk1 a left join tenk1 b on udf(b.thousand) = a.unique1 left join tenk1 c on udf(c.unique2) = udf(coalesce(b.twothousand, a.twothousand))
+ where a.unique2 < udf(10) and udf(udf(coalesce(b.twothousand, a.twothousand))) = udf(44)
-- !query 151 schema
-struct<unique1:int,unique1:int,unique1:int,coalesce(twothousand, twothousand):int>
+struct<CAST(udf(cast(unique1 as string)) AS INT):int,CAST(udf(cast(unique1 as string)) AS INT):int,CAST(udf(cast(unique1 as string)) AS INT):int,CAST(udf(cast(coalesce(twothousand, twothousand) as string)) AS INT):int>
-- !query 151 output
-3078,11 +3078,11 struct<unique1:int,unique1:int,unique1:int,coalesce(twothousand, twothousand):in
select * from
text_tbl t1
inner join int8_tbl i8
- on i8.q2 = 456
+ on udf(i8.q2) = udf(udf(456))
right join text_tbl t2
- on t1.f1 = 'doh!'
+ on udf(t1.f1) = udf(udf('doh!'))
left join int4_tbl i4
- on i8.q1 = i4.f1
+ on udf(udf(i8.q1)) = i4.f1
-- !query 152 schema
struct<f1:string,q1:bigint,q2:bigint,f1:string,f1:int>
-- !query 152 output
-3092,10 +3092,10 doh! 123 456 hi de ho neighbor NULL
-- !query 153
select * from
- (select 1 as id) as xx
+ (select udf(udf(1)) as id) as xx
left join
- (tenk1 as a1 full join (select 1 as id) as yy on (a1.unique1 = yy.id))
- on (xx.id = coalesce(yy.id))
+ (tenk1 as a1 full join (select udf(1) as id) as yy on (udf(a1.unique1) = udf(yy.id)))
+ on (xx.id = udf(udf(coalesce(yy.id))))
-- !query 153 schema
struct<id:int,unique1:int,unique2:int,two:int,four:int,ten:int,twenty:int,hundred:int,thousand:int,twothousand:int,fivethous:int,tenthous:int,odd:int,even:int,stringu1:string,stringu2:string,string4:string,id:int>
-- !query 153 output
-3103,11 +3103,11 struct<id:int,unique1:int,unique2:int,two:int,four:int,ten:int,twenty:int,hundre
-- !query 154
-select a.q2, b.q1
- from int8_tbl a left join int8_tbl b on a.q2 = coalesce(b.q1, 1)
- where coalesce(b.q1, 1) > 0
+select udf(a.q2), udf(b.q1)
+ from int8_tbl a left join int8_tbl b on udf(a.q2) = coalesce(b.q1, 1)
+ where udf(udf(coalesce(b.q1, 1)) > 0)
-- !query 154 schema
-struct<q2:bigint,q1:bigint>
+struct<CAST(udf(cast(q2 as string)) AS BIGINT):bigint,CAST(udf(cast(q1 as string)) AS BIGINT):bigint>
-- !query 154 output
-4567890123456789 NULL
123 123
-3142,7 +3142,7 struct<>
-- !query 157
-select p.* from parent p left join child c on (p.k = c.k)
+select p.* from parent p left join child c on (udf(p.k) = udf(c.k))
-- !query 157 schema
struct<k:int,pd:int>
-- !query 157 output
-3153,8 +3153,8 struct<k:int,pd:int>
-- !query 158
select p.*, linked from parent p
- left join (select c.*, true as linked from child c) as ss
- on (p.k = ss.k)
+ left join (select c.*, udf(udf(true)) as linked from child c) as ss
+ on (udf(p.k) = udf(udf(ss.k)))
-- !query 158 schema
struct<k:int,pd:int,linked:boolean>
-- !query 158 output
-3165,8 +3165,8 struct<k:int,pd:int,linked:boolean>
-- !query 159
select p.* from
- parent p left join child c on (p.k = c.k)
- where p.k = 1 and p.k = 2
+ parent p left join child c on (udf(p.k) = c.k)
+ where p.k = udf(1) and udf(udf(p.k)) = udf(udf(2))
-- !query 159 schema
struct<k:int,pd:int>
-- !query 159 output
-3175,8 +3175,8 struct<k:int,pd:int>
-- !query 160
select p.* from
- (parent p left join child c on (p.k = c.k)) join parent x on p.k = x.k
- where p.k = 1 and p.k = 2
+ (parent p left join child c on (udf(p.k) = c.k)) join parent x on p.k = udf(x.k)
+ where udf(p.k) = udf(1) and udf(udf(p.k)) = udf(udf(2))
-- !query 160 schema
struct<k:int,pd:int>
-- !query 160 output
-3204,7 +3204,7 struct<>
-- !query 163
-SELECT * FROM b LEFT JOIN a ON (b.a_id = a.id) WHERE (a.id IS NULL OR a.id > 0)
+SELECT * FROM b LEFT JOIN a ON (udf(b.a_id) = udf(a.id)) WHERE (udf(udf(a.id)) IS NULL OR udf(a.id) > 0)
-- !query 163 schema
struct<id:int,a_id:int,id:int>
-- !query 163 output
-3212,7 +3212,7 struct<id:int,a_id:int,id:int>
-- !query 164
-SELECT b.* FROM b LEFT JOIN a ON (b.a_id = a.id) WHERE (a.id IS NULL OR a.id > 0)
+SELECT b.* FROM b LEFT JOIN a ON (udf(b.a_id) = udf(a.id)) WHERE (udf(a.id) IS NULL OR udf(udf(a.id)) > 0)
-- !query 164 schema
struct<id:int,a_id:int>
-- !query 164 output
-3231,13 +3231,13 struct<>
-- !query 166
SELECT * FROM
- (SELECT 1 AS x) ss1
+ (SELECT udf(1) AS x) ss1
LEFT JOIN
- (SELECT q1, q2, COALESCE(dat1, q1) AS y
- FROM int8_tbl LEFT JOIN innertab ON q2 = id) ss2
+ (SELECT udf(q1), udf(q2), udf(COALESCE(dat1, q1)) AS y
+ FROM int8_tbl LEFT JOIN innertab ON udf(udf(q2)) = id) ss2
ON true
-- !query 166 schema
-struct<x:int,q1:bigint,q2:bigint,y:bigint>
+struct<x:int,CAST(udf(cast(q1 as string)) AS BIGINT):bigint,CAST(udf(cast(q2 as string)) AS BIGINT):bigint,y:bigint>
-- !query 166 output
1 123 456 123
1 123 4567890123456789 123
-3248,27 +3248,27 struct<x:int,q1:bigint,q2:bigint,y:bigint>
-- !query 167
select * from
- int8_tbl x join (int4_tbl x cross join int4_tbl y) j on q1 = f1
+ int8_tbl x join (int4_tbl x cross join int4_tbl y) j on udf(q1) = udf(f1)
-- !query 167 schema
struct<>
-- !query 167 output
org.apache.spark.sql.AnalysisException
-Reference 'f1' is ambiguous, could be: j.f1, j.f1.; line 2 pos 63
+Reference 'f1' is ambiguous, could be: j.f1, j.f1.; line 2 pos 72
-- !query 168
select * from
- int8_tbl x join (int4_tbl x cross join int4_tbl y) j on q1 = y.f1
+ int8_tbl x join (int4_tbl x cross join int4_tbl y) j on udf(q1) = udf(y.f1)
-- !query 168 schema
struct<>
-- !query 168 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`y.f1`' given input columns: [j.f1, j.f1, x.q1, x.q2]; line 2 pos 63
+cannot resolve '`y.f1`' given input columns: [j.f1, j.f1, x.q1, x.q2]; line 2 pos 72
-- !query 169
select * from
- int8_tbl x join (int4_tbl x cross join int4_tbl y(ff)) j on q1 = f1
+ int8_tbl x join (int4_tbl x cross join int4_tbl y(ff)) j on udf(q1) = udf(udf(f1))
-- !query 169 schema
struct<q1:bigint,q2:bigint,f1:int,ff:int>
-- !query 169 output
-3276,69 +3276,69 struct<q1:bigint,q2:bigint,f1:int,ff:int>
-- !query 170
-select t1.uunique1 from
- tenk1 t1 join tenk2 t2 on t1.two = t2.two
+select udf(t1.uunique1) from
+ tenk1 t1 join tenk2 t2 on t1.two = udf(t2.two)
-- !query 170 schema
struct<>
-- !query 170 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`t1.uunique1`' given input columns: [t1.even, t2.even, t1.fivethous, t2.fivethous, t1.four, t2.four, t1.hundred, t2.hundred, t1.odd, t2.odd, t1.string4, t2.string4, t1.stringu1, t2.stringu1, t1.stringu2, t2.stringu2, t1.ten, t2.ten, t1.tenthous, t2.tenthous, t1.thousand, t2.thousand, t1.twenty, t2.twenty, t1.two, t2.two, t1.twothousand, t2.twothousand, t1.unique1, t2.unique1, t1.unique2, t2.unique2]; line 1 pos 7
+cannot resolve '`t1.uunique1`' given input columns: [t1.even, t2.even, t1.fivethous, t2.fivethous, t1.four, t2.four, t1.hundred, t2.hundred, t1.odd, t2.odd, t1.string4, t2.string4, t1.stringu1, t2.stringu1, t1.stringu2, t2.stringu2, t1.ten, t2.ten, t1.tenthous, t2.tenthous, t1.thousand, t2.thousand, t1.twenty, t2.twenty, t1.two, t2.two, t1.twothousand, t2.twothousand, t1.unique1, t2.unique1, t1.unique2, t2.unique2]; line 1 pos 11
-- !query 171
-select t2.uunique1 from
- tenk1 t1 join tenk2 t2 on t1.two = t2.two
+select udf(udf(t2.uunique1)) from
+ tenk1 t1 join tenk2 t2 on udf(t1.two) = t2.two
-- !query 171 schema
struct<>
-- !query 171 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`t2.uunique1`' given input columns: [t1.even, t2.even, t1.fivethous, t2.fivethous, t1.four, t2.four, t1.hundred, t2.hundred, t1.odd, t2.odd, t1.string4, t2.string4, t1.stringu1, t2.stringu1, t1.stringu2, t2.stringu2, t1.ten, t2.ten, t1.tenthous, t2.tenthous, t1.thousand, t2.thousand, t1.twenty, t2.twenty, t1.two, t2.two, t1.twothousand, t2.twothousand, t1.unique1, t2.unique1, t1.unique2, t2.unique2]; line 1 pos 7
+cannot resolve '`t2.uunique1`' given input columns: [t1.even, t2.even, t1.fivethous, t2.fivethous, t1.four, t2.four, t1.hundred, t2.hundred, t1.odd, t2.odd, t1.string4, t2.string4, t1.stringu1, t2.stringu1, t1.stringu2, t2.stringu2, t1.ten, t2.ten, t1.tenthous, t2.tenthous, t1.thousand, t2.thousand, t1.twenty, t2.twenty, t1.two, t2.two, t1.twothousand, t2.twothousand, t1.unique1, t2.unique1, t1.unique2, t2.unique2]; line 1 pos 15
-- !query 172
-select uunique1 from
- tenk1 t1 join tenk2 t2 on t1.two = t2.two
+select udf(uunique1) from
+ tenk1 t1 join tenk2 t2 on udf(t1.two) = udf(t2.two)
-- !query 172 schema
struct<>
-- !query 172 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`uunique1`' given input columns: [t1.even, t2.even, t1.fivethous, t2.fivethous, t1.four, t2.four, t1.hundred, t2.hundred, t1.odd, t2.odd, t1.string4, t2.string4, t1.stringu1, t2.stringu1, t1.stringu2, t2.stringu2, t1.ten, t2.ten, t1.tenthous, t2.tenthous, t1.thousand, t2.thousand, t1.twenty, t2.twenty, t1.two, t2.two, t1.twothousand, t2.twothousand, t1.unique1, t2.unique1, t1.unique2, t2.unique2]; line 1 pos 7
+cannot resolve '`uunique1`' given input columns: [t1.even, t2.even, t1.fivethous, t2.fivethous, t1.four, t2.four, t1.hundred, t2.hundred, t1.odd, t2.odd, t1.string4, t2.string4, t1.stringu1, t2.stringu1, t1.stringu2, t2.stringu2, t1.ten, t2.ten, t1.tenthous, t2.tenthous, t1.thousand, t2.thousand, t1.twenty, t2.twenty, t1.two, t2.two, t1.twothousand, t2.twothousand, t1.unique1, t2.unique1, t1.unique2, t2.unique2]; line 1 pos 11
-- !query 173
-select f1,g from int4_tbl a, (select f1 as g) ss
+select udf(udf(f1,g)) from int4_tbl a, (select udf(udf(f1)) as g) ss
-- !query 173 schema
struct<>
-- !query 173 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`f1`' given input columns: []; line 1 pos 37
+cannot resolve '`f1`' given input columns: []; line 1 pos 55
-- !query 174
-select f1,g from int4_tbl a, (select a.f1 as g) ss
+select udf(f1,g) from int4_tbl a, (select a.f1 as g) ss
-- !query 174 schema
struct<>
-- !query 174 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`a.f1`' given input columns: []; line 1 pos 37
+cannot resolve '`a.f1`' given input columns: []; line 1 pos 42
-- !query 175
-select f1,g from int4_tbl a cross join (select f1 as g) ss
+select udf(udf(f1,g)) from int4_tbl a cross join (select udf(f1) as g) ss
-- !query 175 schema
struct<>
-- !query 175 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`f1`' given input columns: []; line 1 pos 47
+cannot resolve '`f1`' given input columns: []; line 1 pos 61
-- !query 176
-select f1,g from int4_tbl a cross join (select a.f1 as g) ss
+select udf(f1,g) from int4_tbl a cross join (select udf(udf(a.f1)) as g) ss
-- !query 176 schema
struct<>
-- !query 176 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`a.f1`' given input columns: []; line 1 pos 47
+cannot resolve '`a.f1`' given input columns: []; line 1 pos 60
-- !query 177
-3383,8 +3383,8 struct<>
-- !query 182
select * from j1
-inner join j2 on j1.id1 = j2.id1 and j1.id2 = j2.id2
-where j1.id1 % 1000 = 1 and j2.id1 % 1000 = 1
+inner join j2 on udf(j1.id1) = udf(j2.id1) and udf(udf(j1.id2)) = udf(j2.id2)
+where udf(j1.id1) % 1000 = 1 and udf(udf(j2.id1) % 1000) = 1
-- !query 182 schema
struct<id1:int,id2:int,id1:int,id2:int>
-- !query 182 output
```
</p>
</details>
## How was this patch tested?
Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
Closes#25371 from huaxingao/spark-28393.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The Spark SQL test framework needs to support 2 kinds of tests:
1. tests inside Spark to test Spark itself (extends `SparkFunSuite`)
2. test outside of Spark to test Spark applications (introduced at b57ed2245c)
The class hierarchy of the major testing traits:
`PlanTestBase`, `SQLTestUtilsBase` and `SharedSparkSession` intentionally don't extend `SparkFunSuite`, so that they can be used for tests outside of Spark. Tests in Spark should extends `QueryTest` and/or `SharedSQLContext` in most cases.
However, the name is a little confusing. As a result, some test suites extend `SharedSparkSession` instead of `SharedSQLContext`. `SharedSparkSession` doesn't work well with `SparkFunSuite` as it doesn't have the special handling of thread auditing in `SharedSQLContext`. For example, you will see a warning starting with `===== POSSIBLE THREAD LEAK IN SUITE` when you run `DataFrameSelfJoinSuite`.
This PR proposes to rename `SharedSparkSession` to `SharedSparkSessionBase`, and rename `SharedSQLContext` to `SharedSparkSession`.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#25463 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR reverts some of the latest changes in `ReduceNumShufflePartitions` to fix the case when there are different pre-shuffle partition numbers in the plan. Please see the new UT for an example.
### Why are the changes needed?
Eliminate a bug.
### Does this PR introduce any user-facing change?
Yes, some queries that failed will succeed now.
### How was this patch tested?
Added new UT.
Closes#25479 from peter-toth/SPARK-28356-followup.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
in order to address cases where foreach writer task is failing without calling the close() method, (for example when a task is interrupted) added the option to implement an abort() method that will be called when the task is aborted. users should handle resource cleanup (such as connections) in the abort() method
## How was this patch tested?
update existing unit tests.
Closes#24382 from eyalzit/SPARK-27330-foreach-writer-abort.
Lead-authored-by: Eyal Zituny <eyal.zituny@equalum.io>
Co-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Co-authored-by: eyalzit <eyal.zituny@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
only todo message updated. Need to add udf() for GroupBy Tests, after resolving following jira
[SPARK-28386] and [SPARK-26741]
## How was this patch tested?
NA, only TODO message updated.
Closes#25415 from shivusondur/jiraFollowup.
Authored-by: shivusondur <shivusondur@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add following functions:
```
def add_months(startDate: Column, numMonths: Column): Column
def date_add(start: Column, days: Column): Column
def date_sub(start: Column, days: Column): Column
```
## How was this patch tested?
UT.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#25334 from WeichenXu123/datefunc_impr.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
If both options `path` and `paths` are passed to file data source v2, both values of the options should be included as the target paths.
### Why are the changes needed?
In V1 implementation, file table location includes both values of option `path` and `paths`.
In the refactoring of https://github.com/apache/spark/pull/24025, the value of option `path` is ignored if "paths" are specified. We should make it consistent with V1.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Unit test
Closes#25473 from gengliangwang/fixPathOption.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Added new benchmark `ExtractBenchmark` for the `EXTRACT(field FROM source)` function. It was executed on all currently supported values of the `field` argument: `MILLENNIUM`, `CENTURY`, `DECADE`, `YEAR`, `ISOYEAR`, `QUARTER`, `MONTH`, `WEEK`, `DAY`, `DAYOFWEEK`, `HOUR`, `MINUTE`, `SECOND`, `MILLISECONDS`, `MICROSECONDS`, `EPOCH`. The `cast(id as timestamp)` was taken as the `source` argument.
## How was this patch tested?
By running the benchmark via:
```
$ SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.ExtractBenchmark"
```
Closes#25462 from MaxGekk/extract-benchmark.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
We add support for the V2SessionCatalog for saveAsTable, such that V2 tables can plug in and leverage existing DataFrameWriter.saveAsTable APIs to write and create tables through the session catalog.
## How was this patch tested?
Unit tests. A lot of tests broke under hive when things were not working properly under `ResolveTables`, therefore I believe the current set of tests should be sufficient in testing the table resolution and read code paths.
Closes#25402 from brkyvz/saveAsV2.
Lead-authored-by: Burak Yavuz <brkyvz@gmail.com>
Co-authored-by: Burak Yavuz <burak@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose new expressions `Epoch`, `IsoYear`, `Milliseconds` and `Microseconds`, and support additional parameters of `extract()` for feature parity with PostgreSQL (https://www.postgresql.org/docs/11/functions-datetime.html#FUNCTIONS-DATETIME-EXTRACT):
1. `epoch` - the number of seconds since 1970-01-01 00:00:00 local time in microsecond precision.
2. `isoyear` - the ISO 8601 week-numbering year that the date falls in. Each ISO 8601 week-numbering year begins with the Monday of the week containing the 4th of January.
3. `milliseconds` - the seconds field including fractional parts multiplied by 1,000.
4. `microseconds` - the seconds field including fractional parts multiplied by 1,000,000.
Here are examples:
```sql
spark-sql> SELECT EXTRACT(EPOCH FROM TIMESTAMP '2019-08-11 19:07:30.123456');
1565550450.123456
spark-sql> SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-01');
2005
spark-sql> SELECT EXTRACT(MILLISECONDS FROM TIMESTAMP '2019-08-11 19:07:30.123456');
30123.456
spark-sql> SELECT EXTRACT(MICROSECONDS FROM TIMESTAMP '2019-08-11 19:07:30.123456');
30123456
```
## How was this patch tested?
Added new tests to `DateExpressionsSuite`, and uncommented existing tests in `extract.sql` and `pgSQL/date.sql`.
Closes#25408 from MaxGekk/extract-ext3.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This pr adds DELETE support for V2 datasources. As a first step, this pr only support delete by source filters:
```scala
void delete(Filter[] filters);
```
which could not deal with complicated cases like subqueries.
Since it's uncomfortable to embed the implementation of DELETE in the current V2 APIs, a new mix-in of datasource is added, which is called `SupportsMaintenance`, similar to `SupportsRead` and `SupportsWrite`. A datasource which can be maintained means we can perform DELETE/UPDATE/MERGE/OPTIMIZE on the datasource, as long as the datasource implements the necessary mix-ins.
## How was this patch tested?
new test case.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#25115 from xianyinxin/SPARK-28351.
Authored-by: xy_xin <xianyin.xxy@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
CacheManager.cacheQuery saves the stats from the optimized plan to cache.
## How was this patch tested?
Existing testss.
Closes#24623 from jzhuge/SPARK-27739.
Authored-by: John Zhuge <jzhuge@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Here is the problem description from the JIRA.
```
When the inputs contain the constant 'infinity', Spark SQL does not generate the expected results.
SELECT avg(CAST(x AS DOUBLE)), var_pop(CAST(x AS DOUBLE))
FROM (VALUES ('1'), (CAST('infinity' AS DOUBLE))) v(x);
SELECT avg(CAST(x AS DOUBLE)), var_pop(CAST(x AS DOUBLE))
FROM (VALUES ('infinity'), ('1')) v(x);
SELECT avg(CAST(x AS DOUBLE)), var_pop(CAST(x AS DOUBLE))
FROM (VALUES ('infinity'), ('infinity')) v(x);
SELECT avg(CAST(x AS DOUBLE)), var_pop(CAST(x AS DOUBLE))
FROM (VALUES ('-infinity'), ('infinity')) v(x);
The root cause: Spark SQL does not recognize the special constants in a case insensitive way. In PostgreSQL, they are recognized in a case insensitive way.
Link: https://www.postgresql.org/docs/9.3/datatype-numeric.html
```
In this PR, the casting code is enhanced to handle these `special` string literals in case insensitive manner.
## How was this patch tested?
Added tests in CastSuite and modified existing test suites.
Closes#25331 from dilipbiswal/double_infinity.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Changed type of `sec` argument in the `make_timestamp()` function from `DOUBLE` to `DECIMAL(8, 6)`. The scale is set to 6 to cover microsecond fractions, and the precision is 2 digits for seconds + 6 digits for microsecond fraction. New type prevents losing precision in some cases, for example:
Before:
```sql
spark-sql> select make_timestamp(2019, 8, 12, 0, 0, 58.000001);
2019-08-12 00:00:58
```
After:
```sql
spark-sql> select make_timestamp(2019, 8, 12, 0, 0, 58.000001);
2019-08-12 00:00:58.000001
```
Also switching to `DECIMAL` fixes rounding `sec` towards "nearest neighbor" unless both neighbors are equidistant, in which case round up. For example:
Before:
```sql
spark-sql> select make_timestamp(2019, 8, 12, 0, 0, 0.1234567);
2019-08-12 00:00:00.123456
```
After:
```sql
spark-sql> select make_timestamp(2019, 8, 12, 0, 0, 0.1234567);
2019-08-12 00:00:00.123457
```
## How was this patch tested?
This was tested by `DateExpressionsSuite` and `pgSQL/timestamp.sql`.
Closes#25421 from MaxGekk/make_timestamp-decimal.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In the PR, I propose additional synonyms for the `field` argument of `extract` supported by PostgreSQL. The `extract.sql` is updated to check all supported values of the `field` argument. The list of synonyms was taken from https://github.com/postgres/postgres/blob/master/src/backend/utils/adt/datetime.c .
## How was this patch tested?
By running `extract.sql` via:
```
$ build/sbt "sql/test-only *SQLQueryTestSuite -- -z extract.sql"
```
Closes#25438 from MaxGekk/extract-field-synonyms.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
A GROUPED_AGG pandas python udf can't work, if without group by clause, like `select udf(id) from table`.
This doesn't match with aggregate function like sum, count..., and also dataset API like `df.agg(udf(df['id']))`.
When we parse a udf (or an aggregate function) like that from SQL syntax, it is known as a function in a project. `GlobalAggregates` rule in analysis makes such project as aggregate, by looking for aggregate expressions. At the moment, we should also look for GROUPED_AGG pandas python udf.
## How was this patch tested?
Added tests.
Closes#25352 from viirya/SPARK-28422.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
It throws `Table or view not found` when showing temporary views:
```sql
spark-sql> CREATE TEMPORARY VIEW temp_view AS SELECT 1 AS a;
spark-sql> show create table temp_view;
Error in query: Table or view 'temp_view' not found in database 'default';
```
It's not easy to support temporary views. This pr changed it to throws `SHOW CREATE TABLE is not supported on a temporary view`:
```sql
spark-sql> CREATE TEMPORARY VIEW temp_view AS SELECT 1 AS a;
spark-sql> show create table temp_view;
Error in query: SHOW CREATE TABLE is not supported on a temporary view: temp_view;
```
## How was this patch tested?
unit tests
Closes#25149 from wangyum/SPARK-28383.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR is a followup of a fix as described in here: https://github.com/apache/spark/pull/25215#issuecomment-517659981
<details><summary>Diff comparing to 'group-by.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/udf/udf-group-by.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-group-by.sql.out
index 3a5df254f2..febe47b5ba 100644
--- a/sql/core/src/test/resources/sql-tests/results/udf/udf-group-by.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-group-by.sql.out
-13,26 +13,26 struct<>
-- !query 1
-SELECT a, COUNT(b) FROM testData
+SELECT udf(a), udf(COUNT(b)) FROM testData
-- !query 1 schema
struct<>
-- !query 1 output
org.apache.spark.sql.AnalysisException
-grouping expressions sequence is empty, and 'testdata.`a`' is not an aggregate function. Wrap '(count(testdata.`b`) AS `count(b)`)' in windowing function(s) or wrap 'testdata.`a`' in first() (or first_value) if you don't care which value you get.;
+grouping expressions sequence is empty, and 'testdata.`a`' is not an aggregate function. Wrap '(CAST(udf(cast(count(b) as string)) AS BIGINT) AS `CAST(udf(cast(count(b) as string)) AS BIGINT)`)' in windowing function(s) or wrap 'testdata.`a`' in first() (or first_value) if you don't care which value you get.;
-- !query 2
-SELECT COUNT(a), COUNT(b) FROM testData
+SELECT COUNT(udf(a)), udf(COUNT(b)) FROM testData
-- !query 2 schema
-struct<count(a):bigint,count(b):bigint>
+struct<count(CAST(udf(cast(a as string)) AS INT)):bigint,CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
-- !query 2 output
7 7
-- !query 3
-SELECT a, COUNT(b) FROM testData GROUP BY a
+SELECT udf(a), COUNT(udf(b)) FROM testData GROUP BY a
-- !query 3 schema
-struct<a:int,count(b):bigint>
+struct<CAST(udf(cast(a as string)) AS INT):int,count(CAST(udf(cast(b as string)) AS INT)):bigint>
-- !query 3 output
1 2
2 2
-41,7 +41,7 NULL 1
-- !query 4
-SELECT a, COUNT(b) FROM testData GROUP BY b
+SELECT udf(a), udf(COUNT(udf(b))) FROM testData GROUP BY b
-- !query 4 schema
struct<>
-- !query 4 output
-50,9 +50,9 expression 'testdata.`a`' is neither present in the group by, nor is it an aggre
-- !query 5
-SELECT COUNT(a), COUNT(b) FROM testData GROUP BY a
+SELECT COUNT(udf(a)), COUNT(udf(b)) FROM testData GROUP BY udf(a)
-- !query 5 schema
-struct<count(a):bigint,count(b):bigint>
+struct<count(CAST(udf(cast(a as string)) AS INT)):bigint,count(CAST(udf(cast(b as string)) AS INT)):bigint>
-- !query 5 output
0 1
2 2
-61,15 +61,15 struct<count(a):bigint,count(b):bigint>
-- !query 6
-SELECT 'foo', COUNT(a) FROM testData GROUP BY 1
+SELECT 'foo', COUNT(udf(a)) FROM testData GROUP BY 1
-- !query 6 schema
-struct<foo:string,count(a):bigint>
+struct<foo:string,count(CAST(udf(cast(a as string)) AS INT)):bigint>
-- !query 6 output
foo 7
-- !query 7
-SELECT 'foo' FROM testData WHERE a = 0 GROUP BY 1
+SELECT 'foo' FROM testData WHERE a = 0 GROUP BY udf(1)
-- !query 7 schema
struct<foo:string>
-- !query 7 output
-77,25 +77,25 struct<foo:string>
-- !query 8
-SELECT 'foo', APPROX_COUNT_DISTINCT(a) FROM testData WHERE a = 0 GROUP BY 1
+SELECT 'foo', udf(APPROX_COUNT_DISTINCT(udf(a))) FROM testData WHERE a = 0 GROUP BY udf(1)
-- !query 8 schema
-struct<foo:string,approx_count_distinct(a):bigint>
+struct<foo:string,CAST(udf(cast(approx_count_distinct(cast(udf(cast(a as string)) as int), 0.05, 0, 0) as string)) AS BIGINT):bigint>
-- !query 8 output
-- !query 9
-SELECT 'foo', MAX(STRUCT(a)) FROM testData WHERE a = 0 GROUP BY 1
+SELECT 'foo', MAX(STRUCT(udf(a))) FROM testData WHERE a = 0 GROUP BY udf(1)
-- !query 9 schema
-struct<foo:string,max(named_struct(a, a)):struct<a:int>>
+struct<foo:string,max(named_struct(col1, CAST(udf(cast(a as string)) AS INT))):struct<col1:int>>
-- !query 9 output
-- !query 10
-SELECT a + b, COUNT(b) FROM testData GROUP BY a + b
+SELECT udf(a + b), udf(COUNT(b)) FROM testData GROUP BY a + b
-- !query 10 schema
-struct<(a + b):int,count(b):bigint>
+struct<CAST(udf(cast((a + b) as string)) AS INT):int,CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
-- !query 10 output
2 1
3 2
-105,7 +105,7 NULL 1
-- !query 11
-SELECT a + 2, COUNT(b) FROM testData GROUP BY a + 1
+SELECT udf(a + 2), udf(COUNT(b)) FROM testData GROUP BY a + 1
-- !query 11 schema
struct<>
-- !query 11 output
-114,9 +114,9 expression 'testdata.`a`' is neither present in the group by, nor is it an aggre
-- !query 12
-SELECT a + 1 + 1, COUNT(b) FROM testData GROUP BY a + 1
+SELECT udf(a + 1) + 1, udf(COUNT(b)) FROM testData GROUP BY udf(a + 1)
-- !query 12 schema
-struct<((a + 1) + 1):int,count(b):bigint>
+struct<(CAST(udf(cast((a + 1) as string)) AS INT) + 1):int,CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
-- !query 12 output
3 2
4 2
-125,26 +125,26 NULL 1
-- !query 13
-SELECT SKEWNESS(a), KURTOSIS(a), MIN(a), MAX(a), AVG(a), VARIANCE(a), STDDEV(a), SUM(a), COUNT(a)
+SELECT SKEWNESS(udf(a)), udf(KURTOSIS(a)), udf(MIN(a)), MAX(udf(a)), udf(AVG(udf(a))), udf(VARIANCE(a)), STDDEV(udf(a)), udf(SUM(a)), udf(COUNT(a))
FROM testData
-- !query 13 schema
-struct<skewness(CAST(a AS DOUBLE)):double,kurtosis(CAST(a AS DOUBLE)):double,min(a):int,max(a):int,avg(a):double,var_samp(CAST(a AS DOUBLE)):double,stddev_samp(CAST(a AS DOUBLE)):double,sum(a):bigint,count(a):bigint>
+struct<skewness(CAST(CAST(udf(cast(a as string)) AS INT) AS DOUBLE)):double,CAST(udf(cast(kurtosis(cast(a as double)) as string)) AS DOUBLE):double,CAST(udf(cast(min(a) as string)) AS INT):int,max(CAST(udf(cast(a as string)) AS INT)):int,CAST(udf(cast(avg(cast(cast(udf(cast(a as string)) as int) as bigint)) as string)) AS DOUBLE):double,CAST(udf(cast(var_samp(cast(a as double)) as string)) AS DOUBLE):double,stddev_samp(CAST(CAST(udf(cast(a as string)) AS INT) AS DOUBLE)):double,CAST(udf(cast(sum(cast(a as bigint)) as string)) AS BIGINT):bigint,CAST(udf(cast(count(a) as string)) AS BIGINT):bigint>
-- !query 13 output
-0.2723801058145729 -1.5069204152249134 1 3 2.142857142857143 0.8095238095238094 0.8997354108424372 15 7
-- !query 14
-SELECT COUNT(DISTINCT b), COUNT(DISTINCT b, c) FROM (SELECT 1 AS a, 2 AS b, 3 AS c) GROUP BY a
+SELECT COUNT(DISTINCT udf(b)), udf(COUNT(DISTINCT b, c)) FROM (SELECT 1 AS a, 2 AS b, 3 AS c) GROUP BY udf(a)
-- !query 14 schema
-struct<count(DISTINCT b):bigint,count(DISTINCT b, c):bigint>
+struct<count(DISTINCT CAST(udf(cast(b as string)) AS INT)):bigint,CAST(udf(cast(count(distinct b, c) as string)) AS BIGINT):bigint>
-- !query 14 output
1 1
-- !query 15
-SELECT a AS k, COUNT(b) FROM testData GROUP BY k
+SELECT udf(a) AS k, COUNT(udf(b)) FROM testData GROUP BY k
-- !query 15 schema
-struct<k:int,count(b):bigint>
+struct<k:int,count(CAST(udf(cast(b as string)) AS INT)):bigint>
-- !query 15 output
1 2
2 2
-153,21 +153,21 NULL 1
-- !query 16
-SELECT a AS k, COUNT(b) FROM testData GROUP BY k HAVING k > 1
+SELECT a AS k, udf(COUNT(b)) FROM testData GROUP BY k HAVING k > 1
-- !query 16 schema
-struct<k:int,count(b):bigint>
+struct<k:int,CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
-- !query 16 output
2 2
3 2
-- !query 17
-SELECT COUNT(b) AS k FROM testData GROUP BY k
+SELECT udf(COUNT(b)) AS k FROM testData GROUP BY k
-- !query 17 schema
struct<>
-- !query 17 output
org.apache.spark.sql.AnalysisException
-aggregate functions are not allowed in GROUP BY, but found count(testdata.`b`);
+aggregate functions are not allowed in GROUP BY, but found CAST(udf(cast(count(b) as string)) AS BIGINT);
-- !query 18
-180,7 +180,7 struct<>
-- !query 19
-SELECT k AS a, COUNT(v) FROM testDataHasSameNameWithAlias GROUP BY a
+SELECT k AS a, udf(COUNT(udf(v))) FROM testDataHasSameNameWithAlias GROUP BY udf(a)
-- !query 19 schema
struct<>
-- !query 19 output
-197,32 +197,32 spark.sql.groupByAliases false
-- !query 21
-SELECT a AS k, COUNT(b) FROM testData GROUP BY k
+SELECT a AS k, udf(COUNT(udf(b))) FROM testData GROUP BY k
-- !query 21 schema
struct<>
-- !query 21 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`k`' given input columns: [testdata.a, testdata.b]; line 1 pos 47
+cannot resolve '`k`' given input columns: [testdata.a, testdata.b]; line 1 pos 57
-- !query 22
-SELECT a, COUNT(1) FROM testData WHERE false GROUP BY a
+SELECT udf(a), COUNT(udf(1)) FROM testData WHERE false GROUP BY udf(a)
-- !query 22 schema
-struct<a:int,count(1):bigint>
+struct<CAST(udf(cast(a as string)) AS INT):int,count(CAST(udf(cast(1 as string)) AS INT)):bigint>
-- !query 22 output
-- !query 23
-SELECT COUNT(1) FROM testData WHERE false
+SELECT udf(COUNT(1)) FROM testData WHERE false
-- !query 23 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 23 output
0
-- !query 24
-SELECT 1 FROM (SELECT COUNT(1) FROM testData WHERE false) t
+SELECT 1 FROM (SELECT udf(COUNT(1)) FROM testData WHERE false) t
-- !query 24 schema
struct<1:int>
-- !query 24 output
-232,7 +232,7 struct<1:int>
-- !query 25
SELECT 1 from (
SELECT 1 AS z,
- MIN(a.x)
+ udf(MIN(a.x))
FROM (select 1 as x) a
WHERE false
) b
-244,32 +244,32 struct<1:int>
-- !query 26
-SELECT corr(DISTINCT x, y), corr(DISTINCT y, x), count(*)
+SELECT corr(DISTINCT x, y), udf(corr(DISTINCT y, x)), count(*)
FROM (VALUES (1, 1), (2, 2), (2, 2)) t(x, y)
-- !query 26 schema
-struct<corr(DISTINCT CAST(x AS DOUBLE), CAST(y AS DOUBLE)):double,corr(DISTINCT CAST(y AS DOUBLE), CAST(x AS DOUBLE)):double,count(1):bigint>
+struct<corr(DISTINCT CAST(x AS DOUBLE), CAST(y AS DOUBLE)):double,CAST(udf(cast(corr(distinct cast(y as double), cast(x as double)) as string)) AS DOUBLE):double,count(1):bigint>
-- !query 26 output
1.0 1.0 3
-- !query 27
-SELECT 1 FROM range(10) HAVING true
+SELECT udf(1) FROM range(10) HAVING true
-- !query 27 schema
-struct<1:int>
+struct<CAST(udf(cast(1 as string)) AS INT):int>
-- !query 27 output
1
-- !query 28
-SELECT 1 FROM range(10) HAVING MAX(id) > 0
+SELECT udf(udf(1)) FROM range(10) HAVING MAX(id) > 0
-- !query 28 schema
-struct<1:int>
+struct<CAST(udf(cast(cast(udf(cast(1 as string)) as int) as string)) AS INT):int>
-- !query 28 output
1
-- !query 29
-SELECT id FROM range(10) HAVING id > 0
+SELECT udf(id) FROM range(10) HAVING id > 0
-- !query 29 schema
struct<>
-- !query 29 output
-291,33 +291,33 struct<>
-- !query 31
-SELECT every(v), some(v), any(v) FROM test_agg WHERE 1 = 0
+SELECT udf(every(v)), udf(some(v)), any(v) FROM test_agg WHERE 1 = 0
-- !query 31 schema
-struct<every(v):boolean,some(v):boolean,any(v):boolean>
+struct<CAST(udf(cast(every(v) as string)) AS BOOLEAN):boolean,CAST(udf(cast(some(v) as string)) AS BOOLEAN):boolean,any(v):boolean>
-- !query 31 output
NULL NULL NULL
-- !query 32
-SELECT every(v), some(v), any(v) FROM test_agg WHERE k = 4
+SELECT udf(every(udf(v))), some(v), any(v) FROM test_agg WHERE k = 4
-- !query 32 schema
-struct<every(v):boolean,some(v):boolean,any(v):boolean>
+struct<CAST(udf(cast(every(cast(udf(cast(v as string)) as boolean)) as string)) AS BOOLEAN):boolean,some(v):boolean,any(v):boolean>
-- !query 32 output
NULL NULL NULL
-- !query 33
-SELECT every(v), some(v), any(v) FROM test_agg WHERE k = 5
+SELECT every(v), udf(some(v)), any(v) FROM test_agg WHERE k = 5
-- !query 33 schema
-struct<every(v):boolean,some(v):boolean,any(v):boolean>
+struct<every(v):boolean,CAST(udf(cast(some(v) as string)) AS BOOLEAN):boolean,any(v):boolean>
-- !query 33 output
false true true
-- !query 34
-SELECT k, every(v), some(v), any(v) FROM test_agg GROUP BY k
+SELECT udf(k), every(v), udf(some(v)), any(v) FROM test_agg GROUP BY udf(k)
-- !query 34 schema
-struct<k:int,every(v):boolean,some(v):boolean,any(v):boolean>
+struct<CAST(udf(cast(k as string)) AS INT):int,every(v):boolean,CAST(udf(cast(some(v) as string)) AS BOOLEAN):boolean,any(v):boolean>
-- !query 34 output
1 false true true
2 true true true
-327,9 +327,9 struct<k:int,every(v):boolean,some(v):boolean,any(v):boolean>
-- !query 35
-SELECT k, every(v) FROM test_agg GROUP BY k HAVING every(v) = false
+SELECT udf(k), every(v) FROM test_agg GROUP BY k HAVING every(v) = false
-- !query 35 schema
-struct<k:int,every(v):boolean>
+struct<CAST(udf(cast(k as string)) AS INT):int,every(v):boolean>
-- !query 35 output
1 false
3 false
-337,77 +337,77 struct<k:int,every(v):boolean>
-- !query 36
-SELECT k, every(v) FROM test_agg GROUP BY k HAVING every(v) IS NULL
+SELECT udf(k), udf(every(v)) FROM test_agg GROUP BY udf(k) HAVING every(v) IS NULL
-- !query 36 schema
-struct<k:int,every(v):boolean>
+struct<CAST(udf(cast(k as string)) AS INT):int,CAST(udf(cast(every(v) as string)) AS BOOLEAN):boolean>
-- !query 36 output
4 NULL
-- !query 37
-SELECT k,
- Every(v) AS every
+SELECT udf(k),
+ udf(Every(v)) AS every
FROM test_agg
WHERE k = 2
AND v IN (SELECT Any(v)
FROM test_agg
WHERE k = 1)
-GROUP BY k
+GROUP BY udf(k)
-- !query 37 schema
-struct<k:int,every:boolean>
+struct<CAST(udf(cast(k as string)) AS INT):int,every:boolean>
-- !query 37 output
2 true
-- !query 38
-SELECT k,
+SELECT udf(udf(k)),
Every(v) AS every
FROM test_agg
WHERE k = 2
AND v IN (SELECT Every(v)
FROM test_agg
WHERE k = 1)
-GROUP BY k
+GROUP BY udf(udf(k))
-- !query 38 schema
-struct<k:int,every:boolean>
+struct<CAST(udf(cast(cast(udf(cast(k as string)) as int) as string)) AS INT):int,every:boolean>
-- !query 38 output
-- !query 39
-SELECT every(1)
+SELECT every(udf(1))
-- !query 39 schema
struct<>
-- !query 39 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'every(1)' due to data type mismatch: Input to function 'every' should have been boolean, but it's [int].; line 1 pos 7
+cannot resolve 'every(CAST(udf(cast(1 as string)) AS INT))' due to data type mismatch: Input to function 'every' should have been boolean, but it's [int].; line 1 pos 7
-- !query 40
-SELECT some(1S)
+SELECT some(udf(1S))
-- !query 40 schema
struct<>
-- !query 40 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'some(1S)' due to data type mismatch: Input to function 'some' should have been boolean, but it's [smallint].; line 1 pos 7
+cannot resolve 'some(CAST(udf(cast(1 as string)) AS SMALLINT))' due to data type mismatch: Input to function 'some' should have been boolean, but it's [smallint].; line 1 pos 7
-- !query 41
-SELECT any(1L)
+SELECT any(udf(1L))
-- !query 41 schema
struct<>
-- !query 41 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'any(1L)' due to data type mismatch: Input to function 'any' should have been boolean, but it's [bigint].; line 1 pos 7
+cannot resolve 'any(CAST(udf(cast(1 as string)) AS BIGINT))' due to data type mismatch: Input to function 'any' should have been boolean, but it's [bigint].; line 1 pos 7
-- !query 42
-SELECT every("true")
+SELECT udf(every("true"))
-- !query 42 schema
struct<>
-- !query 42 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'every('true')' due to data type mismatch: Input to function 'every' should have been boolean, but it's [string].; line 1 pos 7
+cannot resolve 'every('true')' due to data type mismatch: Input to function 'every' should have been boolean, but it's [string].; line 1 pos 11
-- !query 43
-428,9 +428,9 struct<k:int,v:boolean,every(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST
-- !query 44
-SELECT k, v, some(v) OVER (PARTITION BY k ORDER BY v) FROM test_agg
+SELECT k, udf(udf(v)), some(v) OVER (PARTITION BY k ORDER BY v) FROM test_agg
-- !query 44 schema
-struct<k:int,v:boolean,some(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):boolean>
+struct<k:int,CAST(udf(cast(cast(udf(cast(v as string)) as boolean) as string)) AS BOOLEAN):boolean,some(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):boolean>
-- !query 44 output
1 false false
1 true true
-445,9 +445,9 struct<k:int,v:boolean,some(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST R
-- !query 45
-SELECT k, v, any(v) OVER (PARTITION BY k ORDER BY v) FROM test_agg
+SELECT udf(udf(k)), v, any(v) OVER (PARTITION BY k ORDER BY v) FROM test_agg
-- !query 45 schema
-struct<k:int,v:boolean,any(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):boolean>
+struct<CAST(udf(cast(cast(udf(cast(k as string)) as int) as string)) AS INT):int,v:boolean,any(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):boolean>
-- !query 45 output
1 false false
1 true true
-462,17 +462,17 struct<k:int,v:boolean,any(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST RA
-- !query 46
-SELECT count(*) FROM test_agg HAVING count(*) > 1L
+SELECT udf(count(*)) FROM test_agg HAVING count(*) > 1L
-- !query 46 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 46 output
10
-- !query 47
-SELECT k, max(v) FROM test_agg GROUP BY k HAVING max(v) = true
+SELECT k, udf(max(v)) FROM test_agg GROUP BY k HAVING max(v) = true
-- !query 47 schema
-struct<k:int,max(v):boolean>
+struct<k:int,CAST(udf(cast(max(v) as string)) AS BOOLEAN):boolean>
-- !query 47 output
1 true
2 true
-480,7 +480,7 struct<k:int,max(v):boolean>
-- !query 48
-SELECT * FROM (SELECT COUNT(*) AS cnt FROM test_agg) WHERE cnt > 1L
+SELECT * FROM (SELECT udf(COUNT(*)) AS cnt FROM test_agg) WHERE cnt > 1L
-- !query 48 schema
struct<cnt:bigint>
-- !query 48 output
-488,7 +488,7 struct<cnt:bigint>
-- !query 49
-SELECT count(*) FROM test_agg WHERE count(*) > 1L
+SELECT udf(count(*)) FROM test_agg WHERE count(*) > 1L
-- !query 49 schema
struct<>
-- !query 49 output
-500,7 +500,7 Invalid expressions: [count(1)];
-- !query 50
-SELECT count(*) FROM test_agg WHERE count(*) + 1L > 1L
+SELECT udf(count(*)) FROM test_agg WHERE count(*) + 1L > 1L
-- !query 50 schema
struct<>
-- !query 50 output
-512,7 +512,7 Invalid expressions: [count(1)];
-- !query 51
-SELECT count(*) FROM test_agg WHERE k = 1 or k = 2 or count(*) + 1L > 1L or max(k) > 1
+SELECT udf(count(*)) FROM test_agg WHERE k = 1 or k = 2 or count(*) + 1L > 1L or max(k) > 1
-- !query 51 schema
struct<>
-- !query 51 output
```
</p>
</details>
## How was this patch tested?
Tested as instructed in SPARK-27921.
Closes#25360 from skonto/group-by-followup.
Authored-by: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Restored comments in `date.sql` removed by 924d794a6f and 997d153e54 . The comments was introduced by 51379b731d .
## How was this patch tested?
By re-running `date.sql` via:
```shell
$ build/sbt "sql/test-only *SQLQueryTestSuite -- -z date.sql"
```
Closes#25422 from MaxGekk/sql-comments-followup.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR replaces `CatalogUtils.maskCredentials` with `SQLConf.get.redactOptions` to match other redacts.
## How was this patch tested?
unit test and manual tests:
Before this PR:
```sql
spark-sql> DESC EXTENDED test_spark_28675;
id int NULL
# Detailed Table Information
Database default
Table test_spark_28675
Owner root
Created Time Fri Aug 09 08:23:17 GMT-07:00 2019
Last Access Wed Dec 31 17:00:00 GMT-07:00 1969
Created By Spark 3.0.0-SNAPSHOT
Type MANAGED
Provider org.apache.spark.sql.jdbc
Location file:/user/hive/warehouse/test_spark_28675
Serde Library org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat org.apache.hadoop.mapred.SequenceFileInputFormat
OutputFormat org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
Storage Properties [url=###, driver=com.mysql.jdbc.Driver, dbtable=test_spark_28675]
spark-sql> SHOW TABLE EXTENDED LIKE 'test_spark_28675';
default test_spark_28675 false Database: default
Table: test_spark_28675
Owner: root
Created Time: Fri Aug 09 08:23:17 GMT-07:00 2019
Last Access: Wed Dec 31 17:00:00 GMT-07:00 1969
Created By: Spark 3.0.0-SNAPSHOT
Type: MANAGED
Provider: org.apache.spark.sql.jdbc
Location: file:/user/hive/warehouse/test_spark_28675
Serde Library: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat: org.apache.hadoop.mapred.SequenceFileInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
Storage Properties: [url=###, driver=com.mysql.jdbc.Driver, dbtable=test_spark_28675]
Schema: root
|-- id: integer (nullable = true)
```
After this PR:
```sql
spark-sql> DESC EXTENDED test_spark_28675;
id int NULL
# Detailed Table Information
Database default
Table test_spark_28675
Owner root
Created Time Fri Aug 09 08:19:49 GMT-07:00 2019
Last Access Wed Dec 31 17:00:00 GMT-07:00 1969
Created By Spark 3.0.0-SNAPSHOT
Type MANAGED
Provider org.apache.spark.sql.jdbc
Location file:/user/hive/warehouse/test_spark_28675
Serde Library org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat org.apache.hadoop.mapred.SequenceFileInputFormat
OutputFormat org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
Storage Properties [url=*********(redacted), driver=com.mysql.jdbc.Driver, dbtable=test_spark_28675]
spark-sql> SHOW TABLE EXTENDED LIKE 'test_spark_28675';
default test_spark_28675 false Database: default
Table: test_spark_28675
Owner: root
Created Time: Fri Aug 09 08:19:49 GMT-07:00 2019
Last Access: Wed Dec 31 17:00:00 GMT-07:00 1969
Created By: Spark 3.0.0-SNAPSHOT
Type: MANAGED
Provider: org.apache.spark.sql.jdbc
Location: file:/user/hive/warehouse/test_spark_28675
Serde Library: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat: org.apache.hadoop.mapred.SequenceFileInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
Storage Properties: [url=*********(redacted), driver=com.mysql.jdbc.Driver, dbtable=test_spark_28675]
Schema: root
|-- id: integer (nullable = true)
```
Closes#25395 from wangyum/SPARK-28675.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR fixed typos in comments and replace the explicit type with '<>' for Java 8+.
## How was this patch tested?
Manually tested.
Closes#25338 from younggyuchun/younggyu.
Authored-by: younggyu chun <younggyuchun@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose new expressions `Millennium`, `Century` and `Decade`, and support additional parameters of `extract()` for feature parity with PostgreSQL (https://www.postgresql.org/docs/11/functions-datetime.html#FUNCTIONS-DATETIME-EXTRACT):
1. `millennium` - the current millennium for given date (or a timestamp implicitly casted to a date). For example, years in the 1900s are in the second millennium. The third millennium started _January 1, 2001_.
2. `century` - the current millennium for given date (or timestamp). The first century starts at 0001-01-01 AD.
3. `decade` - the current decade for given date (or timestamp). Actually, this is the year field divided by 10.
Here are examples:
```sql
spark-sql> SELECT EXTRACT(MILLENNIUM FROM DATE '1981-01-19');
2
spark-sql> SELECT EXTRACT(CENTURY FROM DATE '1981-01-19');
20
spark-sql> SELECT EXTRACT(DECADE FROM DATE '1981-01-19');
198
```
## How was this patch tested?
Added new tests to `DateExpressionsSuite` and uncommented existing tests in `pgSQL/date.sql`.
Closes#25388 from MaxGekk/extract-ext2.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR is a follow-up to https://github.com/apache/spark/pull/24918
## How was this patch tested?
Pass the Jenkins with the newly update test files.
Closes#25393 from beliefer/enable-overlay-tests.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
Right now, batch DataFrame always changes the schema to nullable automatically (See this line: 325bc8e9c6/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala (L399)). But streaming file source is missing this.
This PR updates the streaming file source schema to force it be nullable. I also added a flag `spark.sql.streaming.fileSource.schema.forceNullable` to disable this change since some users may rely on the old behavior.
## How was this patch tested?
The new unit test.
Closes#25382 from zsxwing/SPARK-28651.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Adds support for V2 catalogs and the V2SessionCatalog for V2 tables for saveAsTable.
If the table can resolve through the V2SessionCatalog, we use SaveMode for datasource v1 for backwards compatibility to select the code path we're going to hit.
Depending on the SaveMode:
- SaveMode.Append:
a) If table exists: Use AppendData.byName
b) If table doesn't exist, use CTAS (ignoreIfExists = false)
- SaveMode.Overwrite: Use RTAS (orCreate = true)
- SaveMode.Ignore: Use CTAS (ignoreIfExists = true)
- SaveMode.ErrorIfExists: Use CTAS (ignoreIfExists = false)
## How was this patch tested?
Unit tests in DataSourceV2DataFrameSuite
Closes#25330 from brkyvz/saveAsTable.
Lead-authored-by: Burak Yavuz <brkyvz@gmail.com>
Co-authored-by: Burak Yavuz <burak@databricks.com>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
## What changes were proposed in this pull request?
I propose new levels of truncations for the `date_trunc()` and `trunc()` functions:
1. `MICROSECOND` and `MILLISECOND` truncate values of the `TIMESTAMP` type to microsecond and millisecond precision.
2. `DECADE`, `CENTURY` and `MILLENNIUM` truncate dates/timestamps to lowest date of current decade/century/millennium.
Also the `WEEK` and `QUARTER` levels have been supported by the `trunc()` function.
The function is implemented similarly to `date_trunc` in PostgreSQL: https://www.postgresql.org/docs/11/functions-datetime.html#FUNCTIONS-DATETIME-TRUNC to maintain feature parity with it.
Here are examples of `TRUNC`:
```sql
spark-sql> SELECT TRUNC('2015-10-27', 'DECADE');
2010-01-01
spark-sql> set spark.sql.datetime.java8API.enabled=true;
spark.sql.datetime.java8API.enabled true
spark-sql> SELECT TRUNC('1999-10-27', 'millennium');
1001-01-01
```
Examples of `DATE_TRUNC`:
```sql
spark-sql> SELECT DATE_TRUNC('CENTURY', '2015-03-05T09:32:05.123456');
2001-01-01T00:00:00Z
```
## How was this patch tested?
Added new tests to `DateTimeUtilsSuite`, `DateExpressionsSuite` and `DateFunctionsSuite`, and uncommented existing tests in `pgSQL/date.sql`.
Closes#25336 from MaxGekk/date_truct-ext.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Adds checks around:
- The existence of transforms in the table schema (even in nested fields)
- Duplications of transforms
- Case sensitivity checks around column names
in the V2 table creation code paths.
## How was this patch tested?
Unit tests.
Closes#25305 from brkyvz/v2CreateTable.
Authored-by: Burak Yavuz <brkyvz@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Plans after "Extract Python UDFs" are very flaky and error-prone to other rules.
For instance, if we add some rules, for instance, `PushDownPredicates` in `postHocOptimizationBatches`, the test in `BatchEvalPythonExecSuite` fails:
```scala
test("Python UDF refers to the attributes from more than one child") {
val df = Seq(("Hello", 4)).toDF("a", "b")
val df2 = Seq(("Hello", 4)).toDF("c", "d")
val joinDF = df.crossJoin(df2).where("dummyPythonUDF(a, c) == dummyPythonUDF(d, c)")
val qualifiedPlanNodes = joinDF.queryExecution.executedPlan.collect {
case b: BatchEvalPythonExec => b
}
assert(qualifiedPlanNodes.size == 1)
}
```
```
Invalid PythonUDF dummyUDF(a#63, c#74), requires attributes from more than one child.
```
This is because Python UDF extraction optimization is rolled back as below:
```
=== Applying Rule org.apache.spark.sql.catalyst.optimizer.PushDownPredicates ===
!Filter (dummyUDF(a#7, c#18) = dummyUDF(d#19, c#18)) Join Cross, (dummyUDF(a#7, c#18) = dummyUDF(d#19, c#18))
!+- Join Cross :- Project [_1#2 AS a#7, _2#3 AS b#8]
! :- Project [_1#2 AS a#7, _2#3 AS b#8] : +- LocalRelation [_1#2, _2#3]
! : +- LocalRelation [_1#2, _2#3] +- Project [_1#13 AS c#18, _2#14 AS d#19]
! +- Project [_1#13 AS c#18, _2#14 AS d#19] +- LocalRelation [_1#13, _2#14]
! +- LocalRelation [_1#13, _2#14]
```
Seems we should do Python UDFs cases at the last even after post hoc rules.
Note that this actually rather follows the way in previous versions when those were in physical plans (see SPARK-24721 and SPARK-12981). Those optimization rules were supposed to be placed at the end.
Note that I intentionally didn't move `ExperimentalMethods` (`spark.experimental.extraStrategies`). This is an explicit experimental API and I wanted to just-in-case workaround after this change for now.
## How was this patch tested?
Existing tests should cover.
Closes#25386 from HyukjinKwon/SPARK-28654.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Fix the typo in java doc.
## How was this patch tested?
N/A
Signed-off-by: Yishuang Lu <luystugmail.com>
Closes#25377 from lys0716/dev.
Authored-by: Yishuang Lu <luystu@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Subqueries do not have their own execution id, thus when calling `AdaptiveSparkPlanExec.onUpdatePlan`, it will actually get the `QueryExecution` instance of the main query, which is wasteful and problematic. It could cause issues like stack overflow or dead locks in some circumstances.
This PR fixes this issue by making `AdaptiveSparkPlanExec` compare the `QueryExecution` object retrieved by current execution ID against the `QueryExecution` object from which this plan is created, and only update the UI when the two instances are the same.
## How was this patch tested?
Manual tests on TPC-DS queries.
Closes#25316 from maryannxue/aqe-updateplan-fix.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: herman <herman@databricks.com>
## What changes were proposed in this pull request?
Sometimes when you explain a query, you will get stuck for a while. What's worse, you will get stuck again if you explain again.
This is caused by `FileSourceScanExec`:
1. In its `toString`, it needs to report the number of partitions it reads. This needs to query the hive metastore.
2. In its `outputOrdering`, it needs to get all the files. This needs to query the hive metastore.
This PR fixes by:
1. `toString` do not need to report the number of partitions it reads. We should report it via SQL metrics.
2. The `outputOrdering` is not very useful. We can only apply it if a) all the bucket columns are read. b) there is only one file in each bucket. This condition is really hard to meet, and even if we meet, sorting an already sorted file is pretty fast and avoiding the sort is not that useful. I think it's worth to give up this optimization so that explain don't need to get stuck.
## How was this patch tested?
existing tests
Closes#25328 from cloud-fan/ui.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR is a follow-up to https://github.com/apache/spark/pull/25074
## How was this patch tested?
Pass the Jenkins with the newly update test files.
Closes#25366 from beliefer/uncomment-boolean-test.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Implements the `DESCRIBE TABLE` logical and physical plans for data source v2 tables.
## How was this patch tested?
Added unit tests to `DataSourceV2SQLSuite`.
Closes#25040 from mccheah/describe-table-v2.
Authored-by: mcheah <mcheah@palantir.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add a guide line for dataframe functions, say:
```
This function APIs usually have methods with Column signature only because it can support not only Column but also other types such as a native string. The other variants currently exist for historical reasons.
```
## How was this patch tested?
N/A
Closes#25355 from WeichenXu123/update_functions_guide2.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add's the higher order function `forall`, which tests an array to see if a predicate holds for every element.
The function is implemented in `org.apache.spark.sql.catalyst.expressions.ArrayForAll`.
The function is added to the function registry under the pretty name `forall`.
## How was this patch tested?
I've added appropriate unit tests for the new ArrayForAll expression in
`sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/HigherOrderFunctionsSuite.scala`.
Also added tests for the function in `sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala`.
Not sure who is best to ask about this PR so:
HyukjinKwon rxin gatorsmile ueshin srowen hvanhovell gatorsmile
Closes#24761 from nvander1/feature/for_all.
Lead-authored-by: Nik Vanderhoof <nikolasrvanderhoof@gmail.com>
Co-authored-by: Nik <nikolasrvanderhoof@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to use existing expressions `DayOfYear`, `WeekDay` and `DayOfWeek`, and support additional parameters of `extract()` for feature parity with PostgreSQL (https://www.postgresql.org/docs/11/functions-datetime.html#FUNCTIONS-DATETIME-EXTRACT):
1. `dow` - the day of the week as Sunday (0) to Saturday (6)
2. `isodow` - the day of the week as Monday (1) to Sunday (7)
3. `doy` - the day of the year (1 - 365/366)
Here are examples:
```sql
spark-sql> SELECT EXTRACT(DOW FROM TIMESTAMP '2001-02-16 20:38:40');
5
spark-sql> SELECT EXTRACT(ISODOW FROM TIMESTAMP '2001-02-18 20:38:40');
7
spark-sql> SELECT EXTRACT(DOY FROM TIMESTAMP '2001-02-16 20:38:40');
47
```
## How was this patch tested?
Updated `extract.sql`.
Closes#25367 from MaxGekk/extract-ext.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR targets to rename `PullOutPythonUDFInJoinCondition` to `ExtractPythonUDFFromJoinCondition` and move to 'Extract Python UDFs' together with other Python UDF related rules.
Currently `PullOutPythonUDFInJoinCondition` rule is alone outside of other 'Extract Python UDFs' rules together.
and the name `ExtractPythonUDFFromJoinCondition` is matched to existing Python UDF extraction rules.
## How was this patch tested?
Existing tests should cover.
Closes#25358 from HyukjinKwon/move-python-join-rule.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR adds UDF cases into group by clause in 'pgSQL/select_implicit.sql'
<details><summary>Diff comparing to 'pgSQL/select_implicit.sql'</summary>
<p>
```diff
diff --git a/home/root1/src/spark/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-select_implicit.sql.out b/home/root1/src/spark/sql/core/src/test/resources/sql-tests/results/pgSQL/select_implicit.sql.out
index 17303b2..0675820 100755
--- a/home/root1/src/spark/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-select_implicit.sql.out
+++ b/home/root1/src/spark/sql/core/src/test/resources/sql-tests/results/pgSQL/select_implicit.sql.out
-91,11 +91,9 struct<>
-- !query 11
-SELECT udf(c), udf(count(*)) FROM test_missing_target GROUP BY
-udf(test_missing_target.c)
-ORDER BY udf(c)
+SELECT c, count(*) FROM test_missing_target GROUP BY test_missing_target.c ORDER BY c
-- !query 11 schema
-struct<CAST(udf(cast(c as string)) AS STRING):string,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<c:string,count(1):bigint>
-- !query 11 output
ABAB 2
BBBB 2
-106,10 +104,9 cccc 2
-- !query 12
-SELECT udf(count(*)) FROM test_missing_target GROUP BY udf(test_missing_target.c)
-ORDER BY udf(c)
+SELECT count(*) FROM test_missing_target GROUP BY test_missing_target.c ORDER BY c
-- !query 12 schema
-struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<count(1):bigint>
-- !query 12 output
2
2
-120,18 +117,18 struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 13
-SELECT udf(count(*)) FROM test_missing_target GROUP BY udf(a) ORDER BY udf(b)
+SELECT count(*) FROM test_missing_target GROUP BY a ORDER BY b
-- !query 13 schema
struct<>
-- !query 13 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`b`' given input columns: [CAST(udf(cast(count(1) as string)) AS BIGINT)]; line 1 pos 75
+cannot resolve '`b`' given input columns: [count(1)]; line 1 pos 61
-- !query 14
-SELECT udf(count(*)) FROM test_missing_target GROUP BY udf(b) ORDER BY udf(b)
+SELECT count(*) FROM test_missing_target GROUP BY b ORDER BY b
-- !query 14 schema
-struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<count(1):bigint>
-- !query 14 output
1
2
-140,10 +137,10 struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 15
-SELECT udf(test_missing_target.b), udf(count(*))
- FROM test_missing_target GROUP BY udf(b) ORDER BY udf(b)
+SELECT test_missing_target.b, count(*)
+ FROM test_missing_target GROUP BY b ORDER BY b
-- !query 15 schema
-struct<CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<b:int,count(1):bigint>
-- !query 15 output
1 1
2 2
-152,9 +149,9 struct<CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(count(1) as string)
-- !query 16
-SELECT udf(c) FROM test_missing_target ORDER BY udf(a)
+SELECT c FROM test_missing_target ORDER BY a
-- !query 16 schema
-struct<CAST(udf(cast(c as string)) AS STRING):string>
+struct<c:string>
-- !query 16 output
XXXX
ABAB
-169,10 +166,9 CCCC
-- !query 17
-SELECT udf(count(*)) FROM test_missing_target GROUP BY udf(b) ORDER BY udf(b)
-desc
+SELECT count(*) FROM test_missing_target GROUP BY b ORDER BY b desc
-- !query 17 schema
-struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<count(1):bigint>
-- !query 17 output
4
3
-181,17 +177,17 struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 18
-SELECT udf(count(*)) FROM test_missing_target ORDER BY udf(1) desc
+SELECT count(*) FROM test_missing_target ORDER BY 1 desc
-- !query 18 schema
-struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<count(1):bigint>
-- !query 18 output
10
-- !query 19
-SELECT udf(c), udf(count(*)) FROM test_missing_target GROUP BY 1 ORDER BY 1
+SELECT c, count(*) FROM test_missing_target GROUP BY 1 ORDER BY 1
-- !query 19 schema
-struct<CAST(udf(cast(c as string)) AS STRING):string,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<c:string,count(1):bigint>
-- !query 19 output
ABAB 2
BBBB 2
-202,30 +198,30 cccc 2
-- !query 20
-SELECT udf(c), udf(count(*)) FROM test_missing_target GROUP BY 3
+SELECT c, count(*) FROM test_missing_target GROUP BY 3
-- !query 20 schema
struct<>
-- !query 20 output
org.apache.spark.sql.AnalysisException
-GROUP BY position 3 is not in select list (valid range is [1, 2]); line 1 pos 63
+GROUP BY position 3 is not in select list (valid range is [1, 2]); line 1 pos 53
-- !query 21
-SELECT udf(count(*)) FROM test_missing_target x, test_missing_target y
- WHERE udf(x.a) = udf(y.a)
- GROUP BY udf(b) ORDER BY udf(b)
+SELECT count(*) FROM test_missing_target x, test_missing_target y
+ WHERE x.a = y.a
+ GROUP BY b ORDER BY b
-- !query 21 schema
struct<>
-- !query 21 output
org.apache.spark.sql.AnalysisException
-Reference 'b' is ambiguous, could be: x.b, y.b.; line 3 pos 14
+Reference 'b' is ambiguous, could be: x.b, y.b.; line 3 pos 10
-- !query 22
-SELECT udf(a), udf(a) FROM test_missing_target
- ORDER BY udf(a)
+SELECT a, a FROM test_missing_target
+ ORDER BY a
-- !query 22 schema
-struct<CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(a as string)) AS INT):int>
+struct<a:int,a:int>
-- !query 22 output
0 0
1 1
-240,10 +236,10 struct<CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(a as string)) AS IN
-- !query 23
-SELECT udf(udf(a)/2), udf(udf(a)/2) FROM test_missing_target
- ORDER BY udf(udf(a)/2)
+SELECT a/2, a/2 FROM test_missing_target
+ ORDER BY a/2
-- !query 23 schema
-struct<CAST(udf(cast((cast(udf(cast(a as string)) as int) div 2) as string)) AS INT):int,CAST(udf(cast((cast(udf(cast(a as string)) as int) div 2) as string)) AS INT):int>
+struct<(a div 2):int,(a div 2):int>
-- !query 23 output
0 0
0 0
-258,10 +254,10 struct<CAST(udf(cast((cast(udf(cast(a as string)) as int) div 2) as string)) AS
-- !query 24
-SELECT udf(a/2), udf(a/2) FROM test_missing_target
- GROUP BY udf(a/2) ORDER BY udf(a/2)
+SELECT a/2, a/2 FROM test_missing_target
+ GROUP BY a/2 ORDER BY a/2
-- !query 24 schema
-struct<CAST(udf(cast((a div 2) as string)) AS INT):int,CAST(udf(cast((a div 2) as string)) AS INT):int>
+struct<(a div 2):int,(a div 2):int>
-- !query 24 output
0 0
1 1
-271,11 +267,11 struct<CAST(udf(cast((a div 2) as string)) AS INT):int,CAST(udf(cast((a div 2) a
-- !query 25
-SELECT udf(x.b), udf(count(*)) FROM test_missing_target x, test_missing_target y
- WHERE udf(x.a) = udf(y.a)
- GROUP BY udf(x.b) ORDER BY udf(x.b)
+SELECT x.b, count(*) FROM test_missing_target x, test_missing_target y
+ WHERE x.a = y.a
+ GROUP BY x.b ORDER BY x.b
-- !query 25 schema
-struct<CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<b:int,count(1):bigint>
-- !query 25 output
1 1
2 2
-284,11 +280,11 struct<CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(count(1) as string)
-- !query 26
-SELECT udf(count(*)) FROM test_missing_target x, test_missing_target y
- WHERE udf(x.a) = udf(y.a)
- GROUP BY udf(x.b) ORDER BY udf(x.b)
+SELECT count(*) FROM test_missing_target x, test_missing_target y
+ WHERE x.a = y.a
+ GROUP BY x.b ORDER BY x.b
-- !query 26 schema
-struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<count(1):bigint>
-- !query 26 output
1
2
-297,22 +293,22 struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 27
-SELECT udf(a%2), udf(count(udf(b))) FROM test_missing_target
-GROUP BY udf(test_missing_target.a%2)
-ORDER BY udf(test_missing_target.a%2)
+SELECT a%2, count(b) FROM test_missing_target
+GROUP BY test_missing_target.a%2
+ORDER BY test_missing_target.a%2
-- !query 27 schema
-struct<CAST(udf(cast((a % 2) as string)) AS INT):int,CAST(udf(cast(count(cast(udf(cast(b as string)) as int)) as string)) AS BIGINT):bigint>
+struct<(a % 2):int,count(b):bigint>
-- !query 27 output
0 5
1 5
-- !query 28
-SELECT udf(count(c)) FROM test_missing_target
-GROUP BY udf(lower(test_missing_target.c))
-ORDER BY udf(lower(test_missing_target.c))
+SELECT count(c) FROM test_missing_target
+GROUP BY lower(test_missing_target.c)
+ORDER BY lower(test_missing_target.c)
-- !query 28 schema
-struct<CAST(udf(cast(count(c) as string)) AS BIGINT):bigint>
+struct<count(c):bigint>
-- !query 28 output
2
3
-321,18 +317,18 struct<CAST(udf(cast(count(c) as string)) AS BIGINT):bigint>
-- !query 29
-SELECT udf(count(udf(a))) FROM test_missing_target GROUP BY udf(a) ORDER BY udf(b)
+SELECT count(a) FROM test_missing_target GROUP BY a ORDER BY b
-- !query 29 schema
struct<>
-- !query 29 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`b`' given input columns: [CAST(udf(cast(count(cast(udf(cast(a as string)) as int)) as string)) AS BIGINT)]; line 1 pos 80
+cannot resolve '`b`' given input columns: [count(a)]; line 1 pos 61
-- !query 30
-SELECT udf(count(b)) FROM test_missing_target GROUP BY udf(b/2) ORDER BY udf(b/2)
+SELECT count(b) FROM test_missing_target GROUP BY b/2 ORDER BY b/2
-- !query 30 schema
-struct<CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
+struct<count(b):bigint>
-- !query 30 output
1
5
-340,10 +336,10 struct<CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
-- !query 31
-SELECT udf(lower(test_missing_target.c)), udf(count(udf(c)))
- FROM test_missing_target GROUP BY udf(lower(c)) ORDER BY udf(lower(c))
+SELECT lower(test_missing_target.c), count(c)
+ FROM test_missing_target GROUP BY lower(c) ORDER BY lower(c)
-- !query 31 schema
-struct<CAST(udf(cast(lower(c) as string)) AS STRING):string,CAST(udf(cast(count(cast(udf(cast(c as string)) as string)) as string)) AS BIGINT):bigint>
+struct<lower(c):string,count(c):bigint>
-- !query 31 output
abab 2
bbbb 3
-352,9 +348,9 xxxx 1
-- !query 32
-SELECT udf(a) FROM test_missing_target ORDER BY udf(upper(udf(d)))
+SELECT a FROM test_missing_target ORDER BY upper(d)
-- !query 32 schema
-struct<CAST(udf(cast(a as string)) AS INT):int>
+struct<a:int>
-- !query 32 output
0
1
-369,33 +365,32 struct<CAST(udf(cast(a as string)) AS INT):int>
-- !query 33
-SELECT udf(count(b)) FROM test_missing_target
- GROUP BY udf((b + 1) / 2) ORDER BY udf((b + 1) / 2) desc
+SELECT count(b) FROM test_missing_target
+ GROUP BY (b + 1) / 2 ORDER BY (b + 1) / 2 desc
-- !query 33 schema
-struct<CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
+struct<count(b):bigint>
-- !query 33 output
7
3
-- !query 34
-SELECT udf(count(udf(x.a))) FROM test_missing_target x, test_missing_target y
- WHERE udf(x.a) = udf(y.a)
- GROUP BY udf(b/2) ORDER BY udf(b/2)
+SELECT count(x.a) FROM test_missing_target x, test_missing_target y
+ WHERE x.a = y.a
+ GROUP BY b/2 ORDER BY b/2
-- !query 34 schema
struct<>
-- !query 34 output
org.apache.spark.sql.AnalysisException
-Reference 'b' is ambiguous, could be: x.b, y.b.; line 3 pos 14
+Reference 'b' is ambiguous, could be: x.b, y.b.; line 3 pos 10
-- !query 35
-SELECT udf(x.b/2), udf(count(udf(x.b))) FROM test_missing_target x,
-test_missing_target y
- WHERE udf(x.a) = udf(y.a)
- GROUP BY udf(x.b/2) ORDER BY udf(x.b/2)
+SELECT x.b/2, count(x.b) FROM test_missing_target x, test_missing_target y
+ WHERE x.a = y.a
+ GROUP BY x.b/2 ORDER BY x.b/2
-- !query 35 schema
-struct<CAST(udf(cast((b div 2) as string)) AS INT):int,CAST(udf(cast(count(cast(udf(cast(b as string)) as int)) as string)) AS BIGINT):bigint>
+struct<(b div 2):int,count(b):bigint>
-- !query 35 output
0 1
1 5
-403,14 +398,14 struct<CAST(udf(cast((b div 2) as string)) AS INT):int,CAST(udf(cast(count(cast(
-- !query 36
-SELECT udf(count(udf(b))) FROM test_missing_target x, test_missing_target y
- WHERE udf(x.a) = udf(y.a)
- GROUP BY udf(x.b/2)
+SELECT count(b) FROM test_missing_target x, test_missing_target y
+ WHERE x.a = y.a
+ GROUP BY x.b/2
-- !query 36 schema
struct<>
-- !query 36 output
org.apache.spark.sql.AnalysisException
-Reference 'b' is ambiguous, could be: x.b, y.b.; line 1 pos 21
+Reference 'b' is ambiguous, could be: x.b, y.b.; line 1 pos 13
-- !query 37
```
</p>
</details>
## How was this patch tested?
Tested as Guided in SPARK-27921
Closes#25350 from Udbhav30/master.
Authored-by: Udbhav30 <u.agrawal30@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR add supportColumnar in DebugExec. Seems there was a conflict between https://github.com/apache/spark/pull/25274 and https://github.com/apache/spark/pull/25264
Currently tests are broken in Jenkins:
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/108687/https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/108688/https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/108693/
```
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: makeCopy, tree: ColumnarToRow +- InMemoryTableScan [id#356956L] +- InMemoryRelation [id#356956L], StorageLevel(disk, memory, deserialized, 1 replicas) +- *(1) Range (0, 5, step=1, splits=2)
Stacktrace
sbt.ForkMain$ForkError: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: makeCopy, tree:
ColumnarToRow
+- InMemoryTableScan [id#356956L]
+- InMemoryRelation [id#356956L], StorageLevel(disk, memory, deserialized, 1 replicas)
+- *(1) Range (0, 5, step=1, splits=2)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
at org.apache.spark.sql.catalyst.trees.TreeNode.makeCopy(TreeNode.scala:431)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:404)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:323)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:287)
```
## How was this patch tested?
Manually tested the failed test.
Closes#25365 from HyukjinKwon/SPARK-28537.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR is a followup of a fix as described in here: #25215 (comment)
<details><summary>Diff comparing to 'group-analytics.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/udf/udf-group-analytics.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-group-analytics.sql.out
index 3439a05727..de297ab166 100644
--- a/sql/core/src/test/resources/sql-tests/results/udf/udf-group-analytics.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-group-analytics.sql.out
-13,9 +13,9 struct<>
-- !query 1
-SELECT a + b, b, SUM(a - b) FROM testData GROUP BY a + b, b WITH CUBE
+SELECT udf(a + b), b, udf(SUM(a - b)) FROM testData GROUP BY udf(a + b), b WITH CUBE
-- !query 1 schema
-struct<(a + b):int,b:int,sum((a - b)):bigint>
+struct<CAST(udf(cast((a + b) as string)) AS INT):int,b:int,CAST(udf(cast(sum(cast((a - b) as bigint)) as string)) AS BIGINT):bigint>
-- !query 1 output
2 1 0
2 NULL 0
-33,9 +33,9 NULL NULL 3
-- !query 2
-SELECT a, b, SUM(b) FROM testData GROUP BY a, b WITH CUBE
+SELECT udf(a), udf(b), SUM(b) FROM testData GROUP BY udf(a), b WITH CUBE
-- !query 2 schema
-struct<a:int,b:int,sum(b):bigint>
+struct<CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(b as string)) AS INT):int,sum(b):bigint>
-- !query 2 output
1 1 1
1 2 2
-52,9 +52,9 NULL NULL 9
-- !query 3
-SELECT a + b, b, SUM(a - b) FROM testData GROUP BY a + b, b WITH ROLLUP
+SELECT udf(a + b), b, SUM(a - b) FROM testData GROUP BY a + b, b WITH ROLLUP
-- !query 3 schema
-struct<(a + b):int,b:int,sum((a - b)):bigint>
+struct<CAST(udf(cast((a + b) as string)) AS INT):int,b:int,sum((a - b)):bigint>
-- !query 3 output
2 1 0
2 NULL 0
-70,9 +70,9 NULL NULL 3
-- !query 4
-SELECT a, b, SUM(b) FROM testData GROUP BY a, b WITH ROLLUP
+SELECT udf(a), b, udf(SUM(b)) FROM testData GROUP BY udf(a), b WITH ROLLUP
-- !query 4 schema
-struct<a:int,b:int,sum(b):bigint>
+struct<CAST(udf(cast(a as string)) AS INT):int,b:int,CAST(udf(cast(sum(cast(b as bigint)) as string)) AS BIGINT):bigint>
-- !query 4 output
1 1 1
1 2 2
-97,7 +97,7 struct<>
-- !query 6
-SELECT course, year, SUM(earnings) FROM courseSales GROUP BY ROLLUP(course, year) ORDER BY course, year
+SELECT course, year, SUM(earnings) FROM courseSales GROUP BY ROLLUP(course, year) ORDER BY udf(course), year
-- !query 6 schema
struct<course:string,year:int,sum(earnings):bigint>
-- !query 6 output
-111,7 +111,7 dotNET 2013 48000
-- !query 7
-SELECT course, year, SUM(earnings) FROM courseSales GROUP BY CUBE(course, year) ORDER BY course, year
+SELECT course, year, SUM(earnings) FROM courseSales GROUP BY CUBE(course, year) ORDER BY course, udf(year)
-- !query 7 schema
struct<course:string,year:int,sum(earnings):bigint>
-- !query 7 output
-127,9 +127,9 dotNET 2013 48000
-- !query 8
-SELECT course, year, SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(course, year)
+SELECT course, udf(year), SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(course, year)
-- !query 8 schema
-struct<course:string,year:int,sum(earnings):bigint>
+struct<course:string,CAST(udf(cast(year as string)) AS INT):int,sum(earnings):bigint>
-- !query 8 output
Java NULL 50000
NULL 2012 35000
-138,26 +138,26 dotNET NULL 63000
-- !query 9
-SELECT course, year, SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(course)
+SELECT course, year, udf(SUM(earnings)) FROM courseSales GROUP BY course, year GROUPING SETS(course)
-- !query 9 schema
-struct<course:string,year:int,sum(earnings):bigint>
+struct<course:string,year:int,CAST(udf(cast(sum(cast(earnings as bigint)) as string)) AS BIGINT):bigint>
-- !query 9 output
Java NULL 50000
dotNET NULL 63000
-- !query 10
-SELECT course, year, SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(year)
+SELECT udf(course), year, SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(year)
-- !query 10 schema
-struct<course:string,year:int,sum(earnings):bigint>
+struct<CAST(udf(cast(course as string)) AS STRING):string,year:int,sum(earnings):bigint>
-- !query 10 output
NULL 2012 35000
NULL 2013 78000
-- !query 11
-SELECT course, SUM(earnings) AS sum FROM courseSales
-GROUP BY course, earnings GROUPING SETS((), (course), (course, earnings)) ORDER BY course, sum
+SELECT course, udf(SUM(earnings)) AS sum FROM courseSales
+GROUP BY course, earnings GROUPING SETS((), (course), (course, earnings)) ORDER BY course, udf(sum)
-- !query 11 schema
struct<course:string,sum:bigint>
-- !query 11 output
-173,7 +173,7 dotNET 63000
-- !query 12
SELECT course, SUM(earnings) AS sum, GROUPING_ID(course, earnings) FROM courseSales
-GROUP BY course, earnings GROUPING SETS((), (course), (course, earnings)) ORDER BY course, sum
+GROUP BY course, earnings GROUPING SETS((), (course), (course, earnings)) ORDER BY udf(course), sum
-- !query 12 schema
struct<course:string,sum:bigint,grouping_id(course, earnings):int>
-- !query 12 output
-188,10 +188,10 dotNET 63000 1
-- !query 13
-SELECT course, year, GROUPING(course), GROUPING(year), GROUPING_ID(course, year) FROM courseSales
+SELECT udf(course), udf(year), GROUPING(course), GROUPING(year), GROUPING_ID(course, year) FROM courseSales
GROUP BY CUBE(course, year)
-- !query 13 schema
-struct<course:string,year:int,grouping(course):tinyint,grouping(year):tinyint,grouping_id(course, year):int>
+struct<CAST(udf(cast(course as string)) AS STRING):string,CAST(udf(cast(year as string)) AS INT):int,grouping(course):tinyint,grouping(year):tinyint,grouping_id(course, year):int>
-- !query 13 output
Java 2012 0 0 0
Java 2013 0 0 0
-205,7 +205,7 dotNET NULL 0 1 1
-- !query 14
-SELECT course, year, GROUPING(course) FROM courseSales GROUP BY course, year
+SELECT course, udf(year), GROUPING(course) FROM courseSales GROUP BY course, udf(year)
-- !query 14 schema
struct<>
-- !query 14 output
-214,7 +214,7 grouping() can only be used with GroupingSets/Cube/Rollup;
-- !query 15
-SELECT course, year, GROUPING_ID(course, year) FROM courseSales GROUP BY course, year
+SELECT course, udf(year), GROUPING_ID(course, year) FROM courseSales GROUP BY udf(course), year
-- !query 15 schema
struct<>
-- !query 15 output
-223,7 +223,7 grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 16
-SELECT course, year, grouping__id FROM courseSales GROUP BY CUBE(course, year) ORDER BY grouping__id, course, year
+SELECT course, year, grouping__id FROM courseSales GROUP BY CUBE(course, year) ORDER BY grouping__id, course, udf(year)
-- !query 16 schema
struct<course:string,year:int,grouping__id:int>
-- !query 16 output
-240,7 +240,7 NULL NULL 3
-- !query 17
SELECT course, year FROM courseSales GROUP BY CUBE(course, year)
-HAVING GROUPING(year) = 1 AND GROUPING_ID(course, year) > 0 ORDER BY course, year
+HAVING GROUPING(year) = 1 AND GROUPING_ID(course, year) > 0 ORDER BY course, udf(year)
-- !query 17 schema
struct<course:string,year:int>
-- !query 17 output
-250,7 +250,7 dotNET NULL
-- !query 18
-SELECT course, year FROM courseSales GROUP BY course, year HAVING GROUPING(course) > 0
+SELECT course, udf(year) FROM courseSales GROUP BY udf(course), year HAVING GROUPING(course) > 0
-- !query 18 schema
struct<>
-- !query 18 output
-259,7 +259,7 grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 19
-SELECT course, year FROM courseSales GROUP BY course, year HAVING GROUPING_ID(course) > 0
+SELECT course, udf(udf(year)) FROM courseSales GROUP BY course, year HAVING GROUPING_ID(course) > 0
-- !query 19 schema
struct<>
-- !query 19 output
-268,9 +268,9 grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 20
-SELECT course, year FROM courseSales GROUP BY CUBE(course, year) HAVING grouping__id > 0
+SELECT udf(course), year FROM courseSales GROUP BY CUBE(course, year) HAVING grouping__id > 0
-- !query 20 schema
-struct<course:string,year:int>
+struct<CAST(udf(cast(course as string)) AS STRING):string,year:int>
-- !query 20 output
Java NULL
NULL 2012
-281,7 +281,7 dotNET NULL
-- !query 21
SELECT course, year, GROUPING(course), GROUPING(year) FROM courseSales GROUP BY CUBE(course, year)
-ORDER BY GROUPING(course), GROUPING(year), course, year
+ORDER BY GROUPING(course), GROUPING(year), course, udf(year)
-- !query 21 schema
struct<course:string,year:int,grouping(course):tinyint,grouping(year):tinyint>
-- !query 21 output
-298,7 +298,7 NULL NULL 1 1
-- !query 22
SELECT course, year, GROUPING_ID(course, year) FROM courseSales GROUP BY CUBE(course, year)
-ORDER BY GROUPING(course), GROUPING(year), course, year
+ORDER BY GROUPING(course), GROUPING(year), course, udf(year)
-- !query 22 schema
struct<course:string,year:int,grouping_id(course, year):int>
-- !query 22 output
-314,7 +314,7 NULL NULL 3
-- !query 23
-SELECT course, year FROM courseSales GROUP BY course, year ORDER BY GROUPING(course)
+SELECT course, udf(year) FROM courseSales GROUP BY course, udf(year) ORDER BY GROUPING(course)
-- !query 23 schema
struct<>
-- !query 23 output
-323,7 +323,7 grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 24
-SELECT course, year FROM courseSales GROUP BY course, year ORDER BY GROUPING_ID(course)
+SELECT course, udf(year) FROM courseSales GROUP BY course, udf(year) ORDER BY GROUPING_ID(course)
-- !query 24 schema
struct<>
-- !query 24 output
-332,7 +332,7 grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 25
-SELECT course, year FROM courseSales GROUP BY CUBE(course, year) ORDER BY grouping__id, course, year
+SELECT course, year FROM courseSales GROUP BY CUBE(course, year) ORDER BY grouping__id, udf(course), year
-- !query 25 schema
struct<course:string,year:int>
-- !query 25 output
-348,7 +348,7 NULL NULL
-- !query 26
-SELECT a + b AS k1, b AS k2, SUM(a - b) FROM testData GROUP BY CUBE(k1, k2)
+SELECT udf(a + b) AS k1, udf(b) AS k2, SUM(a - b) FROM testData GROUP BY CUBE(k1, k2)
-- !query 26 schema
struct<k1:int,k2:int,sum((a - b)):bigint>
-- !query 26 output
-368,7 +368,7 NULL NULL 3
-- !query 27
-SELECT a + b AS k, b, SUM(a - b) FROM testData GROUP BY ROLLUP(k, b)
+SELECT udf(udf(a + b)) AS k, b, SUM(a - b) FROM testData GROUP BY ROLLUP(k, b)
-- !query 27 schema
struct<k:int,b:int,sum((a - b)):bigint>
-- !query 27 output
-386,9 +386,9 NULL NULL 3
-- !query 28
-SELECT a + b, b AS k, SUM(a - b) FROM testData GROUP BY a + b, k GROUPING SETS(k)
+SELECT udf(a + b), udf(udf(b)) AS k, SUM(a - b) FROM testData GROUP BY a + b, k GROUPING SETS(k)
-- !query 28 schema
-struct<(a + b):int,k:int,sum((a - b)):bigint>
+struct<CAST(udf(cast((a + b) as string)) AS INT):int,k:int,sum((a - b)):bigint>
-- !query 28 output
NULL 1 3
NULL 2 0
```
</p>
</details>
## How was this patch tested?
Tested as instructed in SPARK-27921.
Closes#25362 from skonto/group-analytics-followup.
Authored-by: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This patch tries to keep consistency whenever UTF-8 charset is needed, as using `StandardCharsets.UTF_8` instead of using "UTF-8". If the String type is needed, `StandardCharsets.UTF_8.name()` is used.
This change also brings the benefit of getting rid of `UnsupportedEncodingException`, as we're providing `Charset` instead of `String` whenever possible.
This also changes some private Catalyst helper methods to operate on encodings as `Charset` objects rather than strings.
## How was this patch tested?
Existing unit tests.
Closes#25335 from HeartSaVioR/SPARK-28601.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
I did a post-hoc review of https://github.com/apache/spark/pull/25008 , and would like to propose some cleanups/fixes/improvements:
1. Do not track the scanTime metrics in `ColumnarToRowExec`. This metrics is specific to file scan, and doesn't make sense for a general batch-to-row operator.
2. Because of 2, we need to track scanTime when building RDDs in the file scan node.
3. use `RDD#mapPartitionsInternal` instead of `flatMap` in several places, as `mapPartitionsInternal` is created for Spark SQL and we use it in almost all the SQL operators.
4. Add `limitNotReachedCond` in `ColumnarToRowExec`. This was in the `ColumnarBatchScan` before and is critical for performance.
5. Clear the relationship between codegen stage and columnar stage. The whole-stage-codegen framework is completely row-based, so these 2 kinds of stages can NEVER overlap. When they are adjacent, it's either a `RowToColumnarExec` above `WholeStageExec`, or a `ColumnarToRowExec` above the `InputAdapter`.
6. Reuse the `ColumnarBatch` in `RowToColumnarExec`. We don't need to create a new one every time, just need to reset it.
7. Do not skip testing full scan node in `LogicalPlanTagInSparkPlanSuite`
8. Add back the removed tests in `WholeStageCodegenSuite`.
## How was this patch tested?
existing tests
Closes#25264 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is an alternative solution of https://github.com/apache/spark/pull/24442 . It fails the query if ambiguous self join is detected, instead of trying to disambiguate it. The problem is that, it's hard to come up with a reasonable rule to disambiguate, the rule proposed by #24442 is mostly a heuristic.
### background of the self-join problem:
This is a long-standing bug and I've seen many people complaining about it in JIRA/dev list.
A typical example:
```
val df1 = …
val df2 = df1.filter(...)
df1.join(df2, df1("a") > df2("a")) // returns empty result
```
The root cause is, `Dataset.apply` is so powerful that users think it returns a column reference which can point to the column of the Dataset at anywhere. This is not true in many cases. `Dataset.apply` returns an `AttributeReference` . Different Datasets may share the same `AttributeReference`. In the example above, `df2` adds a Filter operator above the logical plan of `df1`, and the Filter operator reserves the output `AttributeReference` of its child. This means, `df1("a")` is exactly the same as `df2("a")`, and `df1("a") > df2("a")` always evaluates to false.
### The rule to detect ambiguous column reference caused by self join:
We can reuse the infra in #24442 :
1. each Dataset has a globally unique id.
2. the `AttributeReference` returned by `Dataset.apply` carries the ID and column position(e.g. 3rd column of the Dataset) via metadata.
3. the logical plan of a `Dataset` carries the ID via `TreeNodeTag`
When self-join happens, the analyzer asks the right side plan of join to re-generate output attributes with new exprIds. Based on it, a simple rule to detect ambiguous self join is:
1. find all column references (i.e. `AttributeReference`s with Dataset ID and col position) in the root node of a query plan.
2. for each column reference, traverse the query plan tree, find a sub-plan that carries Dataset ID and the ID is the same as the one in the column reference.
3. get the corresponding output attribute of the sub-plan by the col position in the column reference.
4. if the corresponding output attribute has a different exprID than the column reference, then it means this sub-plan is on the right side of a self-join and has regenerated its output attributes. This is an ambiguous self join because the column reference points to a table being self-joined.
## How was this patch tested?
existing tests and new test cases
Closes#25107 from cloud-fan/new-self-join.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
DebugExec does not implement doExecuteBroadcast and doExecuteColumnar so we can't debug broadcast or columnar related query.
One example for broadcast is here.
```
val df1 = Seq(1, 2, 3).toDF
val df2 = Seq(1, 2, 3).toDF
val joined = df1.join(df2, df1("value") === df2("value"))
joined.debug()
java.lang.UnsupportedOperationException: Debug does not implement doExecuteBroadcast
...
```
Another for columnar is here.
```
val df = Seq(1, 2, 3).toDF
df.persist
df.debug()
java.lang.IllegalStateException: Internal Error class org.apache.spark.sql.execution.debug.package$DebugExec has column support mismatch:
...
```
## How was this patch tested?
Additional test cases in DebuggingSuite.
Closes#25274 from sarutak/fix-debugexec.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
- DataFrameWriter.insertInto should match column names by position.
- Clean up test cases.
## How was this patch tested?
New tests:
- insertInto: append by position
- insertInto: overwrite partitioned table in static mode by position
- insertInto: overwrite partitioned table in dynamic mode by position
Closes#25353 from jzhuge/SPARK-28178-bypos.
Authored-by: John Zhuge <jzhuge@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
It's hard to know if the query needs to be sorted like [`SQLQueryTestSuite.isSorted`](2ecc39c8d3/sql/core/src/test/scala/org/apache/spark/sql/SQLQueryTestSuite.scala (L375-L380)) when building a test framework for Thriftserver. So we can sort both the `outputs` and the `expectedOutputs. However, we removed leading write space in the golden result file. This can lead to inconsistent results.
This PR makes it does not remove leading write space in the golden result file. Trailing write space still needs to be removed.
## How was this patch tested?
N/A
Closes#25351 from wangyum/SPARK-28614.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
See discussion on the JIRA (and dev). At heart, we find that math.log and math.pow can actually return slightly different results across platforms because of hardware optimizations. For the actual SQL log and pow functions, I propose that we should use StrictMath instead to ensure the answers are already the same. (This should have the benefit of helping tests pass on aarch64.)
Further, the atanh function (which is not part of java.lang.Math) can be implemented in a slightly different and more accurate way.
## How was this patch tested?
Existing tests (which will need to be changed).
Some manual testing locally to understand the numeric issues.
Closes#25279 from srowen/SPARK-28519.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
When PythonUDF is used in group by, and it is also in aggregate expression, like
```
SELECT pyUDF(a + 1), COUNT(b) FROM testData GROUP BY pyUDF(a + 1)
```
It causes analysis exception in `CheckAnalysis`, like
```
org.apache.spark.sql.AnalysisException: expression 'testdata.`a`' is neither present in the group by, nor is it an aggregate function.
```
First, `CheckAnalysis` can't check semantic equality between PythonUDFs.
Second, even we make it possible, runtime exception will be thrown
```
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: pythonUDF1#8615
...
Cause: java.lang.RuntimeException: Couldn't find pythonUDF1#8615 in [cast(pythonUDF0#8614 as int)#8617,count(b#8599)#8607L]
```
The cause is, `ExtractPythonUDFs` extracts both PythonUDFs in group by and aggregate expression. The PythonUDFs are two different aliases now in the logical aggregate. In runtime, we can't bind the resulting expression in aggregate to its grouping and aggregate attributes.
This patch proposes a rule `ExtractGroupingPythonUDFFromAggregate` to extract PythonUDFs in group by and evaluate them before aggregate. We replace the group by PythonUDF in aggregate expression with aliased result.
The query plan of query `SELECT pyUDF(a + 1), pyUDF(COUNT(b)) FROM testData GROUP BY pyUDF(a + 1)`, like
```
== Optimized Logical Plan ==
Project [CAST(pyUDF(cast((a + 1) as string)) AS INT)#8608, cast(pythonUDF0#8616 as bigint) AS CAST(pyUDF(cast(count(b) as string)) AS BIGINT)#8610L]
+- BatchEvalPython [pyUDF(cast(agg#8613L as string))], [pythonUDF0#8616]
+- Aggregate [cast(groupingPythonUDF#8614 as int)], [cast(groupingPythonUDF#8614 as int) AS CAST(pyUDF(cast((a + 1) as string)) AS INT)#8608, count(b#8599) AS agg#8613L]
+- Project [pythonUDF0#8615 AS groupingPythonUDF#8614, b#8599]
+- BatchEvalPython [pyUDF(cast((a#8598 + 1) as string))], [pythonUDF0#8615]
+- LocalRelation [a#8598, b#8599]
== Physical Plan ==
*(3) Project [CAST(pyUDF(cast((a + 1) as string)) AS INT)#8608, cast(pythonUDF0#8616 as bigint) AS CAST(pyUDF(cast(count(b) as string)) AS BIGINT)#8610L]
+- BatchEvalPython [pyUDF(cast(agg#8613L as string))], [pythonUDF0#8616]
+- *(2) HashAggregate(keys=[cast(groupingPythonUDF#8614 as int)#8617], functions=[count(b#8599)], output=[CAST(pyUDF(cast((a + 1) as string)) AS INT)#8608, agg#8613L])
+- Exchange hashpartitioning(cast(groupingPythonUDF#8614 as int)#8617, 5), true
+- *(1) HashAggregate(keys=[cast(groupingPythonUDF#8614 as int) AS cast(groupingPythonUDF#8614 as int)#8617], functions=[partial_count(b#8599)], output=[cast(groupingPythonUDF#8614 as int)#8617, count#8619L])
+- *(1) Project [pythonUDF0#8615 AS groupingPythonUDF#8614, b#8599]
+- BatchEvalPython [pyUDF(cast((a#8598 + 1) as string))], [pythonUDF0#8615]
+- LocalTableScan [a#8598, b#8599]
```
## How was this patch tested?
Added tests.
Closes#25215 from viirya/SPARK-28445.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
```sql
spark-sql> select cast(1);
19/07/26 00:54:17 ERROR SparkSQLDriver: Failed in [select cast(1)]
java.lang.UnsupportedOperationException: empty.init
at scala.collection.TraversableLike$class.init(TraversableLike.scala:451)
at scala.collection.mutable.ArrayOps$ofInt.scala$collection$IndexedSeqOptimized$$super$init(ArrayOps.scala:234)
at scala.collection.IndexedSeqOptimized$class.init(IndexedSeqOptimized.scala:135)
at scala.collection.mutable.ArrayOps$ofInt.init(ArrayOps.scala:234)
at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$7$$anonfun$11.apply(FunctionRegistry.scala:565)
at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$7$$anonfun$11.apply(FunctionRegistry.scala:558)
at scala.Option.getOrElse(Option.scala:121)
```
The reason is that we did not handle the case [`validParametersCount.length == 0`](2d74f14d74/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala (L588)) because the [parameter types](2d74f14d74/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala (L589)) can be `Expression`, `DataType` and `Option`. This PR makes it handle the case `validParametersCount.length == 0`.
## How was this patch tested?
unit tests
Closes#25261 from wangyum/SPARK-28521.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Prior to this change, in an executor, on each heartbeat, memory metrics are polled and sent in the heartbeat. The heartbeat interval is 10s by default. With this change, in an executor, memory metrics can optionally be polled in a separate poller at a shorter interval.
For each executor, we use a map of (stageId, stageAttemptId) to (count of running tasks, executor metric peaks) to track what stages are active as well as the per-stage memory metric peaks. When polling the executor memory metrics, we attribute the memory to the active stage(s), and update the peaks. In a heartbeat, we send the per-stage peaks (for stages active at that time), and then reset the peaks. The semantics would be that the per-stage peaks sent in each heartbeat are the peaks since the last heartbeat.
We also keep a map of taskId to memory metric peaks. This tracks the metric peaks during the lifetime of the task. The polling thread updates this as well. At end of a task, we send the peak metric values in the task result. In case of task failure, we send the peak metric values in the `TaskFailedReason`.
We continue to do the stage-level aggregation in the EventLoggingListener.
For the driver, we still only poll on heartbeats. What the driver sends will be the current values of the metrics in the driver at the time of the heartbeat. This is semantically the same as before.
## How was this patch tested?
Unit tests. Manually tested applications on an actual system and checked the event logs; the metrics appear in the SparkListenerTaskEnd and SparkListenerStageExecutorMetrics events.
Closes#23767 from wypoon/wypoon_SPARK-26329.
Authored-by: Wing Yew Poon <wypoon@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Logging in driver when loading single large unsplittable file via `sc.textFile` or csv/json datasouce.
Current condition triggering logging is
* only generate one partition
* file is unsplittable, possible reason is:
- compressed by unsplittable compression algo such as gzip.
- multiLine mode in csv/json datasource
- wholeText mode in text datasource
* file size exceed the config threshold `spark.io.warning.largeFileThreshold` (default value is 1GB)
## How was this patch tested?
Manually test.
Generate one gzip file exceeding 1GB,
```
base64 -b 50 /dev/urandom | head -c 2000000000 > file1.txt
cat file1.txt | gzip > file1.gz
```
then launch spark-shell,
run
```
sc.textFile("file:///path/to/file1.gz").count()
```
Will print log like:
```
WARN HadoopRDD: Loading one large unsplittable file file:/.../f1.gz with only one partition, because the file is compressed by unsplittable compression codec
```
run
```
sc.textFile("file:///path/to/file1.txt").count()
```
Will print log like:
```
WARN HadoopRDD: Loading one large file file:/.../f1.gz with only one partition, we can increase partition numbers by the `minPartitions` argument in method `sc.textFile
```
run
```
spark.read.csv("file:///path/to/file1.gz").count
```
Will print log like:
```
WARN CSVScan: Loading one large unsplittable file file:/.../f1.gz with only one partition, the reason is: the file is compressed by unsplittable compression codec
```
run
```
spark.read.option("multiLine", true).csv("file:///path/to/file1.gz").count
```
Will print log like:
```
WARN CSVScan: Loading one large unsplittable file file:/.../f1.gz with only one partition, the reason is: the csv datasource is set multiLine mode
```
JSON and Text datasource also tested with similar cases.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#25134 from WeichenXu123/log_gz.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When an overflow occurs performing an arithmetic operation, we are returning an incorrect value. Instead, we should throw an exception, as stated in the SQL standard.
## How was this patch tested?
added UT + existing UTs (improved)
Closes#21599 from mgaido91/SPARK-24598.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR moves `replaceFunctionName(usage: String, functionName: String)`
from `DescribeFunctionCommand` to `ExpressionInfo` in order to make `ExpressionInfo` returns actual name instead of placeholder. We can get `ExpressionInfo`s directly through `SessionCatalog.lookupFunctionInfo` API and get the real names.
## How was this patch tested?
unit tests
Closes#25314 from wangyum/SPARK-28581.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Support multiple catalogs in the following InsertInto use cases:
- DataFrameWriter.insertInto("catalog.db.tbl")
Support matrix:
SaveMode|Partitioned Table|Partition Overwrite Mode|Action
--------|-----------------|------------------------|------
Append|*|*|AppendData
Overwrite|no|*|OverwriteByExpression(true)
Overwrite|yes|STATIC|OverwriteByExpression(true)
Overwrite|yes|DYNAMIC|OverwritePartitionsDynamic
## How was this patch tested?
New tests.
All existing catalyst and sql/core tests.
Closes#24980 from jzhuge/SPARK-28178-pr.
Authored-by: John Zhuge <jzhuge@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Right now `Error` is not sent to `QueryExecutionListener.onFailure`. If there is any `Error` (such as `AssertionError`) when running a query, `QueryExecutionListener.onFailure` cannot be triggered.
This PR changes `onFailure` to accept a `Throwable` instead.
## How was this patch tested?
Jenkins
Closes#25292 from zsxwing/fix-QueryExecutionListener.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
New function `make_timestamp()` takes 6 columns `year`, `month`, `day`, `hour`, `min`, `sec` + optionally `timezone`, and makes new column of the `TIMESTAMP` type. If values in the input columns are `null` or out of valid ranges, the function returns `null`. Valid ranges are:
- `year` - `[1, 9999]`
- `month` - `[1, 12]`
- `day` - `[1, 31]`
- `hour` - `[0, 23]`
- `min` - `[0, 59]`
- `sec` - `[0, 60]`. If the `sec` argument equals to 60, the seconds field is set to 0 and 1 minute is added to the final timestamp.
- `timezone` - an identifier of timezone. Actual database of timezones can be found there: https://www.iana.org/time-zones.
Also constructed timestamp must be valid otherwise `make_timestamp` returns `null`.
The function is implemented similarly to `make_timestamp` in PostgreSQL: https://www.postgresql.org/docs/11/functions-datetime.html to maintain feature parity with it.
Here is an example:
```sql
select make_timestamp(2014, 12, 28, 6, 30, 45.887);
2014-12-28 06:30:45.887
select make_timestamp(2014, 12, 28, 6, 30, 45.887, 'CET');
2014-12-28 10:30:45.887
select make_timestamp(2019, 6, 30, 23, 59, 60)
2019-07-01 00:00:00
```
Returned value has Spark Catalyst type `TIMESTAMP` which is similar to Oracle's `TIMESTAMP WITH LOCAL TIME ZONE` (see https://docs.oracle.com/cd/B28359_01/server.111/b28298/ch4datetime.htm#i1006169) where data is stored in the session time zone, and the time zone offset is not stored as part of the column data. When users retrieve the data, Spark returns it in the session time zone specified by the SQL config `spark.sql.session.timeZone`.
## How was this patch tested?
Added new tests to `DateExpressionsSuite`, and uncommented a test for `make_timestamp` in `pgSQL/timestamp.sql`.
Closes#25220 from MaxGekk/make_timestamp.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
These are what I found during working on #22282.
- Remove unused value: `UnsafeArraySuite#defaultTz`
- Remove redundant new modifier to the case class, `KafkaSourceRDDPartition`
- Remove unused variables from `RDD.scala`
- Remove trailing space from `structured-streaming-kafka-integration.md`
- Remove redundant parameter from `ArrowConvertersSuite`: `nullable` is `true` by default.
- Remove leading empty line: `UnsafeRow`
- Remove trailing empty line: `KafkaTestUtils`
- Remove unthrown exception type: `UnsafeMapData`
- Replace unused declarations: `expressions`
- Remove duplicated default parameter: `AnalysisErrorSuite`
- `ObjectExpressionsSuite`: remove duplicated parameters, conversions and unused variable
Closes#25251 from dongjinleekr/cleanup/201907.
Authored-by: Lee Dongjin <dongjin@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to use `uuuu` for years instead of `yyyy` in date/timestamp patterns without the era pattern `G` (https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html). **Parsing/formatting of positive years (current era) will be the same.** The difference is in formatting negative years belong to previous era - BC (Before Christ).
I replaced the `yyyy` pattern by `uuuu` everywhere except:
1. Test, Suite & Benchmark. Existing tests must work as is.
2. `SimpleDateFormat` because it doesn't support the `uuuu` pattern.
3. Comments and examples (except comments related to already replaced patterns).
Before the changes, the year of common era `100` and the year of BC era `-99`, showed similarly as `100`. After the changes negative years will be formatted with the `-` sign.
Before:
```Scala
scala> Seq(java.time.LocalDate.of(-99, 1, 1)).toDF().show
+----------+
| value|
+----------+
|0100-01-01|
+----------+
```
After:
```Scala
scala> Seq(java.time.LocalDate.of(-99, 1, 1)).toDF().show
+-----------+
| value|
+-----------+
|-0099-01-01|
+-----------+
```
## How was this patch tested?
By existing test suites, and added tests for negative years to `DateFormatterSuite` and `TimestampFormatterSuite`.
Closes#25230 from MaxGekk/year-pattern-uuuu.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Code is not generated for LocalTableScanExec although proper situations.
If a LocalTableScanExec plan has the direct parent plan which supports WholeStageCodegen,
the LocalTableScanExec plan also should be within a WholeStageCodegen domain.
But code is not generated for LocalTableScanExec and InputAdapter is inserted for now.
```
val df1 = spark.createDataset(1 to 10).toDF
val df2 = spark.createDataset(1 to 10).toDF
val df3 = df1.join(df2, df1("value") === df2("value"))
df3.explain(true)
...
== Physical Plan ==
*(1) BroadcastHashJoin [value#1], [value#6], Inner, BuildRight
:- LocalTableScan [value#1] // LocalTableScanExec is not within a WholeStageCodegen domain
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)))
+- LocalTableScan [value#6]
```
```
scala> df3.queryExecution.executedPlan.children.head.children.head.getClass
res4: Class[_ <: org.apache.spark.sql.execution.SparkPlan] = class org.apache.spark.sql.execution.InputAdapter
```
For the current implementation of LocalTableScanExec, codegen is enabled in case `parent` is not null
but `parent` is set in `consume`, which is called after `insertInputAdapter` so it doesn't work as intended.
After applying this cnahge, we can get following plan, which means LocalTableScanExec is within a WholeStageCodegen domain.
```
== Physical Plan ==
*(1) BroadcastHashJoin [value#63], [value#68], Inner, BuildRight
:- *(1) LocalTableScan [value#63]
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)))
+- LocalTableScan [value#68]
## How was this patch tested?
New test cases are added into WholeStageCodegenSuite.
Closes#25260 from sarutak/localtablescan-improvement.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
Fix for ```SPARK-28441 (PythonUDF used in correlated scalar subquery causes UnsupportedOperationException)``` is in. Re-enable the commented out test for ```udf(max(udf(column))) ```
## How was this patch tested?
use existing test ```udf-except.sql```
Closes#25278 from huaxingao/spark-28277n.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
In case of CatalogFileIndex datasource table, sizeInBytes is always coming as default size in bytes, which is 8.0EB (Even when the user give fallBackToHdfsForStatsEnabled=true) . So, the datasource table which has CatalogFileIndex, always prefer SortMergeJoin, instead of BroadcastJoin, even though the size is below broadcast join threshold.
In this PR, In case of CatalogFileIndex table, if we enable "fallBackToHdfsForStatsEnabled=true", then the computeStatistics get the sizeInBytes from the hdfs and we get the actual size of the table. Hence, during join operation, when the table size is below broadcast threshold, it will prefer broadCastHashJoin instead of SortMergeJoin.
Added UT
Closes#22502 from shahidki31/SPARK-25474.
Authored-by: shahid <shahidki31@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
`ObjectAggregationIterator` shows a directional info message to increase `spark.sql.objectHashAggregate.sortBased.fallbackThreshold` when the size of the in-memory hash map grows too large and it falls back to sort-based aggregation.
However, we don't know how much we need to increase. This PR adds the size of the current in-memory hash map size to the log message.
**BEFORE**
```
15:21:41.669 Executor task launch worker for task 0 INFO
ObjectAggregationIterator: Aggregation hash map reaches threshold capacity (2 entries), ...
```
**AFTER**
```
15:20:05.742 Executor task launch worker for task 0 INFO
ObjectAggregationIterator: Aggregation hash map size 2 reaches threshold capacity (2 entries), ...
```
## How was this patch tested?
Manual. For example, run `ObjectHashAggregateSuite.scala`'s `typed_count fallback to sort-based aggregation` and search the above message in `target/unit-tests.log`.
Closes#25276 from dongjoon-hyun/SPARK-28545.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR add support typed `interval` expression:
```sql
spark-sql> select interval 'interval 3 year 1 hour';
interval 3 years 1 hours
spark-sql>
```
Please note that this pr did not add a cast alias for `interval` type like [other types](2d74f14d74/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala (L529-L541)) because neither PostgreSQL nor Hive supports this syntax.
## How was this patch tested?
unit tests
Closes#25241 from wangyum/SPARK-28424.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
We should add `assume(shouldTestPythonUDFs)`. Maybe it's not a biggie in general but it can matter in other venders' testing base. For instance, if somebody launches a test in a minimal docker image, it might make the tests failed suddenly.
This skipping stuff isn't completely new in our test base. See `TestUtils.testCommandAvailable` for instance.
## How was this patch tested?
Manually tested.
Closes#25272 from HyukjinKwon/SPARK-28441.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In SPARK-15370, We checked the expression at the root of the correlated subquery, in order to fix count bug. If a `PythonUDF` in in the checking path, evaluating it causes the failure as we can't statically evaluate `PythonUDF`. The Python UDF test added at SPARK-28277 shows this issue.
If we can statically evaluate the expression, we intercept NULL values coming from the outer join and replace them with the value that the subquery's expression like before, if it is not, we replace them with the `PythonUDF` expression, with statically evaluated parameters.
After this, the last query in `udf-except.sql` which throws `java.lang.UnsupportedOperationException` can be run:
```
SELECT t1.k
FROM t1
WHERE t1.v <= (SELECT udf(max(udf(t2.v)))
FROM t2
WHERE udf(t2.k) = udf(t1.k))
MINUS
SELECT t1.k
FROM t1
WHERE udf(t1.v) >= (SELECT min(udf(t2.v))
FROM t2
WHERE t2.k = t1.k)
-- !query 2 schema
struct<k:string>
-- !query 2 output
two
```
Note that this issue is also for other non-foldable expressions, like rand. As like PythonUDF, we can't call `eval` on this kind of expressions in optimization. The evaluation needs to defer to query runtime.
## How was this patch tested?
Added tests.
Closes#25204 from viirya/SPARK-28441.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR adds some tests converted from 'union.sql' to test UDFs
<details><summary>Diff comparing to 'union.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/union.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-union.sql.out
index b023df825d..84b5e10dbe 100644
--- a/sql/core/src/test/resources/sql-tests/results/union.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-union.sql.out
-19,10 +19,10 struct<>
-- !query 2
-SELECT *
-FROM (SELECT * FROM t1
+SELECT udf(c1) as c1, udf(c2) as c2
+FROM (SELECT udf(c1) as c1, udf(c2) as c2 FROM t1
UNION ALL
- SELECT * FROM t1)
+ SELECT udf(c1) as c1, udf(c2) as c2 FROM t1)
-- !query 2 schema
struct<c1:int,c2:string>
-- !query 2 output
-33,12 +33,12 struct<c1:int,c2:string>
-- !query 3
-SELECT *
-FROM (SELECT * FROM t1
+SELECT udf(c1) as c1, udf(c2) as c2
+FROM (SELECT udf(c1) as c1, udf(c2) as c2 FROM t1
UNION ALL
- SELECT * FROM t2
+ SELECT udf(c1) as c1, udf(c2) as c2 FROM t2
UNION ALL
- SELECT * FROM t2)
+ SELECT udf(c1) as c1, udf(c2) as c2 FROM t2)
-- !query 3 schema
struct<c1:decimal(11,1),c2:string>
-- !query 3 output
-51,11 +51,11 struct<c1:decimal(11,1),c2:string>
-- !query 4
-SELECT a
-FROM (SELECT 0 a, 0 b
+SELECT udf(udf(a)) as a
+FROM (SELECT udf(0) a, udf(0) b
UNION ALL
- SELECT SUM(1) a, CAST(0 AS BIGINT) b
- UNION ALL SELECT 0 a, 0 b) T
+ SELECT udf(SUM(1)) a, udf(CAST(0 AS BIGINT)) b
+ UNION ALL SELECT udf(0) a, udf(0) b) T
-- !query 4 schema
struct<a:bigint>
-- !query 4 output
-89,13 +89,13 struct<>
-- !query 8
-SELECT 1 AS x,
- col
-FROM (SELECT col AS col
- FROM (SELECT p1.col AS col
+SELECT udf(1) AS x,
+ udf(col) as col
+FROM (SELECT udf(col) AS col
+ FROM (SELECT udf(p1.col) AS col
FROM p1 CROSS JOIN p2
UNION ALL
- SELECT col
+ SELECT udf(col)
FROM p3) T1) T2
-- !query 8 schema
struct<x:int,col:int>
-105,9 +105,9 struct<x:int,col:int>
-- !query 9
-SELECT map(1, 2), 'str'
+SELECT map(1, 2), udf('str') as str
UNION ALL
-SELECT map(1, 2, 3, NULL), 1
+SELECT map(1, 2, 3, NULL), udf(1)
-- !query 9 schema
struct<map(1, 2):map<int,int>,str:string>
-- !query 9 output
-116,9 +116,9 struct<map(1, 2):map<int,int>,str:string>
-- !query 10
-SELECT array(1, 2), 'str'
+SELECT array(1, 2), udf('str') as str
UNION ALL
-SELECT array(1, 2, 3, NULL), 1
+SELECT array(1, 2, 3, NULL), udf(1)
-- !query 10 schema
struct<array(1, 2):array<int>,str:string>
-- !query 10 output
```
</p>
</details>
## How was this patch tested?
Tested as guided in SPARK-27921.
Closes#25202 from yiheng/fix_28289.
Authored-by: Yiheng Wang <yihengw@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR fixes the log messages like `attempt 0stage 9.0` by adding a comma followed by space. These are all instances in `DataWritingSparkTask` which was introduced at 6d16b9885d. This should be fixed in `branch-2.4`, too.
```
19/07/25 18:35:01 INFO DataWritingSparkTask: Commit authorized for partition 65 (task 153, attempt 0stage 9.0)
19/07/25 18:35:01 INFO DataWritingSparkTask: Committed partition 65 (task 153, attempt 0stage 9.0)
```
## How was this patch tested?
This only changes log messages. Pass the Jenkins with the existing tests.
Closes#25257 from dongjoon-hyun/DataWritingSparkTask.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Support multiple catalogs in the following InsertTable use cases:
- INSERT INTO [TABLE] catalog.db.tbl
- INSERT OVERWRITE TABLE catalog.db.tbl
Support matrix:
Overwrite|Partitioned Table|Partition Clause |Partition Overwrite Mode|Action
---------|-----------------|-----------------|------------------------|-----
false|*|*|*|AppendData
true|no|(empty)|*|OverwriteByExpression(true)
true|yes|p1,p2 or p1 or p2 or (empty)|STATIC|OverwriteByExpression(true)
true|yes|p2,p2 or p1 or p2 or (empty)|DYNAMIC|OverwritePartitionsDynamic
true|yes|p1=23,p2=3|*|OverwriteByExpression(p1=23 and p2=3)
true|yes|p1=23,p2 or p1=23|STATIC|OverwriteByExpression(p1=23)
true|yes|p1=23,p2 or p1=23|DYNAMIC|OverwritePartitionsDynamic
Notes:
- Assume the partitioned table has 2 partitions: p1 and p2.
- `STATIC` is the default Partition Overwrite Mode for data source tables.
- DSv2 tables currently do not support `IfPartitionNotExists`.
## How was this patch tested?
New tests.
All existing catalyst and sql/core tests.
Closes#24832 from jzhuge/SPARK-27845-pr.
Lead-authored-by: Ryan Blue <blue@apache.org>
Co-authored-by: John Zhuge <jzhuge@apache.org>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
## What changes were proposed in this pull request?
This PR adds some tests converted from window.sql to test UDFs. Please see the contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
<details><summary>Diff comparing to 'xxx.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/window.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-window.sql.out
index 367dc4f513..9354d5e311 100644
--- a/sql/core/src/test/resources/sql-tests/results/window.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-window.sql.out
-21,10 +21,10 struct<>
-- !query 1
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY val ROWS CURRENT ROW) FROM testData
-ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY cate ORDER BY udf(val) ROWS CURRENT ROW) FROM testData
+ORDER BY cate, udf(val)
-- !query 1 schema
-struct<val:int,cate:string,count(val) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST ROWS BETWEEN CURRENT ROW AND CURRENT ROW):bigint>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,count(val) OVER (PARTITION BY cate ORDER BY CAST(udf(cast(val as string)) AS INT) ASC NULLS FIRST ROWS BETWEEN CURRENT ROW AND CURRENT ROW):bigint>
-- !query 1 output
NULL NULL 0
3 NULL 1
-38,10 +38,10 NULL a 0
-- !query 2
-SELECT val, cate, sum(val) OVER(PARTITION BY cate ORDER BY val
-ROWS BETWEEN UNBOUNDED PRECEDING AND 1 FOLLOWING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, sum(val) OVER(PARTITION BY cate ORDER BY udf(val)
+ROWS BETWEEN UNBOUNDED PRECEDING AND 1 FOLLOWING) FROM testData ORDER BY cate, udf(val)
-- !query 2 schema
-struct<val:int,cate:string,sum(val) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST ROWS BETWEEN UNBOUNDED PRECEDING AND 1 FOLLOWING):bigint>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,sum(val) OVER (PARTITION BY cate ORDER BY CAST(udf(cast(val as string)) AS INT) ASC NULLS FIRST ROWS BETWEEN UNBOUNDED PRECEDING AND 1 FOLLOWING):bigint>
-- !query 2 output
NULL NULL 3
3 NULL 3
-55,20 +55,20 NULL a 1
-- !query 3
-SELECT val_long, cate, sum(val_long) OVER(PARTITION BY cate ORDER BY val_long
-ROWS BETWEEN CURRENT ROW AND 2147483648 FOLLOWING) FROM testData ORDER BY cate, val_long
+SELECT val_long, udf(cate), sum(val_long) OVER(PARTITION BY cate ORDER BY udf(val_long)
+ROWS BETWEEN CURRENT ROW AND 2147483648 FOLLOWING) FROM testData ORDER BY udf(cate), val_long
-- !query 3 schema
struct<>
-- !query 3 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'ROWS BETWEEN CURRENT ROW AND 2147483648L FOLLOWING' due to data type mismatch: The data type of the upper bound 'bigint' does not match the expected data type 'int'.; line 1 pos 41
+cannot resolve 'ROWS BETWEEN CURRENT ROW AND 2147483648L FOLLOWING' due to data type mismatch: The data type of the upper bound 'bigint' does not match the expected data type 'int'.; line 1 pos 46
-- !query 4
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY val RANGE 1 PRECEDING) FROM testData
-ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate) ORDER BY val RANGE 1 PRECEDING) FROM testData
+ORDER BY cate, udf(val)
-- !query 4 schema
-struct<val:int,cate:string,count(val) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST RANGE BETWEEN 1 PRECEDING AND CURRENT ROW):bigint>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,count(val) OVER (PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY val ASC NULLS FIRST RANGE BETWEEN 1 PRECEDING AND CURRENT ROW):bigint>
-- !query 4 output
NULL NULL 0
3 NULL 1
-82,10 +82,10 NULL a 0
-- !query 5
-SELECT val, cate, sum(val) OVER(PARTITION BY cate ORDER BY val
-RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, val
+SELECT val, udf(cate), sum(val) OVER(PARTITION BY udf(cate) ORDER BY val
+RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY udf(cate), val
-- !query 5 schema
-struct<val:int,cate:string,sum(val) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING):bigint>
+struct<val:int,CAST(udf(cast(cate as string)) AS STRING):string,sum(val) OVER (PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY val ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING):bigint>
-- !query 5 output
NULL NULL NULL
3 NULL 3
-99,10 +99,10 NULL a NULL
-- !query 6
-SELECT val_long, cate, sum(val_long) OVER(PARTITION BY cate ORDER BY val_long
-RANGE BETWEEN CURRENT ROW AND 2147483648 FOLLOWING) FROM testData ORDER BY cate, val_long
+SELECT val_long, udf(cate), sum(val_long) OVER(PARTITION BY udf(cate) ORDER BY val_long
+RANGE BETWEEN CURRENT ROW AND 2147483648 FOLLOWING) FROM testData ORDER BY udf(cate), val_long
-- !query 6 schema
-struct<val_long:bigint,cate:string,sum(val_long) OVER (PARTITION BY cate ORDER BY val_long ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 2147483648 FOLLOWING):bigint>
+struct<val_long:bigint,CAST(udf(cast(cate as string)) AS STRING):string,sum(val_long) OVER (PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY val_long ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 2147483648 FOLLOWING):bigint>
-- !query 6 output
NULL NULL NULL
1 NULL 1
-116,10 +116,10 NULL b NULL
-- !query 7
-SELECT val_double, cate, sum(val_double) OVER(PARTITION BY cate ORDER BY val_double
-RANGE BETWEEN CURRENT ROW AND 2.5 FOLLOWING) FROM testData ORDER BY cate, val_double
+SELECT val_double, udf(cate), sum(val_double) OVER(PARTITION BY udf(cate) ORDER BY val_double
+RANGE BETWEEN CURRENT ROW AND 2.5 FOLLOWING) FROM testData ORDER BY udf(cate), val_double
-- !query 7 schema
-struct<val_double:double,cate:string,sum(val_double) OVER (PARTITION BY cate ORDER BY val_double ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND CAST(2.5 AS DOUBLE) FOLLOWING):double>
+struct<val_double:double,CAST(udf(cast(cate as string)) AS STRING):string,sum(val_double) OVER (PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY val_double ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND CAST(2.5 AS DOUBLE) FOLLOWING):double>
-- !query 7 output
NULL NULL NULL
1.0 NULL 1.0
-133,10 +133,10 NULL NULL NULL
-- !query 8
-SELECT val_date, cate, max(val_date) OVER(PARTITION BY cate ORDER BY val_date
-RANGE BETWEEN CURRENT ROW AND 2 FOLLOWING) FROM testData ORDER BY cate, val_date
+SELECT val_date, udf(cate), max(val_date) OVER(PARTITION BY udf(cate) ORDER BY val_date
+RANGE BETWEEN CURRENT ROW AND 2 FOLLOWING) FROM testData ORDER BY udf(cate), val_date
-- !query 8 schema
-struct<val_date:date,cate:string,max(val_date) OVER (PARTITION BY cate ORDER BY val_date ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 2 FOLLOWING):date>
+struct<val_date:date,CAST(udf(cast(cate as string)) AS STRING):string,max(val_date) OVER (PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY val_date ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 2 FOLLOWING):date>
-- !query 8 output
NULL NULL NULL
2017-08-01 NULL 2017-08-01
-150,11 +150,11 NULL NULL NULL
-- !query 9
-SELECT val_timestamp, cate, avg(val_timestamp) OVER(PARTITION BY cate ORDER BY val_timestamp
+SELECT val_timestamp, udf(cate), avg(val_timestamp) OVER(PARTITION BY udf(cate) ORDER BY val_timestamp
RANGE BETWEEN CURRENT ROW AND interval 23 days 4 hours FOLLOWING) FROM testData
-ORDER BY cate, val_timestamp
+ORDER BY udf(cate), val_timestamp
-- !query 9 schema
-struct<val_timestamp:timestamp,cate:string,avg(CAST(val_timestamp AS DOUBLE)) OVER (PARTITION BY cate ORDER BY val_timestamp ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND interval 3 weeks 2 days 4 hours FOLLOWING):double>
+struct<val_timestamp:timestamp,CAST(udf(cast(cate as string)) AS STRING):string,avg(CAST(val_timestamp AS DOUBLE)) OVER (PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY val_timestamp ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND interval 3 weeks 2 days 4 hours FOLLOWING):double>
-- !query 9 output
NULL NULL NULL
2017-07-31 17:00:00 NULL 1.5015456E9
-168,10 +168,10 NULL NULL NULL
-- !query 10
-SELECT val, cate, sum(val) OVER(PARTITION BY cate ORDER BY val DESC
+SELECT val, udf(cate), sum(val) OVER(PARTITION BY cate ORDER BY val DESC
RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, val
-- !query 10 schema
-struct<val:int,cate:string,sum(val) OVER (PARTITION BY cate ORDER BY val DESC NULLS LAST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING):bigint>
+struct<val:int,CAST(udf(cast(cate as string)) AS STRING):string,sum(val) OVER (PARTITION BY cate ORDER BY val DESC NULLS LAST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING):bigint>
-- !query 10 output
NULL NULL NULL
3 NULL 3
-185,58 +185,58 NULL a NULL
-- !query 11
-SELECT val, cate, count(val) OVER(PARTITION BY cate
-ROWS BETWEEN UNBOUNDED FOLLOWING AND 1 FOLLOWING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate)
+ROWS BETWEEN UNBOUNDED FOLLOWING AND 1 FOLLOWING) FROM testData ORDER BY cate, udf(val)
-- !query 11 schema
struct<>
-- !query 11 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'ROWS BETWEEN UNBOUNDED FOLLOWING AND 1 FOLLOWING' due to data type mismatch: Window frame upper bound '1' does not follow the lower bound 'unboundedfollowing$()'.; line 1 pos 33
+cannot resolve 'ROWS BETWEEN UNBOUNDED FOLLOWING AND 1 FOLLOWING' due to data type mismatch: Window frame upper bound '1' does not follow the lower bound 'unboundedfollowing$()'.; line 1 pos 38
-- !query 12
-SELECT val, cate, count(val) OVER(PARTITION BY cate
-RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate)
+RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, udf(val)
-- !query 12 schema
struct<>
-- !query 12 output
org.apache.spark.sql.AnalysisException
-cannot resolve '(PARTITION BY testdata.`cate` RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING)' due to data type mismatch: A range window frame cannot be used in an unordered window specification.; line 1 pos 33
+cannot resolve '(PARTITION BY CAST(udf(cast(cate as string)) AS STRING) RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING)' due to data type mismatch: A range window frame cannot be used in an unordered window specification.; line 1 pos 38
-- !query 13
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY val, cate
-RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate) ORDER BY udf(val), cate
+RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, udf(val)
-- !query 13 schema
struct<>
-- !query 13 output
org.apache.spark.sql.AnalysisException
-cannot resolve '(PARTITION BY testdata.`cate` ORDER BY testdata.`val` ASC NULLS FIRST, testdata.`cate` ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING)' due to data type mismatch: A range window frame with value boundaries cannot be used in a window specification with multiple order by expressions: val#x ASC NULLS FIRST,cate#x ASC NULLS FIRST; line 1 pos 33
+cannot resolve '(PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY CAST(udf(cast(val as string)) AS INT) ASC NULLS FIRST, testdata.`cate` ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING)' due to data type mismatch: A range window frame with value boundaries cannot be used in a window specification with multiple order by expressions: cast(udf(cast(val#x as string)) as int) ASC NULLS FIRST,cate#x ASC NULLS FIRST; line 1 pos 38
-- !query 14
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY current_timestamp
-RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate) ORDER BY current_timestamp
+RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM testData ORDER BY cate, udf(val)
-- !query 14 schema
struct<>
-- !query 14 output
org.apache.spark.sql.AnalysisException
-cannot resolve '(PARTITION BY testdata.`cate` ORDER BY current_timestamp() ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING)' due to data type mismatch: The data type 'timestamp' used in the order specification does not match the data type 'int' which is used in the range frame.; line 1 pos 33
+cannot resolve '(PARTITION BY CAST(udf(cast(cate as string)) AS STRING) ORDER BY current_timestamp() ASC NULLS FIRST RANGE BETWEEN CURRENT ROW AND 1 FOLLOWING)' due to data type mismatch: The data type 'timestamp' used in the order specification does not match the data type 'int' which is used in the range frame.; line 1 pos 38
-- !query 15
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY val
-RANGE BETWEEN 1 FOLLOWING AND 1 PRECEDING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate) ORDER BY val
+RANGE BETWEEN 1 FOLLOWING AND 1 PRECEDING) FROM testData ORDER BY udf(cate), val
-- !query 15 schema
struct<>
-- !query 15 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'RANGE BETWEEN 1 FOLLOWING AND 1 PRECEDING' due to data type mismatch: The lower bound of a window frame must be less than or equal to the upper bound; line 1 pos 33
+cannot resolve 'RANGE BETWEEN 1 FOLLOWING AND 1 PRECEDING' due to data type mismatch: The lower bound of a window frame must be less than or equal to the upper bound; line 1 pos 38
-- !query 16
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY val
-RANGE BETWEEN CURRENT ROW AND current_date PRECEDING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate) ORDER BY udf(val)
+RANGE BETWEEN CURRENT ROW AND current_date PRECEDING) FROM testData ORDER BY cate, val(val)
-- !query 16 schema
struct<>
-- !query 16 output
-245,48 +245,48 org.apache.spark.sql.catalyst.parser.ParseException
Frame bound value must be a literal.(line 2, pos 30)
== SQL ==
-SELECT val, cate, count(val) OVER(PARTITION BY cate ORDER BY val
-RANGE BETWEEN CURRENT ROW AND current_date PRECEDING) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, count(val) OVER(PARTITION BY udf(cate) ORDER BY udf(val)
+RANGE BETWEEN CURRENT ROW AND current_date PRECEDING) FROM testData ORDER BY cate, val(val)
------------------------------^^^
-- !query 17
-SELECT val, cate,
-max(val) OVER w AS max,
-min(val) OVER w AS min,
-min(val) OVER w AS min,
-count(val) OVER w AS count,
-sum(val) OVER w AS sum,
-avg(val) OVER w AS avg,
-stddev(val) OVER w AS stddev,
-first_value(val) OVER w AS first_value,
-first_value(val, true) OVER w AS first_value_ignore_null,
-first_value(val, false) OVER w AS first_value_contain_null,
-last_value(val) OVER w AS last_value,
-last_value(val, true) OVER w AS last_value_ignore_null,
-last_value(val, false) OVER w AS last_value_contain_null,
+SELECT udf(val), cate,
+max(udf(val)) OVER w AS max,
+min(udf(val)) OVER w AS min,
+min(udf(val)) OVER w AS min,
+count(udf(val)) OVER w AS count,
+sum(udf(val)) OVER w AS sum,
+avg(udf(val)) OVER w AS avg,
+stddev(udf(val)) OVER w AS stddev,
+first_value(udf(val)) OVER w AS first_value,
+first_value(udf(val), true) OVER w AS first_value_ignore_null,
+first_value(udf(val), false) OVER w AS first_value_contain_null,
+last_value(udf(val)) OVER w AS last_value,
+last_value(udf(val), true) OVER w AS last_value_ignore_null,
+last_value(udf(val), false) OVER w AS last_value_contain_null,
rank() OVER w AS rank,
dense_rank() OVER w AS dense_rank,
cume_dist() OVER w AS cume_dist,
percent_rank() OVER w AS percent_rank,
ntile(2) OVER w AS ntile,
row_number() OVER w AS row_number,
-var_pop(val) OVER w AS var_pop,
-var_samp(val) OVER w AS var_samp,
-approx_count_distinct(val) OVER w AS approx_count_distinct,
-covar_pop(val, val_long) OVER w AS covar_pop,
-corr(val, val_long) OVER w AS corr,
-stddev_samp(val) OVER w AS stddev_samp,
-stddev_pop(val) OVER w AS stddev_pop,
-collect_list(val) OVER w AS collect_list,
-collect_set(val) OVER w AS collect_set,
-skewness(val_double) OVER w AS skewness,
-kurtosis(val_double) OVER w AS kurtosis
+var_pop(udf(val)) OVER w AS var_pop,
+var_samp(udf(val)) OVER w AS var_samp,
+approx_count_distinct(udf(val)) OVER w AS approx_count_distinct,
+covar_pop(udf(val), udf(val_long)) OVER w AS covar_pop,
+corr(udf(val), udf(val_long)) OVER w AS corr,
+stddev_samp(udf(val)) OVER w AS stddev_samp,
+stddev_pop(udf(val)) OVER w AS stddev_pop,
+collect_list(udf(val)) OVER w AS collect_list,
+collect_set(udf(val)) OVER w AS collect_set,
+skewness(udf(val_double)) OVER w AS skewness,
+kurtosis(udf(val_double)) OVER w AS kurtosis
FROM testData
-WINDOW w AS (PARTITION BY cate ORDER BY val)
-ORDER BY cate, val
+WINDOW w AS (PARTITION BY udf(cate) ORDER BY udf(val))
+ORDER BY cate, udf(val)
-- !query 17 schema
-struct<val:int,cate:string,max:int,min:int,min:int,count:bigint,sum:bigint,avg:double,stddev:double,first_value:int,first_value_ignore_null:int,first_value_contain_null:int,last_value:int,last_value_ignore_null:int,last_value_contain_null:int,rank:int,dense_rank:int,cume_dist:double,percent_rank:double,ntile:int,row_number:int,var_pop:double,var_samp:double,approx_count_distinct:bigint,covar_pop:double,corr:double,stddev_samp:double,stddev_pop:double,collect_list:array<int>,collect_set:array<int>,skewness:double,kurtosis:double>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,max:int,min:int,min:int,count:bigint,sum:bigint,avg:double,stddev:double,first_value:int,first_value_ignore_null:int,first_value_contain_null:int,last_value:int,last_value_ignore_null:int,last_value_contain_null:int,rank:int,dense_rank:int,cume_dist:double,percent_rank:double,ntile:int,row_number:int,var_pop:double,var_samp:double,approx_count_distinct:bigint,covar_pop:double,corr:double,stddev_samp:double,stddev_pop:double,collect_list:array<int>,collect_set:array<int>,skewness:double,kurtosis:double>
-- !query 17 output
NULL NULL NULL NULL NULL 0 NULL NULL NULL NULL NULL NULL NULL NULL NULL 1 1 0.5 0.0 1 1 NULL NULL 0 NULL NULL
NULL NULL [] [] NULL NULL
3 NULL 3 3 3 1 3 3.0 NaN NULL 3 NULL 3 3 3 2 2 1.0 1.0 2 2 0.0 NaN 1 0.0 NaN
NaN 0.0 [3] [3] NaN NaN
-300,9 +300,9 NULL a NULL NULL NULL 0 NULL NULL NULL NULL NULL NULL NULL NULL NULL 1 1 0.25 0.
-- !query 18
-SELECT val, cate, avg(null) OVER(PARTITION BY cate ORDER BY val) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, avg(null) OVER(PARTITION BY cate ORDER BY val) FROM testData ORDER BY cate, val
-- !query 18 schema
-struct<val:int,cate:string,avg(CAST(NULL AS DOUBLE)) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):double>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,avg(CAST(NULL AS DOUBLE)) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):double>
-- !query 18 output
NULL NULL NULL
3 NULL NULL
-316,7 +316,7 NULL a NULL
-- !query 19
-SELECT val, cate, row_number() OVER(PARTITION BY cate) FROM testData ORDER BY cate, val
+SELECT udf(val), cate, row_number() OVER(PARTITION BY cate) FROM testData ORDER BY cate, udf(val)
-- !query 19 schema
struct<>
-- !query 19 output
-325,9 +325,9 Window function row_number() requires window to be ordered, please add ORDER BY
-- !query 20
-SELECT val, cate, sum(val) OVER(), avg(val) OVER() FROM testData ORDER BY cate, val
+SELECT udf(val), cate, sum(val) OVER(), avg(val) OVER() FROM testData ORDER BY cate, val
-- !query 20 schema
-struct<val:int,cate:string,sum(CAST(val AS BIGINT)) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING):bigint,avg(CAST(val AS BIGINT)) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING):double>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,sum(CAST(val AS BIGINT)) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING):bigint,avg(CAST(val AS BIGINT)) OVER (ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING):double>
-- !query 20 output
NULL NULL 13 1.8571428571428572
3 NULL 13 1.8571428571428572
-341,7 +341,7 NULL a 13 1.8571428571428572
-- !query 21
-SELECT val, cate,
+SELECT udf(val), cate,
first_value(false) OVER w AS first_value,
first_value(true, true) OVER w AS first_value_ignore_null,
first_value(false, false) OVER w AS first_value_contain_null,
-352,7 +352,7 FROM testData
WINDOW w AS ()
ORDER BY cate, val
-- !query 21 schema
-struct<val:int,cate:string,first_value:boolean,first_value_ignore_null:boolean,first_value_contain_null:boolean,last_value:boolean,last_value_ignore_null:boolean,last_value_contain_null:boolean>
+struct<CAST(udf(cast(val as string)) AS INT):int,cate:string,first_value:boolean,first_value_ignore_null:boolean,first_value_contain_null:boolean,last_value:boolean,last_value_ignore_null:boolean,last_value_contain_null:boolean>
-- !query 21 output
NULL NULL false true false false true false
3 NULL false true false false true false
-366,12 +366,12 NULL a false true false false true false
-- !query 22
-SELECT cate, sum(val) OVER (w)
+SELECT udf(cate), sum(val) OVER (w)
FROM testData
WHERE val is not null
WINDOW w AS (PARTITION BY cate ORDER BY val)
-- !query 22 schema
-struct<cate:string,sum(CAST(val AS BIGINT)) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):bigint>
+struct<CAST(udf(cast(cate as string)) AS STRING):string,sum(CAST(val AS BIGINT)) OVER (PARTITION BY cate ORDER BY val ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):bigint>
-- !query 22 output
NULL 3
a 2
```
</p>
</details>
## How was this patch tested?
Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
Closes#25195 from younggyuchun/master.
Authored-by: younggyu chun <younggyuchun@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the current implementation. complex types like Array/Map/StructType are allowed to upcast as StringType.
This is not safe casting. We should disallow it.
## How was this patch tested?
Update the existing test case
Closes#25242 from gengliangwang/fixUpCastStringType.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
changed the test according to steps mentioned in SPARK-27921
<details>
<summary>difference comparing to select_having.sql</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/pgSQL/select_having.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-select_having.sql.out
index 02536eb..f731d11 100644
--- a/sql/core/src/test/resources/sql-tests/results/pgSQL/select_having.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-select_having.sql.out
-91,54 +91,54 struct<>
-- !query 11
-SELECT b, c FROM test_having
- GROUP BY b, c HAVING count(*) = 1 ORDER BY b, c
+SELECT udf(b), udf(c) FROM test_having
+ GROUP BY b, c HAVING udf(count(*)) = 1 ORDER BY udf(b), udf(c)
-- !query 11 schema
-struct<b:int,c:string>
+struct<CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(c as string)) AS STRING):string>
-- !query 11 output
1 XXXX
3 bbbb
-- !query 12
-SELECT b, c FROM test_having
- GROUP BY b, c HAVING b = 3 ORDER BY b, c
+SELECT udf(b), udf(c) FROM test_having
+ GROUP BY b, c HAVING udf(b) = 3 ORDER BY udf(b), udf(c)
-- !query 12 schema
-struct<b:int,c:string>
+struct<CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(c as string)) AS STRING):string>
-- !query 12 output
3 BBBB
3 bbbb
-- !query 13
-SELECT c, max(a) FROM test_having
- GROUP BY c HAVING count(*) > 2 OR min(a) = max(a)
+SELECT udf(c), max(udf(a)) FROM test_having
+ GROUP BY c HAVING udf(count(*)) > 2 OR udf(min(a)) = udf(max(a))
ORDER BY c
-- !query 13 schema
-struct<c:string,max(a):int>
+struct<CAST(udf(cast(c as string)) AS STRING):string,max(CAST(udf(cast(a as string)) AS INT)):int>
-- !query 13 output
XXXX 0
bbbb 5
-- !query 14
-SELECT min(a), max(a) FROM test_having HAVING min(a) = max(a)
+SELECT udf(udf(min(udf(a)))), udf(udf(max(udf(a)))) FROM test_having HAVING udf(udf(min(udf(a)))) = udf(udf(max(udf(a))))
-- !query 14 schema
-struct<min(a):int,max(a):int>
+struct<CAST(udf(cast(cast(udf(cast(min(cast(udf(cast(a as string)) as int)) as string)) as int) as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(max(cast(udf(cast(a as string)) as int)) as string)) as int) as string)) AS INT):int>
-- !query 14 output
-- !query 15
-SELECT min(a), max(a) FROM test_having HAVING min(a) < max(a)
+SELECT udf(min(udf(a))), udf(udf(max(a))) FROM test_having HAVING udf(min(a)) < udf(max(udf(a)))
-- !query 15 schema
-struct<min(a):int,max(a):int>
+struct<CAST(udf(cast(min(cast(udf(cast(a as string)) as int)) as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(max(a) as string)) as int) as string)) AS INT):int>
-- !query 15 output
0 9
-- !query 16
-SELECT a FROM test_having HAVING min(a) < max(a)
+SELECT udf(a) FROM test_having HAVING udf(min(a)) < udf(max(a))
-- !query 16 schema
struct<>
-- !query 16 output
-147,16 +147,16 grouping expressions sequence is empty, and 'default.test_having.`a`' is not an
-- !query 17
-SELECT 1 AS one FROM test_having HAVING a > 1
+SELECT 1 AS one FROM test_having HAVING udf(a) > 1
-- !query 17 schema
struct<>
-- !query 17 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`a`' given input columns: [one]; line 1 pos 40
+cannot resolve '`a`' given input columns: [one]; line 1 pos 44
-- !query 18
-SELECT 1 AS one FROM test_having HAVING 1 > 2
+SELECT 1 AS one FROM test_having HAVING udf(udf(1) > udf(2))
-- !query 18 schema
struct<one:int>
-- !query 18 output
-164,7 +164,7 struct<one:int>
-- !query 19
-SELECT 1 AS one FROM test_having HAVING 1 < 2
+SELECT 1 AS one FROM test_having HAVING udf(udf(1) < udf(2))
-- !query 19 schema
struct<one:int>
-- !query 19 output
-172,7 +172,7 struct<one:int>
-- !query 20
-SELECT 1 AS one FROM test_having WHERE 1/a = 1 HAVING 1 < 2
+SELECT 1 AS one FROM test_having WHERE 1/udf(a) = 1 HAVING 1 < 2
-- !query 20 schema
struct<one:int>
-- !query 20 output
```
</p>
</details>
## How was this patch tested?
by:
```bash
sudo SPARK_GENERATE_GOLDEN_FILES=1 build/sbt "sql/test-only *SQLQueryTestSuite -- -z udf/pgSQL/udf-select_having.sql"
```
Closes#25161 from shivusondur/jira28390.
Authored-by: shivusondur <shivusondur@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/7355 add support casting between IntervalType and StringType for scala interface:
```scala
import org.apache.spark.sql.types._
import org.apache.spark.sql.catalyst.expressions._
Cast(Literal("interval 3 month 1 hours"), CalendarIntervalType).eval()
res0: Any = interval 3 months 1 hours
```
But SQL interface does not support it:
```sql
scala> spark.sql("SELECT CAST('interval 3 month 1 hour' AS interval)").show
org.apache.spark.sql.catalyst.parser.ParseException:
DataType interval is not supported.(line 1, pos 41)
== SQL ==
SELECT CAST('interval 3 month 1 hour' AS interval)
-----------------------------------------^^^
at org.apache.spark.sql.catalyst.parser.AstBuilder.$anonfun$visitPrimitiveDataType$1(AstBuilder.scala:1931)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:108)
at org.apache.spark.sql.catalyst.parser.AstBuilder.visitPrimitiveDataType(AstBuilder.scala:1909)
at org.apache.spark.sql.catalyst.parser.AstBuilder.visitPrimitiveDataType(AstBuilder.scala:52)
...
```
This PR add supports accepting the `interval` keyword in the schema string. So that SQL interface can support this feature.
## How was this patch tested?
unit tests
Closes#25189 from wangyum/SPARK-28435.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR proposes to use `InheritableThreadLocal` instead of `ThreadLocal` for current epoch in `EpochTracker`. Python UDF needs threads to write out to and read it from Python processes and when there are new threads, previously set epoch is lost.
After this PR, Python UDFs can be used at Structured Streaming with the continuous mode.
## How was this patch tested?
The test cases were written on the top of https://github.com/apache/spark/pull/24945.
Unit tests were added.
Manual tests.
Closes#24946 from HyukjinKwon/SPARK-27234.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR adds some tests converted from 'pgSQL/select_implicit.sql' to test UDFs
<details><summary>Diff comparing to 'pgSQL/select_implicit.sql'</summary>
<p>
```diff
... diff --git a/sql/core/src/test/resources/sql-tests/results/pgSQL/select_implicit.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-select_implicit.sql.out
index 0675820..e6a5995 100755
--- a/sql/core/src/test/resources/sql-tests/results/pgSQL/select_implicit.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-select_implicit.sql.out
-91,9 +91,11 struct<>
-- !query 11
-SELECT c, count(*) FROM test_missing_target GROUP BY test_missing_target.c ORDER BY c
+SELECT udf(c), udf(count(*)) FROM test_missing_target GROUP BY
+test_missing_target.c
+ORDER BY udf(c)
-- !query 11 schema
-struct<c:string,count(1):bigint>
+struct<CAST(udf(cast(c as string)) AS STRING):string,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 11 output
ABAB 2
BBBB 2
-104,9 +106,10 cccc 2
-- !query 12
-SELECT count(*) FROM test_missing_target GROUP BY test_missing_target.c ORDER BY c
+SELECT udf(count(*)) FROM test_missing_target GROUP BY test_missing_target.c
+ORDER BY udf(c)
-- !query 12 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 12 output
2
2
-117,18 +120,18 struct<count(1):bigint>
-- !query 13
-SELECT count(*) FROM test_missing_target GROUP BY a ORDER BY b
+SELECT udf(count(*)) FROM test_missing_target GROUP BY a ORDER BY udf(b)
-- !query 13 schema
struct<>
-- !query 13 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`b`' given input columns: [count(1)]; line 1 pos 61
+cannot resolve '`b`' given input columns: [CAST(udf(cast(count(1) as string)) AS BIGINT)]; line 1 pos 70
-- !query 14
-SELECT count(*) FROM test_missing_target GROUP BY b ORDER BY b
+SELECT udf(count(*)) FROM test_missing_target GROUP BY b ORDER BY udf(b)
-- !query 14 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 14 output
1
2
-137,10 +140,10 struct<count(1):bigint>
-- !query 15
-SELECT test_missing_target.b, count(*)
- FROM test_missing_target GROUP BY b ORDER BY b
+SELECT udf(test_missing_target.b), udf(count(*))
+ FROM test_missing_target GROUP BY b ORDER BY udf(b)
-- !query 15 schema
-struct<b:int,count(1):bigint>
+struct<CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 15 output
1 1
2 2
-149,9 +152,9 struct<b:int,count(1):bigint>
-- !query 16
-SELECT c FROM test_missing_target ORDER BY a
+SELECT udf(c) FROM test_missing_target ORDER BY udf(a)
-- !query 16 schema
-struct<c:string>
+struct<CAST(udf(cast(c as string)) AS STRING):string>
-- !query 16 output
XXXX
ABAB
-166,9 +169,9 CCCC
-- !query 17
-SELECT count(*) FROM test_missing_target GROUP BY b ORDER BY b desc
+SELECT udf(count(*)) FROM test_missing_target GROUP BY b ORDER BY udf(b) desc
-- !query 17 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 17 output
4
3
-177,17 +180,17 struct<count(1):bigint>
-- !query 18
-SELECT count(*) FROM test_missing_target ORDER BY 1 desc
+SELECT udf(count(*)) FROM test_missing_target ORDER BY udf(1) desc
-- !query 18 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 18 output
10
-- !query 19
-SELECT c, count(*) FROM test_missing_target GROUP BY 1 ORDER BY 1
+SELECT udf(c), udf(count(*)) FROM test_missing_target GROUP BY 1 ORDER BY 1
-- !query 19 schema
-struct<c:string,count(1):bigint>
+struct<CAST(udf(cast(c as string)) AS STRING):string,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 19 output
ABAB 2
BBBB 2
-198,18 +201,18 cccc 2
-- !query 20
-SELECT c, count(*) FROM test_missing_target GROUP BY 3
+SELECT udf(c), udf(count(*)) FROM test_missing_target GROUP BY 3
-- !query 20 schema
struct<>
-- !query 20 output
org.apache.spark.sql.AnalysisException
-GROUP BY position 3 is not in select list (valid range is [1, 2]); line 1 pos 53
+GROUP BY position 3 is not in select list (valid range is [1, 2]); line 1 pos 63
-- !query 21
-SELECT count(*) FROM test_missing_target x, test_missing_target y
- WHERE x.a = y.a
- GROUP BY b ORDER BY b
+SELECT udf(count(*)) FROM test_missing_target x, test_missing_target y
+ WHERE udf(x.a) = udf(y.a)
+ GROUP BY b ORDER BY udf(b)
-- !query 21 schema
struct<>
-- !query 21 output
-218,10 +221,10 Reference 'b' is ambiguous, could be: x.b, y.b.; line 3 pos 10
-- !query 22
-SELECT a, a FROM test_missing_target
- ORDER BY a
+SELECT udf(a), udf(a) FROM test_missing_target
+ ORDER BY udf(a)
-- !query 22 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(a as string)) AS INT):int>
-- !query 22 output
0 0
1 1
-236,10 +239,10 struct<a:int,a:int>
-- !query 23
-SELECT a/2, a/2 FROM test_missing_target
- ORDER BY a/2
+SELECT udf(udf(a)/2), udf(udf(a)/2) FROM test_missing_target
+ ORDER BY udf(udf(a)/2)
-- !query 23 schema
-struct<(a div 2):int,(a div 2):int>
+struct<CAST(udf(cast((cast(udf(cast(a as string)) as int) div 2) as string)) AS INT):int,CAST(udf(cast((cast(udf(cast(a as string)) as int) div 2) as string)) AS INT):int>
-- !query 23 output
0 0
0 0
-254,10 +257,10 struct<(a div 2):int,(a div 2):int>
-- !query 24
-SELECT a/2, a/2 FROM test_missing_target
- GROUP BY a/2 ORDER BY a/2
+SELECT udf(a/2), udf(a/2) FROM test_missing_target
+ GROUP BY a/2 ORDER BY udf(a/2)
-- !query 24 schema
-struct<(a div 2):int,(a div 2):int>
+struct<CAST(udf(cast((a div 2) as string)) AS INT):int,CAST(udf(cast((a div 2) as string)) AS INT):int>
-- !query 24 output
0 0
1 1
-267,11 +270,11 struct<(a div 2):int,(a div 2):int>
-- !query 25
-SELECT x.b, count(*) FROM test_missing_target x, test_missing_target y
- WHERE x.a = y.a
- GROUP BY x.b ORDER BY x.b
+SELECT udf(x.b), udf(count(*)) FROM test_missing_target x, test_missing_target y
+ WHERE udf(x.a) = udf(y.a)
+ GROUP BY x.b ORDER BY udf(x.b)
-- !query 25 schema
-struct<b:int,count(1):bigint>
+struct<CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 25 output
1 1
2 2
-280,11 +283,11 struct<b:int,count(1):bigint>
-- !query 26
-SELECT count(*) FROM test_missing_target x, test_missing_target y
- WHERE x.a = y.a
- GROUP BY x.b ORDER BY x.b
+SELECT udf(count(*)) FROM test_missing_target x, test_missing_target y
+ WHERE udf(x.a) = udf(y.a)
+ GROUP BY x.b ORDER BY udf(x.b)
-- !query 26 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 26 output
1
2
-293,22 +296,22 struct<count(1):bigint>
-- !query 27
-SELECT a%2, count(b) FROM test_missing_target
+SELECT a%2, udf(count(udf(b))) FROM test_missing_target
GROUP BY test_missing_target.a%2
-ORDER BY test_missing_target.a%2
+ORDER BY udf(test_missing_target.a%2)
-- !query 27 schema
-struct<(a % 2):int,count(b):bigint>
+struct<(a % 2):int,CAST(udf(cast(count(cast(udf(cast(b as string)) as int)) as string)) AS BIGINT):bigint>
-- !query 27 output
0 5
1 5
-- !query 28
-SELECT count(c) FROM test_missing_target
+SELECT udf(count(c)) FROM test_missing_target
GROUP BY lower(test_missing_target.c)
-ORDER BY lower(test_missing_target.c)
+ORDER BY udf(lower(test_missing_target.c))
-- !query 28 schema
-struct<count(c):bigint>
+struct<CAST(udf(cast(count(c) as string)) AS BIGINT):bigint>
-- !query 28 output
2
3
-317,18 +320,18 struct<count(c):bigint>
-- !query 29
-SELECT count(a) FROM test_missing_target GROUP BY a ORDER BY b
+SELECT udf(count(udf(a))) FROM test_missing_target GROUP BY a ORDER BY udf(b)
-- !query 29 schema
struct<>
-- !query 29 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`b`' given input columns: [count(a)]; line 1 pos 61
+cannot resolve '`b`' given input columns: [CAST(udf(cast(count(cast(udf(cast(a as string)) as int)) as string)) AS BIGINT)]; line 1 pos 75
-- !query 30
-SELECT count(b) FROM test_missing_target GROUP BY b/2 ORDER BY b/2
+SELECT udf(count(b)) FROM test_missing_target GROUP BY b/2 ORDER BY udf(b/2)
-- !query 30 schema
-struct<count(b):bigint>
+struct<CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
-- !query 30 output
1
5
-336,10 +339,10 struct<count(b):bigint>
-- !query 31
-SELECT lower(test_missing_target.c), count(c)
- FROM test_missing_target GROUP BY lower(c) ORDER BY lower(c)
+SELECT udf(lower(test_missing_target.c)), udf(count(udf(c)))
+ FROM test_missing_target GROUP BY lower(c) ORDER BY udf(lower(c))
-- !query 31 schema
-struct<lower(c):string,count(c):bigint>
+struct<CAST(udf(cast(lower(c) as string)) AS STRING):string,CAST(udf(cast(count(cast(udf(cast(c as string)) as string)) as string)) AS BIGINT):bigint>
-- !query 31 output
abab 2
bbbb 3
-348,9 +351,9 xxxx 1
-- !query 32
-SELECT a FROM test_missing_target ORDER BY upper(d)
+SELECT udf(a) FROM test_missing_target ORDER BY udf(upper(udf(d)))
-- !query 32 schema
-struct<a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int>
-- !query 32 output
0
1
-365,19 +368,19 struct<a:int>
-- !query 33
-SELECT count(b) FROM test_missing_target
- GROUP BY (b + 1) / 2 ORDER BY (b + 1) / 2 desc
+SELECT udf(count(b)) FROM test_missing_target
+ GROUP BY (b + 1) / 2 ORDER BY udf((b + 1) / 2) desc
-- !query 33 schema
-struct<count(b):bigint>
+struct<CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
-- !query 33 output
7
3
-- !query 34
-SELECT count(x.a) FROM test_missing_target x, test_missing_target y
- WHERE x.a = y.a
- GROUP BY b/2 ORDER BY b/2
+SELECT udf(count(udf(x.a))) FROM test_missing_target x, test_missing_target y
+ WHERE udf(x.a) = udf(y.a)
+ GROUP BY b/2 ORDER BY udf(b/2)
-- !query 34 schema
struct<>
-- !query 34 output
-386,11 +389,12 Reference 'b' is ambiguous, could be: x.b, y.b.; line 3 pos 10
-- !query 35
-SELECT x.b/2, count(x.b) FROM test_missing_target x, test_missing_target y
- WHERE x.a = y.a
- GROUP BY x.b/2 ORDER BY x.b/2
+SELECT udf(x.b/2), udf(count(udf(x.b))) FROM test_missing_target x,
+test_missing_target y
+ WHERE udf(x.a) = udf(y.a)
+ GROUP BY x.b/2 ORDER BY udf(x.b/2)
-- !query 35 schema
-struct<(b div 2):int,count(b):bigint>
+struct<CAST(udf(cast((b div 2) as string)) AS INT):int,CAST(udf(cast(count(cast(udf(cast(b as string)) as int)) as string)) AS BIGINT):bigint>
-- !query 35 output
0 1
1 5
-398,14 +402,14 struct<(b div 2):int,count(b):bigint>
-- !query 36
-SELECT count(b) FROM test_missing_target x, test_missing_target y
- WHERE x.a = y.a
+SELECT udf(count(udf(b))) FROM test_missing_target x, test_missing_target y
+ WHERE udf(x.a) = udf(y.a)
GROUP BY x.b/2
-- !query 36 schema
struct<>
-- !query 36 output
org.apache.spark.sql.AnalysisException
-Reference 'b' is ambiguous, could be: x.b, y.b.; line 1 pos 13
+Reference 'b' is ambiguous, could be: x.b, y.b.; line 1 pos 21
-- !query 37
```
</p>
</details>
## How was this patch tested?
Tested as Guided in SPARK-27921
Closes#25233 from Udbhav30/master.
Authored-by: Udbhav30 <u.agrawal30@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
query plan was designed to be immutable, but sometimes we do allow it to carry mutable states, because of the complexity of the SQL system. One example is `TreeNodeTag`. It's a state of `TreeNode` and can be carried over during copy and transform. The adaptive execution framework relies on it to link the logical and physical plans.
This leads to a problem: when we get `QueryExecution#analyzed`, the plan can be changed unexpectedly because it's mutable. I hit a real issue in https://github.com/apache/spark/pull/25107 : I use `TreeNodeTag` to carry dataset id in logical plans. However, the analyzed plan ends up with many duplicated dataset id tags in different nodes. It turns out that, the optimizer transforms the logical plan and add the tag to more nodes.
For example, the logical plan is `SubqueryAlias(Filter(...))`, and I expect only the `SubqueryAlais` has the dataset id tag. However, the optimizer removes `SubqueryAlias` and carries over the dataset id tag to `Filter`. When I go back to the analyzed plan, both `SubqueryAlias` and `Filter` has the dataset id tag, which breaks my assumption.
Since now query plan is mutable, I think it's better to limit the life cycle of a query plan instance. We can clone the query plan between analyzer, optimizer and planner, so that the life cycle is limited in one stage.
## How was this patch tested?
new test
Closes#25111 from cloud-fan/clone.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR change `CalendarIntervalType`'s readable string representation from `calendarinterval` to `interval`.
## How was this patch tested?
Existing UT
Closes#25225 from wangyum/SPARK-28469.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Fix CSV datasource to throw `com.univocity.parsers.common.TextParsingException` with large size message, which will make log output consume large disk space.
This issue is troublesome when sometimes we need parse CSV with large size column.
This PR proposes to set CSV parser/writer settings by `setErrorContentLength(1000)` to limit the error message length.
## How was this patch tested?
Manually.
```
val s = "a" * 40 * 1000000
Seq(s).toDF.write.mode("overwrite").csv("/tmp/bogdan/es4196.csv")
spark.read .option("maxCharsPerColumn", 30000000) .csv("/tmp/bogdan/es4196.csv").count
```
**Before:**
The thrown message will include error content of about 30MB size (The column size exceed the max value 30MB, so the error content include the whole parsed content, so it is 30MB).
**After:**
The thrown message will include error content like "...aaa...aa" (the number of 'a' is 1024), i.e. limit the content size to be 1024.
Closes#25184 from WeichenXu123/limit_csv_exception_size.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
New function `make_date()` takes 3 columns `year`, `month` and `day`, and makes new column of the `DATE` type. If values in the input columns are `null` or out of valid ranges, the function returns `null`. Valid ranges are:
- `year` - `[1, 9999]`
- `month` - `[1, 12]`
- `day` - `[1, 31]`
Also constructed date must be valid otherwise `make_date` returns `null`.
The function is implemented similarly to `make_date` in PostgreSQL: https://www.postgresql.org/docs/11/functions-datetime.html to maintain feature parity with it.
Here is an example:
```sql
select make_date(2013, 7, 15);
2013-07-15
```
## How was this patch tested?
Added new tests to `DateExpressionsSuite`.
Closes#25210 from MaxGekk/make_date-timestamp.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR adds some tests converted from `group-by.sql` to test UDFs. Please see contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
<details><summary>Diff comparing to 'group-by.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/udf/udf-group-by.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-group-by.sql.out
index 3a5df254f2..0118c05b1d 100644
--- a/sql/core/src/test/resources/sql-tests/results/udf/udf-group-by.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-group-by.sql.out
-13,26 +13,26 struct<>
-- !query 1
-SELECT a, COUNT(b) FROM testData
+SELECT udf(a), udf(COUNT(b)) FROM testData
-- !query 1 schema
struct<>
-- !query 1 output
org.apache.spark.sql.AnalysisException
-grouping expressions sequence is empty, and 'testdata.`a`' is not an aggregate function. Wrap '(count(testdata.`b`) AS `count(b)`)' in windowing function(s) or wrap 'testdata.`a`' in first() (or first_value) if you don't care which value you get.;
+grouping expressions sequence is empty, and 'testdata.`a`' is not an aggregate function. Wrap '(CAST(udf(cast(count(b) as string)) AS BIGINT) AS `CAST(udf(cast(count(b) as string)) AS BIGINT)`)' in windowing function(s) or wrap 'testdata.`a`' in first() (or first_value) if you don't care which value you get.;
-- !query 2
-SELECT COUNT(a), COUNT(b) FROM testData
+SELECT COUNT(udf(a)), udf(COUNT(b)) FROM testData
-- !query 2 schema
-struct<count(a):bigint,count(b):bigint>
+struct<count(CAST(udf(cast(a as string)) AS INT)):bigint,CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
-- !query 2 output
7 7
-- !query 3
-SELECT a, COUNT(b) FROM testData GROUP BY a
+SELECT udf(a), COUNT(udf(b)) FROM testData GROUP BY a
-- !query 3 schema
-struct<a:int,count(b):bigint>
+struct<CAST(udf(cast(a as string)) AS INT):int,count(CAST(udf(cast(b as string)) AS INT)):bigint>
-- !query 3 output
1 2
2 2
-41,7 +41,7 NULL 1
-- !query 4
-SELECT a, COUNT(b) FROM testData GROUP BY b
+SELECT udf(a), udf(COUNT(udf(b))) FROM testData GROUP BY b
-- !query 4 schema
struct<>
-- !query 4 output
-50,9 +50,9 expression 'testdata.`a`' is neither present in the group by, nor is it an aggre
-- !query 5
-SELECT COUNT(a), COUNT(b) FROM testData GROUP BY a
+SELECT COUNT(udf(a)), COUNT(udf(b)) FROM testData GROUP BY udf(a)
-- !query 5 schema
-struct<count(a):bigint,count(b):bigint>
+struct<count(CAST(udf(cast(a as string)) AS INT)):bigint,count(CAST(udf(cast(b as string)) AS INT)):bigint>
-- !query 5 output
0 1
2 2
-61,15 +61,15 struct<count(a):bigint,count(b):bigint>
-- !query 6
-SELECT 'foo', COUNT(a) FROM testData GROUP BY 1
+SELECT 'foo', COUNT(udf(a)) FROM testData GROUP BY 1
-- !query 6 schema
-struct<foo:string,count(a):bigint>
+struct<foo:string,count(CAST(udf(cast(a as string)) AS INT)):bigint>
-- !query 6 output
foo 7
-- !query 7
-SELECT 'foo' FROM testData WHERE a = 0 GROUP BY 1
+SELECT 'foo' FROM testData WHERE a = 0 GROUP BY udf(1)
-- !query 7 schema
struct<foo:string>
-- !query 7 output
-77,25 +77,25 struct<foo:string>
-- !query 8
-SELECT 'foo', APPROX_COUNT_DISTINCT(a) FROM testData WHERE a = 0 GROUP BY 1
+SELECT 'foo', udf(APPROX_COUNT_DISTINCT(udf(a))) FROM testData WHERE a = 0 GROUP BY 1
-- !query 8 schema
-struct<foo:string,approx_count_distinct(a):bigint>
+struct<foo:string,CAST(udf(cast(approx_count_distinct(cast(udf(cast(a as string)) as int), 0.05, 0, 0) as string)) AS BIGINT):bigint>
-- !query 8 output
-- !query 9
-SELECT 'foo', MAX(STRUCT(a)) FROM testData WHERE a = 0 GROUP BY 1
+SELECT 'foo', MAX(STRUCT(udf(a))) FROM testData WHERE a = 0 GROUP BY 1
-- !query 9 schema
-struct<foo:string,max(named_struct(a, a)):struct<a:int>>
+struct<foo:string,max(named_struct(col1, CAST(udf(cast(a as string)) AS INT))):struct<col1:int>>
-- !query 9 output
-- !query 10
-SELECT a + b, COUNT(b) FROM testData GROUP BY a + b
+SELECT udf(a + b), udf(COUNT(b)) FROM testData GROUP BY a + b
-- !query 10 schema
-struct<(a + b):int,count(b):bigint>
+struct<CAST(udf(cast((a + b) as string)) AS INT):int,CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
-- !query 10 output
2 1
3 2
-105,7 +105,7 NULL 1
-- !query 11
-SELECT a + 2, COUNT(b) FROM testData GROUP BY a + 1
+SELECT udf(a + 2), udf(COUNT(b)) FROM testData GROUP BY a + 1
-- !query 11 schema
struct<>
-- !query 11 output
-114,37 +114,35 expression 'testdata.`a`' is neither present in the group by, nor is it an aggre
-- !query 12
-SELECT a + 1 + 1, COUNT(b) FROM testData GROUP BY a + 1
+SELECT udf(a + 1 + 1), udf(COUNT(b)) FROM testData GROUP BY udf(a + 1)
-- !query 12 schema
-struct<((a + 1) + 1):int,count(b):bigint>
+struct<>
-- !query 12 output
-3 2
-4 2
-5 2
-NULL 1
+org.apache.spark.sql.AnalysisException
+expression 'testdata.`a`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;
-- !query 13
-SELECT SKEWNESS(a), KURTOSIS(a), MIN(a), MAX(a), AVG(a), VARIANCE(a), STDDEV(a), SUM(a), COUNT(a)
+SELECT SKEWNESS(udf(a)), udf(KURTOSIS(a)), udf(MIN(a)), MAX(udf(a)), udf(AVG(udf(a))), udf(VARIANCE(a)), STDDEV(udf(a)), udf(SUM(a)), udf(COUNT(a))
FROM testData
-- !query 13 schema
-struct<skewness(CAST(a AS DOUBLE)):double,kurtosis(CAST(a AS DOUBLE)):double,min(a):int,max(a):int,avg(a):double,var_samp(CAST(a AS DOUBLE)):double,stddev_samp(CAST(a AS DOUBLE)):double,sum(a):bigint,count(a):bigint>
+struct<skewness(CAST(CAST(udf(cast(a as string)) AS INT) AS DOUBLE)):double,CAST(udf(cast(kurtosis(cast(a as double)) as string)) AS DOUBLE):double,CAST(udf(cast(min(a) as string)) AS INT):int,max(CAST(udf(cast(a as string)) AS INT)):int,CAST(udf(cast(avg(cast(cast(udf(cast(a as string)) as int) as bigint)) as string)) AS DOUBLE):double,CAST(udf(cast(var_samp(cast(a as double)) as string)) AS DOUBLE):double,stddev_samp(CAST(CAST(udf(cast(a as string)) AS INT) AS DOUBLE)):double,CAST(udf(cast(sum(cast(a as bigint)) as string)) AS BIGINT):bigint,CAST(udf(cast(count(a) as string)) AS BIGINT):bigint>
-- !query 13 output
-0.2723801058145729 -1.5069204152249134 1 3 2.142857142857143 0.8095238095238094 0.8997354108424372 15 7
-- !query 14
-SELECT COUNT(DISTINCT b), COUNT(DISTINCT b, c) FROM (SELECT 1 AS a, 2 AS b, 3 AS c) GROUP BY a
+SELECT COUNT(DISTINCT udf(b)), udf(COUNT(DISTINCT b, c)) FROM (SELECT 1 AS a, 2 AS b, 3 AS c) GROUP BY a
-- !query 14 schema
-struct<count(DISTINCT b):bigint,count(DISTINCT b, c):bigint>
+struct<count(DISTINCT CAST(udf(cast(b as string)) AS INT)):bigint,CAST(udf(cast(count(distinct b, c) as string)) AS BIGINT):bigint>
-- !query 14 output
1 1
-- !query 15
-SELECT a AS k, COUNT(b) FROM testData GROUP BY k
+SELECT a AS k, COUNT(udf(b)) FROM testData GROUP BY k
-- !query 15 schema
-struct<k:int,count(b):bigint>
+struct<k:int,count(CAST(udf(cast(b as string)) AS INT)):bigint>
-- !query 15 output
1 2
2 2
-153,21 +151,21 NULL 1
-- !query 16
-SELECT a AS k, COUNT(b) FROM testData GROUP BY k HAVING k > 1
+SELECT a AS k, udf(COUNT(b)) FROM testData GROUP BY k HAVING k > 1
-- !query 16 schema
-struct<k:int,count(b):bigint>
+struct<k:int,CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
-- !query 16 output
2 2
3 2
-- !query 17
-SELECT COUNT(b) AS k FROM testData GROUP BY k
+SELECT udf(COUNT(b)) AS k FROM testData GROUP BY k
-- !query 17 schema
struct<>
-- !query 17 output
org.apache.spark.sql.AnalysisException
-aggregate functions are not allowed in GROUP BY, but found count(testdata.`b`);
+aggregate functions are not allowed in GROUP BY, but found CAST(udf(cast(count(b) as string)) AS BIGINT);
-- !query 18
-180,7 +178,7 struct<>
-- !query 19
-SELECT k AS a, COUNT(v) FROM testDataHasSameNameWithAlias GROUP BY a
+SELECT k AS a, udf(COUNT(udf(v))) FROM testDataHasSameNameWithAlias GROUP BY a
-- !query 19 schema
struct<>
-- !query 19 output
-197,32 +195,32 spark.sql.groupByAliases false
-- !query 21
-SELECT a AS k, COUNT(b) FROM testData GROUP BY k
+SELECT a AS k, udf(COUNT(udf(b))) FROM testData GROUP BY k
-- !query 21 schema
struct<>
-- !query 21 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`k`' given input columns: [testdata.a, testdata.b]; line 1 pos 47
+cannot resolve '`k`' given input columns: [testdata.a, testdata.b]; line 1 pos 57
-- !query 22
-SELECT a, COUNT(1) FROM testData WHERE false GROUP BY a
+SELECT a, COUNT(udf(1)) FROM testData WHERE false GROUP BY a
-- !query 22 schema
-struct<a:int,count(1):bigint>
+struct<a:int,count(CAST(udf(cast(1 as string)) AS INT)):bigint>
-- !query 22 output
-- !query 23
-SELECT COUNT(1) FROM testData WHERE false
+SELECT udf(COUNT(1)) FROM testData WHERE false
-- !query 23 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 23 output
0
-- !query 24
-SELECT 1 FROM (SELECT COUNT(1) FROM testData WHERE false) t
+SELECT 1 FROM (SELECT udf(COUNT(1)) FROM testData WHERE false) t
-- !query 24 schema
struct<1:int>
-- !query 24 output
-232,7 +230,7 struct<1:int>
-- !query 25
SELECT 1 from (
SELECT 1 AS z,
- MIN(a.x)
+ udf(MIN(a.x))
FROM (select 1 as x) a
WHERE false
) b
-244,32 +242,32 struct<1:int>
-- !query 26
-SELECT corr(DISTINCT x, y), corr(DISTINCT y, x), count(*)
+SELECT corr(DISTINCT x, y), udf(corr(DISTINCT y, x)), count(*)
FROM (VALUES (1, 1), (2, 2), (2, 2)) t(x, y)
-- !query 26 schema
-struct<corr(DISTINCT CAST(x AS DOUBLE), CAST(y AS DOUBLE)):double,corr(DISTINCT CAST(y AS DOUBLE), CAST(x AS DOUBLE)):double,count(1):bigint>
+struct<corr(DISTINCT CAST(x AS DOUBLE), CAST(y AS DOUBLE)):double,CAST(udf(cast(corr(distinct cast(y as double), cast(x as double)) as string)) AS DOUBLE):double,count(1):bigint>
-- !query 26 output
1.0 1.0 3
-- !query 27
-SELECT 1 FROM range(10) HAVING true
+SELECT udf(1) FROM range(10) HAVING true
-- !query 27 schema
-struct<1:int>
+struct<CAST(udf(cast(1 as string)) AS INT):int>
-- !query 27 output
1
-- !query 28
-SELECT 1 FROM range(10) HAVING MAX(id) > 0
+SELECT udf(udf(1)) FROM range(10) HAVING MAX(id) > 0
-- !query 28 schema
-struct<1:int>
+struct<CAST(udf(cast(cast(udf(cast(1 as string)) as int) as string)) AS INT):int>
-- !query 28 output
1
-- !query 29
-SELECT id FROM range(10) HAVING id > 0
+SELECT udf(id) FROM range(10) HAVING id > 0
-- !query 29 schema
struct<>
-- !query 29 output
-291,33 +289,33 struct<>
-- !query 31
-SELECT every(v), some(v), any(v) FROM test_agg WHERE 1 = 0
+SELECT udf(every(v)), udf(some(v)), any(v) FROM test_agg WHERE 1 = 0
-- !query 31 schema
-struct<every(v):boolean,some(v):boolean,any(v):boolean>
+struct<CAST(udf(cast(every(v) as string)) AS BOOLEAN):boolean,CAST(udf(cast(some(v) as string)) AS BOOLEAN):boolean,any(v):boolean>
-- !query 31 output
NULL NULL NULL
-- !query 32
-SELECT every(v), some(v), any(v) FROM test_agg WHERE k = 4
+SELECT udf(every(udf(v))), some(v), any(v) FROM test_agg WHERE k = 4
-- !query 32 schema
-struct<every(v):boolean,some(v):boolean,any(v):boolean>
+struct<CAST(udf(cast(every(cast(udf(cast(v as string)) as boolean)) as string)) AS BOOLEAN):boolean,some(v):boolean,any(v):boolean>
-- !query 32 output
NULL NULL NULL
-- !query 33
-SELECT every(v), some(v), any(v) FROM test_agg WHERE k = 5
+SELECT every(v), udf(some(v)), any(v) FROM test_agg WHERE k = 5
-- !query 33 schema
-struct<every(v):boolean,some(v):boolean,any(v):boolean>
+struct<every(v):boolean,CAST(udf(cast(some(v) as string)) AS BOOLEAN):boolean,any(v):boolean>
-- !query 33 output
false true true
-- !query 34
-SELECT k, every(v), some(v), any(v) FROM test_agg GROUP BY k
+SELECT k, every(v), udf(some(v)), any(v) FROM test_agg GROUP BY k
-- !query 34 schema
-struct<k:int,every(v):boolean,some(v):boolean,any(v):boolean>
+struct<k:int,every(v):boolean,CAST(udf(cast(some(v) as string)) AS BOOLEAN):boolean,any(v):boolean>
-- !query 34 output
1 false true true
2 true true true
-327,9 +325,9 struct<k:int,every(v):boolean,some(v):boolean,any(v):boolean>
-- !query 35
-SELECT k, every(v) FROM test_agg GROUP BY k HAVING every(v) = false
+SELECT udf(k), every(v) FROM test_agg GROUP BY k HAVING every(v) = false
-- !query 35 schema
-struct<k:int,every(v):boolean>
+struct<CAST(udf(cast(k as string)) AS INT):int,every(v):boolean>
-- !query 35 output
1 false
3 false
-337,16 +335,16 struct<k:int,every(v):boolean>
-- !query 36
-SELECT k, every(v) FROM test_agg GROUP BY k HAVING every(v) IS NULL
+SELECT k, udf(every(v)) FROM test_agg GROUP BY k HAVING every(v) IS NULL
-- !query 36 schema
-struct<k:int,every(v):boolean>
+struct<k:int,CAST(udf(cast(every(v) as string)) AS BOOLEAN):boolean>
-- !query 36 output
4 NULL
-- !query 37
SELECT k,
- Every(v) AS every
+ udf(Every(v)) AS every
FROM test_agg
WHERE k = 2
AND v IN (SELECT Any(v)
-360,7 +358,7 struct<k:int,every:boolean>
-- !query 38
-SELECT k,
+SELECT udf(udf(k)),
Every(v) AS every
FROM test_agg
WHERE k = 2
-369,45 +367,45 WHERE k = 2
WHERE k = 1)
GROUP BY k
-- !query 38 schema
-struct<k:int,every:boolean>
+struct<CAST(udf(cast(cast(udf(cast(k as string)) as int) as string)) AS INT):int,every:boolean>
-- !query 38 output
-- !query 39
-SELECT every(1)
+SELECT every(udf(1))
-- !query 39 schema
struct<>
-- !query 39 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'every(1)' due to data type mismatch: Input to function 'every' should have been boolean, but it's [int].; line 1 pos 7
+cannot resolve 'every(CAST(udf(cast(1 as string)) AS INT))' due to data type mismatch: Input to function 'every' should have been boolean, but it's [int].; line 1 pos 7
-- !query 40
-SELECT some(1S)
+SELECT some(udf(1S))
-- !query 40 schema
struct<>
-- !query 40 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'some(1S)' due to data type mismatch: Input to function 'some' should have been boolean, but it's [smallint].; line 1 pos 7
+cannot resolve 'some(CAST(udf(cast(1 as string)) AS SMALLINT))' due to data type mismatch: Input to function 'some' should have been boolean, but it's [smallint].; line 1 pos 7
-- !query 41
-SELECT any(1L)
+SELECT any(udf(1L))
-- !query 41 schema
struct<>
-- !query 41 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'any(1L)' due to data type mismatch: Input to function 'any' should have been boolean, but it's [bigint].; line 1 pos 7
+cannot resolve 'any(CAST(udf(cast(1 as string)) AS BIGINT))' due to data type mismatch: Input to function 'any' should have been boolean, but it's [bigint].; line 1 pos 7
-- !query 42
-SELECT every("true")
+SELECT udf(every("true"))
-- !query 42 schema
struct<>
-- !query 42 output
org.apache.spark.sql.AnalysisException
-cannot resolve 'every('true')' due to data type mismatch: Input to function 'every' should have been boolean, but it's [string].; line 1 pos 7
+cannot resolve 'every('true')' due to data type mismatch: Input to function 'every' should have been boolean, but it's [string].; line 1 pos 11
-- !query 43
-428,9 +426,9 struct<k:int,v:boolean,every(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST
-- !query 44
-SELECT k, v, some(v) OVER (PARTITION BY k ORDER BY v) FROM test_agg
+SELECT k, udf(udf(v)), some(v) OVER (PARTITION BY k ORDER BY v) FROM test_agg
-- !query 44 schema
-struct<k:int,v:boolean,some(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):boolean>
+struct<k:int,CAST(udf(cast(cast(udf(cast(v as string)) as boolean) as string)) AS BOOLEAN):boolean,some(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):boolean>
-- !query 44 output
1 false false
1 true true
-445,9 +443,9 struct<k:int,v:boolean,some(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST R
-- !query 45
-SELECT k, v, any(v) OVER (PARTITION BY k ORDER BY v) FROM test_agg
+SELECT udf(udf(k)), v, any(v) OVER (PARTITION BY k ORDER BY v) FROM test_agg
-- !query 45 schema
-struct<k:int,v:boolean,any(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):boolean>
+struct<CAST(udf(cast(cast(udf(cast(k as string)) as int) as string)) AS INT):int,v:boolean,any(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):boolean>
-- !query 45 output
1 false false
1 true true
-462,17 +460,17 struct<k:int,v:boolean,any(v) OVER (PARTITION BY k ORDER BY v ASC NULLS FIRST RA
-- !query 46
-SELECT count(*) FROM test_agg HAVING count(*) > 1L
+SELECT udf(count(*)) FROM test_agg HAVING count(*) > 1L
-- !query 46 schema
-struct<count(1):bigint>
+struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 46 output
10
-- !query 47
-SELECT k, max(v) FROM test_agg GROUP BY k HAVING max(v) = true
+SELECT k, udf(max(v)) FROM test_agg GROUP BY k HAVING max(v) = true
-- !query 47 schema
-struct<k:int,max(v):boolean>
+struct<k:int,CAST(udf(cast(max(v) as string)) AS BOOLEAN):boolean>
-- !query 47 output
1 true
2 true
-480,7 +478,7 struct<k:int,max(v):boolean>
-- !query 48
-SELECT * FROM (SELECT COUNT(*) AS cnt FROM test_agg) WHERE cnt > 1L
+SELECT * FROM (SELECT udf(COUNT(*)) AS cnt FROM test_agg) WHERE cnt > 1L
-- !query 48 schema
struct<cnt:bigint>
-- !query 48 output
-488,7 +486,7 struct<cnt:bigint>
-- !query 49
-SELECT count(*) FROM test_agg WHERE count(*) > 1L
+SELECT udf(count(*)) FROM test_agg WHERE count(*) > 1L
-- !query 49 schema
struct<>
-- !query 49 output
-500,7 +498,7 Invalid expressions: [count(1)];
-- !query 50
-SELECT count(*) FROM test_agg WHERE count(*) + 1L > 1L
+SELECT udf(count(*)) FROM test_agg WHERE count(*) + 1L > 1L
-- !query 50 schema
struct<>
-- !query 50 output
-512,7 +510,7 Invalid expressions: [count(1)];
-- !query 51
-SELECT count(*) FROM test_agg WHERE k = 1 or k = 2 or count(*) + 1L > 1L or max(k) > 1
+SELECT udf(count(*)) FROM test_agg WHERE k = 1 or k = 2 or count(*) + 1L > 1L or max(k) > 1
-- !query 51 schema
struct<>
-- !query 51 output
```
</p>
</details>
## How was this patch tested?
Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
Verified pandas & pyarrow versions:
```$python3
Python 3.6.8 (default, Jan 14 2019, 11:02:34)
[GCC 8.0.1 20180414 (experimental) [trunk revision 259383]] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
>>> import pyarrow
>>> pyarrow.__version__
'0.14.0'
>>> pandas.__version__
'0.24.2'
```
From the sql output it seems that sql statements are evaluated correctly given that udf returns a string and may change results as Null will be returned as None and will be counted in returned values.
Closes#25098 from skonto/group-by.sql.
Authored-by: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Implements the `REPLACE TABLE` and `REPLACE TABLE AS SELECT` logical plans. `REPLACE TABLE` is now a valid operation in spark-sql provided that the tables being modified are managed by V2 catalogs.
This also introduces an atomic mix-in that table catalogs can choose to implement. Table catalogs can now implement `TransactionalTableCatalog`. The semantics of this API are that table creation and replacement can be "staged" and then "committed".
On the execution of `REPLACE TABLE AS SELECT`, `REPLACE TABLE`, and `CREATE TABLE AS SELECT`, if the catalog implements transactional operations, the physical plan will use said functionality. Otherwise, these operations fall back on non-atomic variants. For `REPLACE TABLE` in particular, the usage of non-atomic operations can unfortunately lead to inconsistent state.
## How was this patch tested?
Unit tests - multiple additions to `DataSourceV2SQLSuite`.
Closes#24798 from mccheah/spark-27724.
Authored-by: mcheah <mcheah@palantir.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr is to remove the unnecessary test in DataFrameSuite.
## How was this patch tested?
N/A
Closes#25216 from maropu/SPARK-28189-FOLLOWUP.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR adds some tests converted from `inline-table.sql` to test UDFs. Please see contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
<details><summary>Diff comparing to 'inline-table.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/inline-table.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-inline-table.sql.out
index 4e80f0bda5..2cf24e50c8 100644
--- a/sql/core/src/test/resources/sql-tests/results/inline-table.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-inline-table.sql.out
-3,33 +3,33
-- !query 0
-select * from values ("one", 1)
+select udf(col1), udf(col2) from values ("one", 1)
-- !query 0 schema
-struct<col1:string,col2:int>
+struct<CAST(udf(cast(col1 as string)) AS STRING):string,CAST(udf(cast(col2 as string)) AS INT):int>
-- !query 0 output
one 1
-- !query 1
-select * from values ("one", 1) as data
+select udf(col1), udf(udf(col2)) from values ("one", 1) as data
-- !query 1 schema
-struct<col1:string,col2:int>
+struct<CAST(udf(cast(col1 as string)) AS STRING):string,CAST(udf(cast(cast(udf(cast(col2 as string)) as int) as string)) AS INT):int>
-- !query 1 output
one 1
-- !query 2
-select * from values ("one", 1) as data(a, b)
+select udf(a), b from values ("one", 1) as data(a, b)
-- !query 2 schema
-struct<a:string,b:int>
+struct<CAST(udf(cast(a as string)) AS STRING):string,b:int>
-- !query 2 output
one 1
-- !query 3
-select * from values 1, 2, 3 as data(a)
+select udf(a) from values 1, 2, 3 as data(a)
-- !query 3 schema
-struct<a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int>
-- !query 3 output
1
2
-37,9 +37,9 struct<a:int>
-- !query 4
-select * from values ("one", 1), ("two", 2), ("three", null) as data(a, b)
+select udf(a), b from values ("one", 1), ("two", 2), ("three", null) as data(a, b)
-- !query 4 schema
-struct<a:string,b:int>
+struct<CAST(udf(cast(a as string)) AS STRING):string,b:int>
-- !query 4 output
one 1
three NULL
-47,107 +47,107 two 2
-- !query 5
-select * from values ("one", null), ("two", null) as data(a, b)
+select udf(a), b from values ("one", null), ("two", null) as data(a, b)
-- !query 5 schema
-struct<a:string,b:null>
+struct<CAST(udf(cast(a as string)) AS STRING):string,b:null>
-- !query 5 output
one NULL
two NULL
-- !query 6
-select * from values ("one", 1), ("two", 2L) as data(a, b)
+select udf(a), b from values ("one", 1), ("two", 2L) as data(a, b)
-- !query 6 schema
-struct<a:string,b:bigint>
+struct<CAST(udf(cast(a as string)) AS STRING):string,b:bigint>
-- !query 6 output
one 1
two 2
-- !query 7
-select * from values ("one", 1 + 0), ("two", 1 + 3L) as data(a, b)
+select udf(udf(a)), udf(b) from values ("one", 1 + 0), ("two", 1 + 3L) as data(a, b)
-- !query 7 schema
-struct<a:string,b:bigint>
+struct<CAST(udf(cast(cast(udf(cast(a as string)) as string) as string)) AS STRING):string,CAST(udf(cast(b as string)) AS BIGINT):bigint>
-- !query 7 output
one 1
two 4
-- !query 8
-select * from values ("one", array(0, 1)), ("two", array(2, 3)) as data(a, b)
+select udf(a), b from values ("one", array(0, 1)), ("two", array(2, 3)) as data(a, b)
-- !query 8 schema
-struct<a:string,b:array<int>>
+struct<CAST(udf(cast(a as string)) AS STRING):string,b:array<int>>
-- !query 8 output
one [0,1]
two [2,3]
-- !query 9
-select * from values ("one", 2.0), ("two", 3.0D) as data(a, b)
+select udf(a), b from values ("one", 2.0), ("two", 3.0D) as data(a, b)
-- !query 9 schema
-struct<a:string,b:double>
+struct<CAST(udf(cast(a as string)) AS STRING):string,b:double>
-- !query 9 output
one 2.0
two 3.0
-- !query 10
-select * from values ("one", rand(5)), ("two", 3.0D) as data(a, b)
+select udf(a), b from values ("one", rand(5)), ("two", 3.0D) as data(a, b)
-- !query 10 schema
struct<>
-- !query 10 output
org.apache.spark.sql.AnalysisException
-cannot evaluate expression rand(5) in inline table definition; line 1 pos 29
+cannot evaluate expression rand(5) in inline table definition; line 1 pos 37
-- !query 11
-select * from values ("one", 2.0), ("two") as data(a, b)
+select udf(a), udf(b) from values ("one", 2.0), ("two") as data(a, b)
-- !query 11 schema
struct<>
-- !query 11 output
org.apache.spark.sql.AnalysisException
-expected 2 columns but found 1 columns in row 1; line 1 pos 14
+expected 2 columns but found 1 columns in row 1; line 1 pos 27
-- !query 12
-select * from values ("one", array(0, 1)), ("two", struct(1, 2)) as data(a, b)
+select udf(a), udf(b) from values ("one", array(0, 1)), ("two", struct(1, 2)) as data(a, b)
-- !query 12 schema
struct<>
-- !query 12 output
org.apache.spark.sql.AnalysisException
-incompatible types found in column b for inline table; line 1 pos 14
+incompatible types found in column b for inline table; line 1 pos 27
-- !query 13
-select * from values ("one"), ("two") as data(a, b)
+select udf(a), udf(b) from values ("one"), ("two") as data(a, b)
-- !query 13 schema
struct<>
-- !query 13 output
org.apache.spark.sql.AnalysisException
-expected 2 columns but found 1 columns in row 0; line 1 pos 14
+expected 2 columns but found 1 columns in row 0; line 1 pos 27
-- !query 14
-select * from values ("one", random_not_exist_func(1)), ("two", 2) as data(a, b)
+select udf(a), udf(b) from values ("one", random_not_exist_func(1)), ("two", 2) as data(a, b)
-- !query 14 schema
struct<>
-- !query 14 output
org.apache.spark.sql.AnalysisException
-Undefined function: 'random_not_exist_func'. This function is neither a registered temporary function nor a permanent function registered in the database 'default'.; line 1 pos 29
+Undefined function: 'random_not_exist_func'. This function is neither a registered temporary function nor a permanent function registered in the database 'default'.; line 1 pos 42
-- !query 15
-select * from values ("one", count(1)), ("two", 2) as data(a, b)
+select udf(a), udf(b) from values ("one", count(1)), ("two", 2) as data(a, b)
-- !query 15 schema
struct<>
-- !query 15 output
org.apache.spark.sql.AnalysisException
-cannot evaluate expression count(1) in inline table definition; line 1 pos 29
+cannot evaluate expression count(1) in inline table definition; line 1 pos 42
-- !query 16
-select * from values (timestamp('1991-12-06 00:00:00.0'), array(timestamp('1991-12-06 01:00:00.0'), timestamp('1991-12-06 12:00:00.0'))) as data(a, b)
+select udf(a), b from values (timestamp('1991-12-06 00:00:00.0'), array(timestamp('1991-12-06 01:00:00.0'), timestamp('1991-12-06 12:00:00.0'))) as data(a, b)
-- !query 16 schema
-struct<a:timestamp,b:array<timestamp>>
+struct<CAST(udf(cast(a as string)) AS TIMESTAMP):timestamp,b:array<timestamp>>
-- !query 16 output
1991-12-06 00:00:00 [1991-12-06 01:00:00.0,1991-12-06 12:00:00.0]
```
</p>
</details>
## How was this patch tested?
Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
Closes#25124 from imback82/inline-table-sql.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR adds some tests converted from group-analytics.sql to test UDFs. Please see contribution guide of this umbrella ticket - SPARK-27921.
<details><summary>Diff comparing to 'group-analytics.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/udf/udf-group-analytics.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-group-analytics.sql.out
index 31e9e08e2c..3439a05727 100644
--- a/sql/core/src/test/resources/sql-tests/results/udf/udf-group-analytics.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-group-analytics.sql.out
-13,9 +13,9 struct<>
-- !query 1
-SELECT a + b, b, udf(SUM(a - b)) FROM testData GROUP BY a + b, b WITH CUBE
+SELECT a + b, b, SUM(a - b) FROM testData GROUP BY a + b, b WITH CUBE
-- !query 1 schema
-struct<(a + b):int,b:int,CAST(udf(cast(sum(cast((a - b) as bigint)) as string)) AS BIGINT):bigint>
+struct<(a + b):int,b:int,sum((a - b)):bigint>
-- !query 1 output
2 1 0
2 NULL 0
-33,9 +33,9 NULL NULL 3
-- !query 2
-SELECT a, udf(b), SUM(b) FROM testData GROUP BY a, b WITH CUBE
+SELECT a, b, SUM(b) FROM testData GROUP BY a, b WITH CUBE
-- !query 2 schema
-struct<a:int,CAST(udf(cast(b as string)) AS INT):int,sum(b):bigint>
+struct<a:int,b:int,sum(b):bigint>
-- !query 2 output
1 1 1
1 2 2
-52,9 +52,9 NULL NULL 9
-- !query 3
-SELECT udf(a + b), b, SUM(a - b) FROM testData GROUP BY a + b, b WITH ROLLUP
+SELECT a + b, b, SUM(a - b) FROM testData GROUP BY a + b, b WITH ROLLUP
-- !query 3 schema
-struct<CAST(udf(cast((a + b) as string)) AS INT):int,b:int,sum((a - b)):bigint>
+struct<(a + b):int,b:int,sum((a - b)):bigint>
-- !query 3 output
2 1 0
2 NULL 0
-70,9 +70,9 NULL NULL 3
-- !query 4
-SELECT a, b, udf(SUM(b)) FROM testData GROUP BY a, b WITH ROLLUP
+SELECT a, b, SUM(b) FROM testData GROUP BY a, b WITH ROLLUP
-- !query 4 schema
-struct<a:int,b:int,CAST(udf(cast(sum(cast(b as bigint)) as string)) AS BIGINT):bigint>
+struct<a:int,b:int,sum(b):bigint>
-- !query 4 output
1 1 1
1 2 2
-97,7 +97,7 struct<>
-- !query 6
-SELECT course, year, SUM(earnings) FROM courseSales GROUP BY ROLLUP(course, year) ORDER BY udf(course), year
+SELECT course, year, SUM(earnings) FROM courseSales GROUP BY ROLLUP(course, year) ORDER BY course, year
-- !query 6 schema
struct<course:string,year:int,sum(earnings):bigint>
-- !query 6 output
-111,7 +111,7 dotNET 2013 48000
-- !query 7
-SELECT course, year, SUM(earnings) FROM courseSales GROUP BY CUBE(course, year) ORDER BY course, udf(year)
+SELECT course, year, SUM(earnings) FROM courseSales GROUP BY CUBE(course, year) ORDER BY course, year
-- !query 7 schema
struct<course:string,year:int,sum(earnings):bigint>
-- !query 7 output
-127,9 +127,9 dotNET 2013 48000
-- !query 8
-SELECT course, udf(year), SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(course, year)
+SELECT course, year, SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(course, year)
-- !query 8 schema
-struct<course:string,CAST(udf(cast(year as string)) AS INT):int,sum(earnings):bigint>
+struct<course:string,year:int,sum(earnings):bigint>
-- !query 8 output
Java NULL 50000
NULL 2012 35000
-138,26 +138,26 dotNET NULL 63000
-- !query 9
-SELECT course, year, udf(SUM(earnings)) FROM courseSales GROUP BY course, year GROUPING SETS(course)
+SELECT course, year, SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(course)
-- !query 9 schema
-struct<course:string,year:int,CAST(udf(cast(sum(cast(earnings as bigint)) as string)) AS BIGINT):bigint>
+struct<course:string,year:int,sum(earnings):bigint>
-- !query 9 output
Java NULL 50000
dotNET NULL 63000
-- !query 10
-SELECT udf(course), year, SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(year)
+SELECT course, year, SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(year)
-- !query 10 schema
-struct<CAST(udf(cast(course as string)) AS STRING):string,year:int,sum(earnings):bigint>
+struct<course:string,year:int,sum(earnings):bigint>
-- !query 10 output
NULL 2012 35000
NULL 2013 78000
-- !query 11
-SELECT course, udf(SUM(earnings)) AS sum FROM courseSales
-GROUP BY course, earnings GROUPING SETS((), (course), (course, earnings)) ORDER BY course, udf(sum)
+SELECT course, SUM(earnings) AS sum FROM courseSales
+GROUP BY course, earnings GROUPING SETS((), (course), (course, earnings)) ORDER BY course, sum
-- !query 11 schema
struct<course:string,sum:bigint>
-- !query 11 output
-173,7 +173,7 dotNET 63000
-- !query 12
SELECT course, SUM(earnings) AS sum, GROUPING_ID(course, earnings) FROM courseSales
-GROUP BY course, earnings GROUPING SETS((), (course), (course, earnings)) ORDER BY udf(course), sum
+GROUP BY course, earnings GROUPING SETS((), (course), (course, earnings)) ORDER BY course, sum
-- !query 12 schema
struct<course:string,sum:bigint,grouping_id(course, earnings):int>
-- !query 12 output
-188,10 +188,10 dotNET 63000 1
-- !query 13
-SELECT udf(course), udf(year), GROUPING(course), GROUPING(year), GROUPING_ID(course, year) FROM courseSales
+SELECT course, year, GROUPING(course), GROUPING(year), GROUPING_ID(course, year) FROM courseSales
GROUP BY CUBE(course, year)
-- !query 13 schema
-struct<CAST(udf(cast(course as string)) AS STRING):string,CAST(udf(cast(year as string)) AS INT):int,grouping(course):tinyint,grouping(year):tinyint,grouping_id(course, year):int>
+struct<course:string,year:int,grouping(course):tinyint,grouping(year):tinyint,grouping_id(course, year):int>
-- !query 13 output
Java 2012 0 0 0
Java 2013 0 0 0
-205,7 +205,7 dotNET NULL 0 1 1
-- !query 14
-SELECT course, udf(year), GROUPING(course) FROM courseSales GROUP BY course, year
+SELECT course, year, GROUPING(course) FROM courseSales GROUP BY course, year
-- !query 14 schema
struct<>
-- !query 14 output
-214,7 +214,7 grouping() can only be used with GroupingSets/Cube/Rollup;
-- !query 15
-SELECT course, udf(year), GROUPING_ID(course, year) FROM courseSales GROUP BY course, year
+SELECT course, year, GROUPING_ID(course, year) FROM courseSales GROUP BY course, year
-- !query 15 schema
struct<>
-- !query 15 output
-223,7 +223,7 grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 16
-SELECT course, year, grouping__id FROM courseSales GROUP BY CUBE(course, year) ORDER BY grouping__id, course, udf(year)
+SELECT course, year, grouping__id FROM courseSales GROUP BY CUBE(course, year) ORDER BY grouping__id, course, year
-- !query 16 schema
struct<course:string,year:int,grouping__id:int>
-- !query 16 output
-240,7 +240,7 NULL NULL 3
-- !query 17
SELECT course, year FROM courseSales GROUP BY CUBE(course, year)
-HAVING GROUPING(year) = 1 AND GROUPING_ID(course, year) > 0 ORDER BY course, udf(year)
+HAVING GROUPING(year) = 1 AND GROUPING_ID(course, year) > 0 ORDER BY course, year
-- !query 17 schema
struct<course:string,year:int>
-- !query 17 output
-250,7 +250,7 dotNET NULL
-- !query 18
-SELECT course, udf(year) FROM courseSales GROUP BY course, year HAVING GROUPING(course) > 0
+SELECT course, year FROM courseSales GROUP BY course, year HAVING GROUPING(course) > 0
-- !query 18 schema
struct<>
-- !query 18 output
-259,7 +259,7 grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 19
-SELECT course, udf(udf(year)) FROM courseSales GROUP BY course, year HAVING GROUPING_ID(course) > 0
+SELECT course, year FROM courseSales GROUP BY course, year HAVING GROUPING_ID(course) > 0
-- !query 19 schema
struct<>
-- !query 19 output
-268,9 +268,9 grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 20
-SELECT udf(course), year FROM courseSales GROUP BY CUBE(course, year) HAVING grouping__id > 0
+SELECT course, year FROM courseSales GROUP BY CUBE(course, year) HAVING grouping__id > 0
-- !query 20 schema
-struct<CAST(udf(cast(course as string)) AS STRING):string,year:int>
+struct<course:string,year:int>
-- !query 20 output
Java NULL
NULL 2012
-281,7 +281,7 dotNET NULL
-- !query 21
SELECT course, year, GROUPING(course), GROUPING(year) FROM courseSales GROUP BY CUBE(course, year)
-ORDER BY GROUPING(course), GROUPING(year), course, udf(year)
+ORDER BY GROUPING(course), GROUPING(year), course, year
-- !query 21 schema
struct<course:string,year:int,grouping(course):tinyint,grouping(year):tinyint>
-- !query 21 output
-298,7 +298,7 NULL NULL 1 1
-- !query 22
SELECT course, year, GROUPING_ID(course, year) FROM courseSales GROUP BY CUBE(course, year)
-ORDER BY GROUPING(course), GROUPING(year), course, udf(year)
+ORDER BY GROUPING(course), GROUPING(year), course, year
-- !query 22 schema
struct<course:string,year:int,grouping_id(course, year):int>
-- !query 22 output
-314,7 +314,7 NULL NULL 3
-- !query 23
-SELECT course, udf(year) FROM courseSales GROUP BY course, udf(year) ORDER BY GROUPING(course)
+SELECT course, year FROM courseSales GROUP BY course, year ORDER BY GROUPING(course)
-- !query 23 schema
struct<>
-- !query 23 output
-323,7 +323,7 grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 24
-SELECT course, udf(year) FROM courseSales GROUP BY course, udf(year) ORDER BY GROUPING_ID(course)
+SELECT course, year FROM courseSales GROUP BY course, year ORDER BY GROUPING_ID(course)
-- !query 24 schema
struct<>
-- !query 24 output
-332,7 +332,7 grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 25
-SELECT course, year FROM courseSales GROUP BY CUBE(course, year) ORDER BY grouping__id, udf(course), year
+SELECT course, year FROM courseSales GROUP BY CUBE(course, year) ORDER BY grouping__id, course, year
-- !query 25 schema
struct<course:string,year:int>
-- !query 25 output
-348,7 +348,7 NULL NULL
-- !query 26
-SELECT udf(a + b) AS k1, udf(b) AS k2, SUM(a - b) FROM testData GROUP BY CUBE(k1, k2)
+SELECT a + b AS k1, b AS k2, SUM(a - b) FROM testData GROUP BY CUBE(k1, k2)
-- !query 26 schema
struct<k1:int,k2:int,sum((a - b)):bigint>
-- !query 26 output
-368,7 +368,7 NULL NULL 3
-- !query 27
-SELECT udf(udf(a + b)) AS k, b, SUM(a - b) FROM testData GROUP BY ROLLUP(k, b)
+SELECT a + b AS k, b, SUM(a - b) FROM testData GROUP BY ROLLUP(k, b)
-- !query 27 schema
struct<k:int,b:int,sum((a - b)):bigint>
-- !query 27 output
-386,9 +386,9 NULL NULL 3
-- !query 28
-SELECT udf(a + b), udf(udf(b)) AS k, SUM(a - b) FROM testData GROUP BY a + b, k GROUPING SETS(k)
+SELECT a + b, b AS k, SUM(a - b) FROM testData GROUP BY a + b, k GROUPING SETS(k)
-- !query 28 schema
-struct<CAST(udf(cast((a + b) as string)) AS INT):int,k:int,sum((a - b)):bigint>
+struct<(a + b):int,k:int,sum((a - b)):bigint>
-- !query 28 output
NULL 1 3
NULL 2 0
```
</p>
</details>
## How was this patch tested?
Tested as guided in SPARK-27921.
Verified pandas & pyarrow versions:
```$python3
Python 3.6.8 (default, Jan 14 2019, 11:02:34)
[GCC 8.0.1 20180414 (experimental) [trunk revision 259383]] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
>>> import pyarrow
>>> pyarrow.__version__
'0.14.0'
>>> pandas.__version__
'0.24.2'
```
From the sql output it seems that sql statements are evaluated correctly given that udf returns a string and may change results as Null will be returned as None and will be counted in returned values.
Closes#25196 from skonto/group-analytics.sql.
Authored-by: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR removes a few hardware-dependent assertions which can cause a failure in `aarch64`.
**x86_64**
```
rootdonotdel-openlab-allinone-l00242678:/home/ubuntu# uname -a
Linux donotdel-openlab-allinone-l00242678 4.4.0-154-generic #181-Ubuntu SMP Tue Jun 25 05:29:03 UTC
2019 x86_64 x86_64 x86_64 GNU/Linux
scala> import java.lang.Float.floatToRawIntBits
import java.lang.Float.floatToRawIntBits
scala> floatToRawIntBits(0.0f/0.0f)
res0: Int = -4194304
scala> floatToRawIntBits(Float.NaN)
res1: Int = 2143289344
```
**aarch64**
```
[rootarm-huangtianhua spark]# uname -a
Linux arm-huangtianhua 4.14.0-49.el7a.aarch64 #1 SMP Tue Apr 10 17:22:26 UTC 2018 aarch64 aarch64 aarch64 GNU/Linux
scala> import java.lang.Float.floatToRawIntBits
import java.lang.Float.floatToRawIntBits
scala> floatToRawIntBits(0.0f/0.0f)
res1: Int = 2143289344
scala> floatToRawIntBits(Float.NaN)
res2: Int = 2143289344
```
## How was this patch tested?
Pass the Jenkins (This removes the test coverage).
Closes#25186 from huangtianhua/special-test-case-for-aarch64.
Authored-by: huangtianhua <huangtianhua@huawei.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
SPARK-28199 (#24996) hid implementations of Triggers into `private[sql]` and encourage end users to use `Trigger.xxx` methods instead.
As I got some post review comment on 7548a8826d (r34366934) we could remove annotations which are meant to be used with public API.
## How was this patch tested?
N/A
Closes#25200 from HeartSaVioR/SPARK-28199-FOLLOWUP.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR adds some tests converted from `join-empty-relation.sql` to test UDFs. Please see contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
<details><summary>Diff comparing to 'join-empty-relation.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/join-empty-relation.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-join-empty-relation.sql.out
index 857073a827..e79d01fb14 100644
--- a/sql/core/src/test/resources/sql-tests/results/join-empty-relation.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-join-empty-relation.sql.out
-27,111 +27,111 struct<>
-- !query 3
-SELECT * FROM t1 INNER JOIN empty_table
+SELECT udf(t1.a), udf(empty_table.a) FROM t1 INNER JOIN empty_table ON (udf(t1.a) = udf(udf(empty_table.a)))
-- !query 3 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(a as string)) AS INT):int>
-- !query 3 output
-- !query 4
-SELECT * FROM t1 CROSS JOIN empty_table
+SELECT udf(t1.a), udf(udf(empty_table.a)) FROM t1 CROSS JOIN empty_table ON (udf(udf(t1.a)) = udf(empty_table.a))
-- !query 4 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(a as string)) as int) as string)) AS INT):int>
-- !query 4 output
-- !query 5
-SELECT * FROM t1 LEFT OUTER JOIN empty_table
+SELECT udf(udf(t1.a)), empty_table.a FROM t1 LEFT OUTER JOIN empty_table ON (udf(t1.a) = udf(empty_table.a))
-- !query 5 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(cast(udf(cast(a as string)) as int) as string)) AS INT):int,a:int>
-- !query 5 output
1 NULL
-- !query 6
-SELECT * FROM t1 RIGHT OUTER JOIN empty_table
+SELECT udf(t1.a), udf(empty_table.a) FROM t1 RIGHT OUTER JOIN empty_table ON (udf(t1.a) = udf(empty_table.a))
-- !query 6 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(a as string)) AS INT):int>
-- !query 6 output
-- !query 7
-SELECT * FROM t1 FULL OUTER JOIN empty_table
+SELECT udf(t1.a), empty_table.a FROM t1 FULL OUTER JOIN empty_table ON (udf(t1.a) = udf(empty_table.a))
-- !query 7 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int,a:int>
-- !query 7 output
1 NULL
-- !query 8
-SELECT * FROM t1 LEFT SEMI JOIN empty_table
+SELECT udf(udf(t1.a)) FROM t1 LEFT SEMI JOIN empty_table ON (udf(t1.a) = udf(udf(empty_table.a)))
-- !query 8 schema
-struct<a:int>
+struct<CAST(udf(cast(cast(udf(cast(a as string)) as int) as string)) AS INT):int>
-- !query 8 output
-- !query 9
-SELECT * FROM t1 LEFT ANTI JOIN empty_table
+SELECT udf(t1.a) FROM t1 LEFT ANTI JOIN empty_table ON (udf(t1.a) = udf(empty_table.a))
-- !query 9 schema
-struct<a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int>
-- !query 9 output
1
-- !query 10
-SELECT * FROM empty_table INNER JOIN t1
+SELECT udf(empty_table.a), udf(t1.a) FROM empty_table INNER JOIN t1 ON (udf(udf(empty_table.a)) = udf(t1.a))
-- !query 10 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(a as string)) AS INT):int>
-- !query 10 output
-- !query 11
-SELECT * FROM empty_table CROSS JOIN t1
+SELECT udf(empty_table.a), udf(udf(t1.a)) FROM empty_table CROSS JOIN t1 ON (udf(empty_table.a) = udf(udf(t1.a)))
-- !query 11 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(cast(udf(cast(a as string)) as int) as string)) AS INT):int>
-- !query 11 output
-- !query 12
-SELECT * FROM empty_table LEFT OUTER JOIN t1
+SELECT udf(udf(empty_table.a)), udf(t1.a) FROM empty_table LEFT OUTER JOIN t1 ON (udf(empty_table.a) = udf(t1.a))
-- !query 12 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(cast(udf(cast(a as string)) as int) as string)) AS INT):int,CAST(udf(cast(a as string)) AS INT):int>
-- !query 12 output
-- !query 13
-SELECT * FROM empty_table RIGHT OUTER JOIN t1
+SELECT empty_table.a, udf(t1.a) FROM empty_table RIGHT OUTER JOIN t1 ON (udf(empty_table.a) = udf(t1.a))
-- !query 13 schema
-struct<a:int,a:int>
+struct<a:int,CAST(udf(cast(a as string)) AS INT):int>
-- !query 13 output
NULL 1
-- !query 14
-SELECT * FROM empty_table FULL OUTER JOIN t1
+SELECT empty_table.a, udf(udf(t1.a)) FROM empty_table FULL OUTER JOIN t1 ON (udf(empty_table.a) = udf(t1.a))
-- !query 14 schema
-struct<a:int,a:int>
+struct<a:int,CAST(udf(cast(cast(udf(cast(a as string)) as int) as string)) AS INT):int>
-- !query 14 output
NULL 1
-- !query 15
-SELECT * FROM empty_table LEFT SEMI JOIN t1
+SELECT udf(udf(empty_table.a)) FROM empty_table LEFT SEMI JOIN t1 ON (udf(empty_table.a) = udf(udf(t1.a)))
-- !query 15 schema
-struct<a:int>
+struct<CAST(udf(cast(cast(udf(cast(a as string)) as int) as string)) AS INT):int>
-- !query 15 output
-- !query 16
-SELECT * FROM empty_table LEFT ANTI JOIN t1
+SELECT empty_table.a FROM empty_table LEFT ANTI JOIN t1 ON (udf(empty_table.a) = udf(t1.a))
-- !query 16 schema
struct<a:int>
-- !query 16 output
-139,56 +139,56 struct<a:int>
-- !query 17
-SELECT * FROM empty_table INNER JOIN empty_table
+SELECT udf(empty_table.a) FROM empty_table INNER JOIN empty_table AS empty_table2 ON (udf(empty_table.a) = udf(udf(empty_table2.a)))
-- !query 17 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int>
-- !query 17 output
-- !query 18
-SELECT * FROM empty_table CROSS JOIN empty_table
+SELECT udf(udf(empty_table.a)) FROM empty_table CROSS JOIN empty_table AS empty_table2 ON (udf(udf(empty_table.a)) = udf(empty_table2.a))
-- !query 18 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(cast(udf(cast(a as string)) as int) as string)) AS INT):int>
-- !query 18 output
-- !query 19
-SELECT * FROM empty_table LEFT OUTER JOIN empty_table
+SELECT udf(empty_table.a) FROM empty_table LEFT OUTER JOIN empty_table AS empty_table2 ON (udf(empty_table.a) = udf(empty_table2.a))
-- !query 19 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int>
-- !query 19 output
-- !query 20
-SELECT * FROM empty_table RIGHT OUTER JOIN empty_table
+SELECT udf(udf(empty_table.a)) FROM empty_table RIGHT OUTER JOIN empty_table AS empty_table2 ON (udf(empty_table.a) = udf(udf(empty_table2.a)))
-- !query 20 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(cast(udf(cast(a as string)) as int) as string)) AS INT):int>
-- !query 20 output
-- !query 21
-SELECT * FROM empty_table FULL OUTER JOIN empty_table
+SELECT udf(empty_table.a) FROM empty_table FULL OUTER JOIN empty_table AS empty_table2 ON (udf(empty_table.a) = udf(empty_table2.a))
-- !query 21 schema
-struct<a:int,a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int>
-- !query 21 output
-- !query 22
-SELECT * FROM empty_table LEFT SEMI JOIN empty_table
+SELECT udf(udf(empty_table.a)) FROM empty_table LEFT SEMI JOIN empty_table AS empty_table2 ON (udf(empty_table.a) = udf(empty_table2.a))
-- !query 22 schema
-struct<a:int>
+struct<CAST(udf(cast(cast(udf(cast(a as string)) as int) as string)) AS INT):int>
-- !query 22 output
-- !query 23
-SELECT * FROM empty_table LEFT ANTI JOIN empty_table
+SELECT udf(empty_table.a) FROM empty_table LEFT ANTI JOIN empty_table AS empty_table2 ON (udf(empty_table.a) = udf(empty_table2.a))
-- !query 23 schema
-struct<a:int>
+struct<CAST(udf(cast(a as string)) AS INT):int>
-- !query 23 output
```
</p>
</details>
## How was this patch tested?
Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
Closes#25127 from imback82/join-empty-relation-sql.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Performance issue using explode was found when a complex field contains huge array is to get duplicated as the number of exploded array elements. Given example:
```scala
val df = spark.sparkContext.parallelize(Seq(("1",
Array.fill(M)({
val i = math.random
(i.toString, (i + 1).toString, (i + 2).toString, (i + 3).toString)
})))).toDF("col", "arr")
.selectExpr("col", "struct(col, arr) as st")
.selectExpr("col", "st.col as col1", "explode(st.arr) as arr_col")
```
The explode causes `st` to be duplicated as many as the exploded elements.
Benchmarks it:
```
[info] Java HotSpot(TM) 64-Bit Server VM 1.8.0_202-b08 on Mac OS X 10.14.4
[info] Intel(R) Core(TM) i7-8750H CPU 2.20GHz
[info] generate big nested struct array: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] generate big nested struct array wholestage off 52668 53162 699 0.0 877803.4 1.0X
[info] generate big nested struct array wholestage on 47261 49093 1125 0.0 787690.2 1.1X
[info]
```
The query plan:
```
== Physical Plan ==
Project [col#508, st#512.col AS col1#515, arr_col#519]
+- Generate explode(st#512.arr), [col#508, st#512], false, [arr_col#519]
+- Project [_1#503 AS col#508, named_struct(col, _1#503, arr, _2#504) AS st#512]
+- SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(assertnotnull(input[0, scala.Tuple2, true]))._1, true, false) AS _1#503, mapobjects(MapObjects_loopValue84, MapObjects_loopIsNull84, ObjectType(class scala.Tuple4), if (isnull(lambdavariable(MapObjects_loopValue84, MapObjects_loopIsNull84, ObjectType(class scala.Tuple4), true))) null else named_struct(_1, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue84, MapObjects_loopIsNull84, ObjectType(class scala.Tuple4), true))._1, true, false), _2, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue84, MapObjects_loopIsNull84, ObjectType(class scala.Tuple4), true))._2, true, false), _3, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue84, MapObjects_loopIsNull84, ObjectType(class scala.Tuple4), true))._3, true, false), _4, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue84, MapObjects_loopIsNull84, ObjectType(class scala.Tuple4), true))._4, true, false)), knownnotnull(assertnotnull(input[0, scala.Tuple2, true]))._2, None) AS _2#504]
+- Scan[obj#534]
```
This patch takes nested column pruning approach to prune unnecessary nested fields. It adds a projection of the needed nested fields as aliases on the child of `Generate`, and substitutes them by alias attributes on the projection on top of `Generate`.
Benchmarks it after the change:
```
[info] Java HotSpot(TM) 64-Bit Server VM 1.8.0_202-b08 on Mac OS X 10.14.4
[info] Intel(R) Core(TM) i7-8750H CPU 2.20GHz
[info] generate big nested struct array: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
[info] ------------------------------------------------------------------------------------------------------------------------
[info] generate big nested struct array wholestage off 311 331 28 0.2 5188.6 1.0X
[info] generate big nested struct array wholestage on 297 312 15 0.2 4947.3 1.0X
[info]
```
The query plan:
```
== Physical Plan ==
Project [col#592, _gen_alias_608#608 AS col1#599, arr_col#603]
+- Generate explode(st#596.arr), [col#592, _gen_alias_608#608], false, [arr_col#603]
+- Project [_1#587 AS col#592, named_struct(col, _1#587, arr, _2#588) AS st#596, _1#587 AS _gen_alias_608#608]
+- SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(assertnotnull(in
put[0, scala.Tuple2, true]))._1, true, false) AS _1#587, mapobjects(MapObjects_loopValue102, MapObjects_loopIsNull102, ObjectType(class scala.Tuple4),
if (isnull(lambdavariable(MapObjects_loopValue102, MapObjects_loopIsNull102, ObjectType(class scala.Tuple4), true))) null else named_struct(_1, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue102, MapObjects_loopIsNull102, ObjectType(class scala.Tuple4), true))._1, true, false), _2, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue102, MapObjects_loopIsNull102, ObjectType(class scala.Tuple4), true))._2, true, false), _3, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue102, MapObjects_loopIsNull102, ObjectType(class scala.Tuple4), true))._3, true, false), _4, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(lambdavariable(MapObjects_loopValue102, MapObjects_loopIsNull102, ObjectType(class scala.Tuple4), true))._4, true, false)), knownnotnull(assertnotnull(input[0, scala.Tuple2, true]))._2, None) AS _2#588]
+- Scan[obj#586]
```
This behavior is controlled by a SQL config `spark.sql.optimizer.expression.nestedPruning.enabled`.
## How was this patch tested?
Added benchmark.
Closes#24637 from viirya/SPARK-27707.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR adds some tests converted from ```except.sql``` to test UDFs. Please see contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
<details><summary>Diff comparing to 'except.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/except.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-except.sql.out
index c9b712d4d2..27ca7ea226 100644
--- a/sql/core/src/test/resources/sql-tests/results/except.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-except.sql.out
-30,16 +30,16 struct<>
-- !query 2
-SELECT * FROM t1 EXCEPT SELECT * FROM t2
+SELECT udf(k), udf(v) FROM t1 EXCEPT SELECT udf(k), udf(v) FROM t2
-- !query 2 schema
-struct<k:string,v:int>
+struct<CAST(udf(cast(k as string)) AS STRING):string,CAST(udf(cast(v as string)) AS INT):int>
-- !query 2 output
three 3
two 2
-- !query 3
-SELECT * FROM t1 EXCEPT SELECT * FROM t1 where v <> 1 and v <> 2
+SELECT * FROM t1 EXCEPT SELECT * FROM t1 where udf(v) <> 1 and v <> udf(2)
-- !query 3 schema
struct<k:string,v:int>
-- !query 3 output
-49,7 +49,7 two 2
-- !query 4
-SELECT * FROM t1 where v <> 1 and v <> 22 EXCEPT SELECT * FROM t1 where v <> 2 and v >= 3
+SELECT * FROM t1 where udf(v) <> 1 and v <> udf(22) EXCEPT SELECT * FROM t1 where udf(v) <> 2 and v >= udf(3)
-- !query 4 schema
struct<k:string,v:int>
-- !query 4 output
-59,7 +59,7 two 2
-- !query 5
SELECT t1.* FROM t1, t2 where t1.k = t2.k
EXCEPT
-SELECT t1.* FROM t1, t2 where t1.k = t2.k and t1.k != 'one'
+SELECT t1.* FROM t1, t2 where t1.k = t2.k and t1.k != udf('one')
-- !query 5 schema
struct<k:string,v:int>
-- !query 5 output
-68,7 +68,7 one NULL
-- !query 6
-SELECT * FROM t2 where v >= 1 and v <> 22 EXCEPT SELECT * FROM t1
+SELECT * FROM t2 where v >= udf(1) and udf(v) <> 22 EXCEPT SELECT * FROM t1
-- !query 6 schema
struct<k:string,v:int>
-- !query 6 output
-77,9 +77,9 one 5
-- !query 7
-SELECT (SELECT min(k) FROM t2 WHERE t2.k = t1.k) min_t2 FROM t1
+SELECT (SELECT min(udf(k)) FROM t2 WHERE t2.k = t1.k) min_t2 FROM t1
MINUS
-SELECT (SELECT min(k) FROM t2) abs_min_t2 FROM t1 WHERE t1.k = 'one'
+SELECT (SELECT udf(min(k)) FROM t2) abs_min_t2 FROM t1 WHERE t1.k = udf('one')
-- !query 7 schema
struct<min_t2:string>
-- !query 7 output
-90,16 +90,17 two
-- !query 8
SELECT t1.k
FROM t1
-WHERE t1.v <= (SELECT max(t2.v)
+WHERE t1.v <= (SELECT udf(max(udf(t2.v)))
FROM t2
- WHERE t2.k = t1.k)
+ WHERE udf(t2.k) = udf(t1.k))
MINUS
SELECT t1.k
FROM t1
-WHERE t1.v >= (SELECT min(t2.v)
+WHERE udf(t1.v) >= (SELECT min(udf(t2.v))
FROM t2
WHERE t2.k = t1.k)
-- !query 8 schema
-struct<k:string>
+struct<>
-- !query 8 output
-two
+java.lang.UnsupportedOperationException
+Cannot evaluate expression: udf(cast(null as string))
```
</p>
</details>
## How was this patch tested?
Tested as guided in [SPARK-27921.](https://issues.apache.org/jira/browse/SPARK-27921)
Closes#25101 from huaxingao/spark-28277.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR adds some tests converted from ```outer-join.sql``` to test UDFs. Please see contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
<details><summary>Diff comparing to 'outer-join.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/outer-join.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-outer-join.sql.out
index 5db3bae5d0..819f786070 100644
--- a/sql/core/src/test/resources/sql-tests/results/outer-join.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-outer-join.sql.out
-24,17 +24,17 struct<>
-- !query 2
SELECT
- (SUM(COALESCE(t1.int_col1, t2.int_col0))),
- ((COALESCE(t1.int_col1, t2.int_col0)) * 2)
+ (udf(SUM(udf(COALESCE(t1.int_col1, t2.int_col0))))),
+ (udf(COALESCE(t1.int_col1, t2.int_col0)) * 2)
FROM t1
RIGHT JOIN t2
- ON (t2.int_col0) = (t1.int_col1)
-GROUP BY GREATEST(COALESCE(t2.int_col1, 109), COALESCE(t1.int_col1, -449)),
+ ON udf(t2.int_col0) = udf(t1.int_col1)
+GROUP BY udf(GREATEST(COALESCE(udf(t2.int_col1), 109), COALESCE(t1.int_col1, udf(-449)))),
COALESCE(t1.int_col1, t2.int_col0)
-HAVING (SUM(COALESCE(t1.int_col1, t2.int_col0)))
- > ((COALESCE(t1.int_col1, t2.int_col0)) * 2)
+HAVING (udf(SUM(COALESCE(udf(t1.int_col1), udf(t2.int_col0)))))
+ > (udf(COALESCE(t1.int_col1, t2.int_col0)) * 2)
-- !query 2 schema
-struct<sum(coalesce(int_col1, int_col0)):bigint,(coalesce(int_col1, int_col0) * 2):int>
+struct<CAST(udf(cast(sum(cast(cast(udf(cast(coalesce(int_col1, int_col0) as string)) as int) as bigint)) as string)) AS BIGINT):bigint,(CAST(udf(cast(coalesce(int_col1, int_col0) as string)) AS INT) * 2):int>
-- !query 2 output
-367 -734
-507 -1014
-70,10 +70,10 spark.sql.crossJoin.enabled true
SELECT *
FROM (
SELECT
- COALESCE(t2.int_col1, t1.int_col1) AS int_col
+ udf(COALESCE(udf(t2.int_col1), udf(t1.int_col1))) AS int_col
FROM t1
LEFT JOIN t2 ON false
-) t where (t.int_col) is not null
+) t where (udf(t.int_col)) is not null
-- !query 6 schema
struct<int_col:int>
-- !query 6 output
```
</p>
</details>
## How was this patch tested?
Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
Closes#25103 from huaxingao/spark-28285.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR adds some tests converted from 'udaf.sql' to test UDFs
<details><summary>Diff comparing to 'udaf.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/udaf.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-udaf.sql.out
index f4455bb717..e1747f4667 100644
--- a/sql/core/src/test/resources/sql-tests/results/udaf.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-udaf.sql.out
-3,6 +3,8
-- !query 0
+-- This test file was converted from udaf.sql.
+
CREATE OR REPLACE TEMPORARY VIEW t1 AS SELECT * FROM VALUES
(1), (2), (3), (4)
as t1(int_col1)
-21,15 +23,15 struct<>
-- !query 2
-SELECT default.myDoubleAvg(int_col1) as my_avg from t1
+SELECT default.myDoubleAvg(udf(int_col1)) as my_avg, udf(default.myDoubleAvg(udf(int_col1))) as my_avg2, udf(default.myDoubleAvg(int_col1)) as my_avg3 from t1
-- !query 2 schema
-struct<my_avg:double>
+struct<my_avg:double,my_avg2:double,my_avg3:double>
-- !query 2 output
-102.5
+102.5 102.5 102.5
-- !query 3
-SELECT default.myDoubleAvg(int_col1, 3) as my_avg from t1
+SELECT default.myDoubleAvg(udf(int_col1), udf(3)) as my_avg from t1
-- !query 3 schema
struct<>
-- !query 3 output
-46,12 +48,12 struct<>
-- !query 5
-SELECT default.udaf1(int_col1) as udaf1 from t1
+SELECT default.udaf1(udf(int_col1)) as udaf1, udf(default.udaf1(udf(int_col1))) as udaf2, udf(default.udaf1(int_col1)) as udaf3 from t1
-- !query 5 schema
struct<>
-- !query 5 output
org.apache.spark.sql.AnalysisException
-Can not load class 'test.non.existent.udaf' when registering the function 'default.udaf1', please make sure it is on the classpath; line 1 pos 7
+Can not load class 'test.non.existent.udaf' when registering the function 'default.udaf1', please make sure it is on the classpath; line 1 pos 94
-- !query 6
```
</p>
</details>
## How was this patch tested?
Tested as guided in SPARK-27921.
Closes#25194 from vinodkc/br_Fix_SPARK-27921_3.
Authored-by: Vinod KC <vinod.kc.in@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR adds some tests converted from pivot.sql to test UDFs following the combination guide in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
<details><summary>Diff comparing to 'pivot.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/pivot.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-pivot.sql.out
index 9a8f783da4..cb9e4d736c 100644
--- a/sql/core/src/test/resources/sql-tests/results/pivot.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-pivot.sql.out
-1,5 +1,5
-- Automatically generated by SQLQueryTestSuite
--- Number of queries: 32
+-- Number of queries: 30
-- !query 0
-40,14 +40,14 struct<>
-- !query 3
SELECT * FROM (
- SELECT year, course, earnings FROM courseSales
+ SELECT udf(year), course, earnings FROM courseSales
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR course IN ('dotNET', 'Java')
)
-- !query 3 schema
-struct<year:int,dotNET:bigint,Java:bigint>
+struct<CAST(udf(cast(year as string)) AS INT):int,dotNET:bigint,Java:bigint>
-- !query 3 output
2012 15000 20000
2013 48000 30000
-56,7 +56,7 struct<year:int,dotNET:bigint,Java:bigint>
-- !query 4
SELECT * FROM courseSales
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR year IN (2012, 2013)
)
-- !query 4 schema
-71,11 +71,11 SELECT * FROM (
SELECT year, course, earnings FROM courseSales
)
PIVOT (
- sum(earnings), avg(earnings)
+ udf(sum(earnings)), udf(avg(earnings))
FOR course IN ('dotNET', 'Java')
)
-- !query 5 schema
-struct<year:int,dotNET_sum(CAST(earnings AS BIGINT)):bigint,dotNET_avg(CAST(earnings AS BIGINT)):double,Java_sum(CAST(earnings AS BIGINT)):bigint,Java_avg(CAST(earnings AS BIGINT)):double>
+struct<year:int,dotNET_CAST(udf(cast(sum(cast(earnings as bigint)) as string)) AS BIGINT):bigint,dotNET_CAST(udf(cast(avg(cast(earnings as bigint)) as string)) AS DOUBLE):double,Java_CAST(udf(cast(sum(cast(earnings as bigint)) as string)) AS BIGINT):bigint,Java_CAST(udf(cast(avg(cast(earnings as bigint)) as string)) AS DOUBLE):double>
-- !query 5 output
2012 15000 7500.0 20000 20000.0
2013 48000 48000.0 30000 30000.0
-83,10 +83,10 struct<year:int,dotNET_sum(CAST(earnings AS BIGINT)):bigint,dotNET_avg(CAST(earn
-- !query 6
SELECT * FROM (
- SELECT course, earnings FROM courseSales
+ SELECT udf(course) as course, earnings FROM courseSales
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR course IN ('dotNET', 'Java')
)
-- !query 6 schema
-100,23 +100,23 SELECT * FROM (
SELECT year, course, earnings FROM courseSales
)
PIVOT (
- sum(earnings), min(year)
+ udf(sum(udf(earnings))), udf(min(year))
FOR course IN ('dotNET', 'Java')
)
-- !query 7 schema
-struct<dotNET_sum(CAST(earnings AS BIGINT)):bigint,dotNET_min(year):int,Java_sum(CAST(earnings AS BIGINT)):bigint,Java_min(year):int>
+struct<dotNET_CAST(udf(cast(sum(cast(cast(udf(cast(earnings as string)) as int) as bigint)) as string)) AS BIGINT):bigint,dotNET_CAST(udf(cast(min(year) as string)) AS INT):int,Java_CAST(udf(cast(sum(cast(cast(udf(cast(earnings as string)) as int) as bigint)) as string)) AS BIGINT):bigint,Java_CAST(udf(cast(min(year) as string)) AS INT):int>
-- !query 7 output
63000 2012 50000 2012
-- !query 8
SELECT * FROM (
- SELECT course, year, earnings, s
+ SELECT course, year, earnings, udf(s) as s
FROM courseSales
JOIN years ON year = y
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR s IN (1, 2)
)
-- !query 8 schema
-135,11 +135,11 SELECT * FROM (
JOIN years ON year = y
)
PIVOT (
- sum(earnings), min(s)
+ udf(sum(earnings)), udf(min(s))
FOR course IN ('dotNET', 'Java')
)
-- !query 9 schema
-struct<year:int,dotNET_sum(CAST(earnings AS BIGINT)):bigint,dotNET_min(s):int,Java_sum(CAST(earnings AS BIGINT)):bigint,Java_min(s):int>
+struct<year:int,dotNET_CAST(udf(cast(sum(cast(earnings as bigint)) as string)) AS BIGINT):bigint,dotNET_CAST(udf(cast(min(s) as string)) AS INT):int,Java_CAST(udf(cast(sum(cast(earnings as bigint)) as string)) AS BIGINT):bigint,Java_CAST(udf(cast(min(s) as string)) AS INT):int>
-- !query 9 output
2012 15000 1 20000 1
2013 48000 2 30000 2
-152,7 +152,7 SELECT * FROM (
JOIN years ON year = y
)
PIVOT (
- sum(earnings * s)
+ udf(sum(earnings * s))
FOR course IN ('dotNET', 'Java')
)
-- !query 10 schema
-167,7 +167,7 SELECT 2012_s, 2013_s, 2012_a, 2013_a, c FROM (
SELECT year y, course c, earnings e FROM courseSales
)
PIVOT (
- sum(e) s, avg(e) a
+ udf(sum(e)) s, udf(avg(e)) a
FOR y IN (2012, 2013)
)
-- !query 11 schema
-182,7 +182,7 SELECT firstYear_s, secondYear_s, firstYear_a, secondYear_a, c FROM (
SELECT year y, course c, earnings e FROM courseSales
)
PIVOT (
- sum(e) s, avg(e) a
+ udf(sum(e)) s, udf(avg(e)) a
FOR y IN (2012 as firstYear, 2013 secondYear)
)
-- !query 12 schema
-195,7 +195,7 struct<firstYear_s:bigint,secondYear_s:bigint,firstYear_a:double,secondYear_a:do
-- !query 13
SELECT * FROM courseSales
PIVOT (
- abs(earnings)
+ udf(abs(earnings))
FOR year IN (2012, 2013)
)
-- !query 13 schema
-210,7 +210,7 SELECT * FROM (
SELECT year, course, earnings FROM courseSales
)
PIVOT (
- sum(earnings), year
+ udf(sum(earnings)), year
FOR course IN ('dotNET', 'Java')
)
-- !query 14 schema
-225,7 +225,7 SELECT * FROM (
SELECT course, earnings FROM courseSales
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR year IN (2012, 2013)
)
-- !query 15 schema
-240,11 +240,11 SELECT * FROM (
SELECT year, course, earnings FROM courseSales
)
PIVOT (
- ceil(sum(earnings)), avg(earnings) + 1 as a1
+ udf(ceil(udf(sum(earnings)))), avg(earnings) + 1 as a1
FOR course IN ('dotNET', 'Java')
)
-- !query 16 schema
-struct<year:int,dotNET_CEIL(sum(CAST(earnings AS BIGINT))):bigint,dotNET_a1:double,Java_CEIL(sum(CAST(earnings AS BIGINT))):bigint,Java_a1:double>
+struct<year:int,dotNET_CAST(udf(cast(CEIL(cast(udf(cast(sum(cast(earnings as bigint)) as string)) as bigint)) as string)) AS BIGINT):bigint,dotNET_a1:double,Java_CAST(udf(cast(CEIL(cast(udf(cast(sum(cast(earnings as bigint)) as string)) as bigint)) as string)) AS BIGINT):bigint,Java_a1:double>
-- !query 16 output
2012 15000 7501.0 20000 20001.0
2013 48000 48001.0 30000 30001.0
-255,7 +255,7 SELECT * FROM (
SELECT year, course, earnings FROM courseSales
)
PIVOT (
- sum(avg(earnings))
+ sum(udf(avg(earnings)))
FOR course IN ('dotNET', 'Java')
)
-- !query 17 schema
-272,7 +272,7 SELECT * FROM (
JOIN years ON year = y
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR (course, year) IN (('dotNET', 2012), ('Java', 2013))
)
-- !query 18 schema
-289,7 +289,7 SELECT * FROM (
JOIN years ON year = y
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR (course, s) IN (('dotNET', 2) as c1, ('Java', 1) as c2)
)
-- !query 19 schema
-306,7 +306,7 SELECT * FROM (
JOIN years ON year = y
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR (course, year) IN ('dotNET', 'Java')
)
-- !query 20 schema
-319,7 +319,7 Invalid pivot value 'dotNET': value data type string does not match pivot column
-- !query 21
SELECT * FROM courseSales
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR year IN (s, 2013)
)
-- !query 21 schema
-332,7 +332,7 cannot resolve '`s`' given input columns: [coursesales.course, coursesales.earni
-- !query 22
SELECT * FROM courseSales
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR year IN (course, 2013)
)
-- !query 22 schema
-343,151 +343,118 Literal expressions required for pivot values, found 'course#x';
-- !query 23
-SELECT * FROM (
- SELECT course, year, a
- FROM courseSales
- JOIN yearsWithComplexTypes ON year = y
-)
-PIVOT (
- min(a)
- FOR course IN ('dotNET', 'Java')
-)
--- !query 23 schema
-struct<year:int,dotNET:array<int>,Java:array<int>>
--- !query 23 output
-2012 [1,1] [1,1]
-2013 [2,2] [2,2]
-
-
--- !query 24
-SELECT * FROM (
- SELECT course, year, y, a
- FROM courseSales
- JOIN yearsWithComplexTypes ON year = y
-)
-PIVOT (
- max(a)
- FOR (y, course) IN ((2012, 'dotNET'), (2013, 'Java'))
-)
--- !query 24 schema
-struct<year:int,[2012, dotNET]:array<int>,[2013, Java]:array<int>>
--- !query 24 output
-2012 [1,1] NULL
-2013 NULL [2,2]
-
-
--- !query 25
SELECT * FROM (
SELECT earnings, year, a
FROM courseSales
JOIN yearsWithComplexTypes ON year = y
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR a IN (array(1, 1), array(2, 2))
)
--- !query 25 schema
+-- !query 23 schema
struct<year:int,[1, 1]:bigint,[2, 2]:bigint>
--- !query 25 output
+-- !query 23 output
2012 35000 NULL
2013 NULL 78000
--- !query 26
+-- !query 24
SELECT * FROM (
- SELECT course, earnings, year, a
+ SELECT course, earnings, udf(year) as year, a
FROM courseSales
JOIN yearsWithComplexTypes ON year = y
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR (course, a) IN (('dotNET', array(1, 1)), ('Java', array(2, 2)))
)
--- !query 26 schema
+-- !query 24 schema
struct<year:int,[dotNET, [1, 1]]:bigint,[Java, [2, 2]]:bigint>
--- !query 26 output
+-- !query 24 output
2012 15000 NULL
2013 NULL 30000
--- !query 27
+-- !query 25
SELECT * FROM (
SELECT earnings, year, s
FROM courseSales
JOIN yearsWithComplexTypes ON year = y
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR s IN ((1, 'a'), (2, 'b'))
)
--- !query 27 schema
+-- !query 25 schema
struct<year:int,[1, a]:bigint,[2, b]:bigint>
--- !query 27 output
+-- !query 25 output
2012 35000 NULL
2013 NULL 78000
--- !query 28
+-- !query 26
SELECT * FROM (
SELECT course, earnings, year, s
FROM courseSales
JOIN yearsWithComplexTypes ON year = y
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR (course, s) IN (('dotNET', (1, 'a')), ('Java', (2, 'b')))
)
--- !query 28 schema
+-- !query 26 schema
struct<year:int,[dotNET, [1, a]]:bigint,[Java, [2, b]]:bigint>
--- !query 28 output
+-- !query 26 output
2012 15000 NULL
2013 NULL 30000
--- !query 29
+-- !query 27
SELECT * FROM (
SELECT earnings, year, m
FROM courseSales
JOIN yearsWithComplexTypes ON year = y
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR m IN (map('1', 1), map('2', 2))
)
--- !query 29 schema
+-- !query 27 schema
struct<>
--- !query 29 output
+-- !query 27 output
org.apache.spark.sql.AnalysisException
Invalid pivot column 'm#x'. Pivot columns must be comparable.;
--- !query 30
+-- !query 28
SELECT * FROM (
SELECT course, earnings, year, m
FROM courseSales
JOIN yearsWithComplexTypes ON year = y
)
PIVOT (
- sum(earnings)
+ udf(sum(earnings))
FOR (course, m) IN (('dotNET', map('1', 1)), ('Java', map('2', 2)))
)
--- !query 30 schema
+-- !query 28 schema
struct<>
--- !query 30 output
+-- !query 28 output
org.apache.spark.sql.AnalysisException
Invalid pivot column 'named_struct(course, course#x, m, m#x)'. Pivot columns must be comparable.;
--- !query 31
+-- !query 29
SELECT * FROM (
- SELECT course, earnings, "a" as a, "z" as z, "b" as b, "y" as y, "c" as c, "x" as x, "d" as d, "w" as w
+ SELECT course, earnings, udf("a") as a, udf("z") as z, udf("b") as b, udf("y") as y,
+ udf("c") as c, udf("x") as x, udf("d") as d, udf("w") as w
FROM courseSales
)
PIVOT (
- sum(Earnings)
+ udf(sum(Earnings))
FOR Course IN ('dotNET', 'Java')
)
--- !query 31 schema
+-- !query 29 schema
struct<a:string,z:string,b:string,y:string,c:string,x:string,d:string,w:string,dotNET:bigint,Java:bigint>
--- !query 31 output
+-- !query 29 output
a z b y c x d w 63000 50000
```
</p>
</details>
## How was this patch tested?
Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
Closes#25122 from chitralverma/SPARK-28286.
Authored-by: chitralverma <chitralverma@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR adds some tests converted from `except-all.sql` to test UDFs. Please see contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
<details><summary>Diff comparing to 'except-all.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/except-all.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-except-all.sql.out
index 01091a2f75..b7bfad0e53 100644
--- a/sql/core/src/test/resources/sql-tests/results/except-all.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-except-all.sql.out
-49,11 +49,11 struct<>
-- !query 4
-SELECT * FROM tab1
+SELECT udf(c1) FROM tab1
EXCEPT ALL
-SELECT * FROM tab2
+SELECT udf(c1) FROM tab2
-- !query 4 schema
-struct<c1:int>
+struct<CAST(udf(cast(c1 as string)) AS INT):int>
-- !query 4 output
0
2
-62,11 +62,11 NULL
-- !query 5
-SELECT * FROM tab1
+SELECT udf(c1) FROM tab1
MINUS ALL
-SELECT * FROM tab2
+SELECT udf(c1) FROM tab2
-- !query 5 schema
-struct<c1:int>
+struct<CAST(udf(cast(c1 as string)) AS INT):int>
-- !query 5 output
0
2
-75,11 +75,11 NULL
-- !query 6
-SELECT * FROM tab1
+SELECT udf(c1) FROM tab1
EXCEPT ALL
-SELECT * FROM tab2 WHERE c1 IS NOT NULL
+SELECT udf(c1) FROM tab2 WHERE udf(c1) IS NOT NULL
-- !query 6 schema
-struct<c1:int>
+struct<CAST(udf(cast(c1 as string)) AS INT):int>
-- !query 6 output
0
2
-89,21 +89,21 NULL
-- !query 7
-SELECT * FROM tab1 WHERE c1 > 5
+SELECT udf(c1) FROM tab1 WHERE udf(c1) > 5
EXCEPT ALL
-SELECT * FROM tab2
+SELECT udf(c1) FROM tab2
-- !query 7 schema
-struct<c1:int>
+struct<CAST(udf(cast(c1 as string)) AS INT):int>
-- !query 7 output
-- !query 8
-SELECT * FROM tab1
+SELECT udf(c1) FROM tab1
EXCEPT ALL
-SELECT * FROM tab2 WHERE c1 > 6
+SELECT udf(c1) FROM tab2 WHERE udf(c1 > udf(6))
-- !query 8 schema
-struct<c1:int>
+struct<CAST(udf(cast(c1 as string)) AS INT):int>
-- !query 8 output
0
1
-117,11 +117,11 NULL
-- !query 9
-SELECT * FROM tab1
+SELECT udf(c1) FROM tab1
EXCEPT ALL
-SELECT CAST(1 AS BIGINT)
+SELECT CAST(udf(1) AS BIGINT)
-- !query 9 schema
-struct<c1:bigint>
+struct<CAST(udf(cast(c1 as string)) AS INT):bigint>
-- !query 9 output
0
2
-134,7 +134,7 NULL
-- !query 10
-SELECT * FROM tab1
+SELECT udf(c1) FROM tab1
EXCEPT ALL
SELECT array(1)
-- !query 10 schema
-145,62 +145,62 ExceptAll can only be performed on tables with the compatible column types. arra
-- !query 11
-SELECT * FROM tab3
+SELECT udf(k), v FROM tab3
EXCEPT ALL
-SELECT * FROM tab4
+SELECT k, udf(v) FROM tab4
-- !query 11 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,v:int>
-- !query 11 output
1 2
1 3
-- !query 12
-SELECT * FROM tab4
+SELECT k, udf(v) FROM tab4
EXCEPT ALL
-SELECT * FROM tab3
+SELECT udf(k), v FROM tab3
-- !query 12 schema
-struct<k:int,v:int>
+struct<k:int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 12 output
2 2
2 20
-- !query 13
-SELECT * FROM tab4
+SELECT udf(k), udf(v) FROM tab4
EXCEPT ALL
-SELECT * FROM tab3
+SELECT udf(k), udf(v) FROM tab3
INTERSECT DISTINCT
-SELECT * FROM tab4
+SELECT udf(k), udf(v) FROM tab4
-- !query 13 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 13 output
2 2
2 20
-- !query 14
-SELECT * FROM tab4
+SELECT udf(k), v FROM tab4
EXCEPT ALL
-SELECT * FROM tab3
+SELECT k, udf(v) FROM tab3
EXCEPT DISTINCT
-SELECT * FROM tab4
+SELECT udf(k), udf(v) FROM tab4
-- !query 14 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,v:int>
-- !query 14 output
-- !query 15
-SELECT * FROM tab3
+SELECT k, udf(v) FROM tab3
EXCEPT ALL
-SELECT * FROM tab4
+SELECT udf(k), udf(v) FROM tab4
UNION ALL
-SELECT * FROM tab3
+SELECT udf(k), v FROM tab3
EXCEPT DISTINCT
-SELECT * FROM tab4
+SELECT k, udf(v) FROM tab4
-- !query 15 schema
-struct<k:int,v:int>
+struct<k:int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 15 output
1 3
-217,83 +217,83 ExceptAll can only be performed on tables with the same number of columns, but t
-- !query 17
-SELECT * FROM tab3
+SELECT udf(k), udf(v) FROM tab3
EXCEPT ALL
-SELECT * FROM tab4
+SELECT udf(k), udf(v) FROM tab4
UNION
-SELECT * FROM tab3
+SELECT udf(k), udf(v) FROM tab3
EXCEPT DISTINCT
-SELECT * FROM tab4
+SELECT udf(k), udf(v) FROM tab4
-- !query 17 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 17 output
1 3
-- !query 18
-SELECT * FROM tab3
+SELECT udf(k), udf(v) FROM tab3
MINUS ALL
-SELECT * FROM tab4
+SELECT k, udf(v) FROM tab4
UNION
-SELECT * FROM tab3
+SELECT udf(k), udf(v) FROM tab3
MINUS DISTINCT
-SELECT * FROM tab4
+SELECT k, udf(v) FROM tab4
-- !query 18 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 18 output
1 3
-- !query 19
-SELECT * FROM tab3
+SELECT k, udf(v) FROM tab3
EXCEPT ALL
-SELECT * FROM tab4
+SELECT udf(k), v FROM tab4
EXCEPT DISTINCT
-SELECT * FROM tab3
+SELECT k, udf(v) FROM tab3
EXCEPT DISTINCT
-SELECT * FROM tab4
+SELECT udf(k), v FROM tab4
-- !query 19 schema
-struct<k:int,v:int>
+struct<k:int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 19 output
-- !query 20
SELECT *
-FROM (SELECT tab3.k,
- tab4.v
+FROM (SELECT tab3.k,
+ udf(tab4.v)
FROM tab3
JOIN tab4
- ON tab3.k = tab4.k)
+ ON udf(tab3.k) = tab4.k)
EXCEPT ALL
SELECT *
-FROM (SELECT tab3.k,
- tab4.v
+FROM (SELECT udf(tab3.k),
+ tab4.v
FROM tab3
JOIN tab4
- ON tab3.k = tab4.k)
+ ON tab3.k = udf(tab4.k))
-- !query 20 schema
-struct<k:int,v:int>
+struct<k:int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 20 output
-- !query 21
SELECT *
-FROM (SELECT tab3.k,
- tab4.v
+FROM (SELECT udf(udf(tab3.k)),
+ udf(tab4.v)
FROM tab3
JOIN tab4
- ON tab3.k = tab4.k)
+ ON udf(udf(tab3.k)) = udf(tab4.k))
EXCEPT ALL
SELECT *
-FROM (SELECT tab4.v AS k,
- tab3.k AS v
+FROM (SELECT udf(tab4.v) AS k,
+ udf(udf(tab3.k)) AS v
FROM tab3
JOIN tab4
- ON tab3.k = tab4.k)
+ ON udf(tab3.k) = udf(tab4.k))
-- !query 21 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(cast(udf(cast(k as string)) as int) as string)) AS INT):int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 21 output
1 2
1 2
-305,11 +305,11 struct<k:int,v:int>
-- !query 22
-SELECT v FROM tab3 GROUP BY v
+SELECT udf(v) FROM tab3 GROUP BY v
EXCEPT ALL
-SELECT k FROM tab4 GROUP BY k
+SELECT udf(k) FROM tab4 GROUP BY k
-- !query 22 schema
-struct<v:int>
+struct<CAST(udf(cast(v as string)) AS INT):int>
-- !query 22 output
3
```
</p>
</details>
## How was this patch tested?
Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
Closes#25090 from imback82/except-all.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR adds some tests converted from `intersect-all.sql` to test UDFs. Please see contribution guide of this umbrella ticket - [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
<details><summary>Diff comparing to 'intersect-all.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/intersect-all.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-intersect-all.sql.out
index 63dd56ce46..0cb82be2da 100644
--- a/sql/core/src/test/resources/sql-tests/results/intersect-all.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-intersect-all.sql.out
-34,11 +34,11 struct<>
-- !query 2
-SELECT * FROM tab1
+SELECT udf(k), v FROM tab1
INTERSECT ALL
-SELECT * FROM tab2
+SELECT k, udf(v) FROM tab2
-- !query 2 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,v:int>
-- !query 2 output
1 2
1 2
-48,11 +48,11 NULL NULL
-- !query 3
-SELECT * FROM tab1
+SELECT k, udf(v) FROM tab1
INTERSECT ALL
-SELECT * FROM tab1 WHERE k = 1
+SELECT udf(k), v FROM tab1 WHERE udf(k) = 1
-- !query 3 schema
-struct<k:int,v:int>
+struct<k:int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 3 output
1 2
1 2
-61,39 +61,39 struct<k:int,v:int>
-- !query 4
-SELECT * FROM tab1 WHERE k > 2
+SELECT udf(k), udf(v) FROM tab1 WHERE k > udf(2)
INTERSECT ALL
-SELECT * FROM tab2
+SELECT udf(k), udf(v) FROM tab2
-- !query 4 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 4 output
-- !query 5
-SELECT * FROM tab1
+SELECT udf(k), v FROM tab1
INTERSECT ALL
-SELECT * FROM tab2 WHERE k > 3
+SELECT udf(k), v FROM tab2 WHERE udf(udf(k)) > 3
-- !query 5 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,v:int>
-- !query 5 output
-- !query 6
-SELECT * FROM tab1
+SELECT udf(k), v FROM tab1
INTERSECT ALL
-SELECT CAST(1 AS BIGINT), CAST(2 AS BIGINT)
+SELECT CAST(udf(1) AS BIGINT), CAST(udf(2) AS BIGINT)
-- !query 6 schema
-struct<k:bigint,v:bigint>
+struct<CAST(udf(cast(k as string)) AS INT):bigint,v:bigint>
-- !query 6 output
1 2
-- !query 7
-SELECT * FROM tab1
+SELECT k, udf(v) FROM tab1
INTERSECT ALL
-SELECT array(1), 2
+SELECT array(1), udf(2)
-- !query 7 schema
struct<>
-- !query 7 output
-102,9 +102,9 IntersectAll can only be performed on tables with the compatible column types. a
-- !query 8
-SELECT k FROM tab1
+SELECT udf(k) FROM tab1
INTERSECT ALL
-SELECT k, v FROM tab2
+SELECT udf(k), udf(v) FROM tab2
-- !query 8 schema
struct<>
-- !query 8 output
-113,13 +113,13 IntersectAll can only be performed on tables with the same number of columns, bu
-- !query 9
-SELECT * FROM tab2
+SELECT udf(k), v FROM tab2
INTERSECT ALL
-SELECT * FROM tab1
+SELECT k, udf(v) FROM tab1
INTERSECT ALL
-SELECT * FROM tab2
+SELECT udf(k), udf(v) FROM tab2
-- !query 9 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,v:int>
-- !query 9 output
1 2
1 2
-129,15 +129,15 NULL NULL
-- !query 10
-SELECT * FROM tab1
+SELECT udf(k), v FROM tab1
EXCEPT
-SELECT * FROM tab2
+SELECT k, udf(v) FROM tab2
UNION ALL
-SELECT * FROM tab1
+SELECT k, udf(udf(v)) FROM tab1
INTERSECT ALL
-SELECT * FROM tab2
+SELECT udf(k), v FROM tab2
-- !query 10 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,v:int>
-- !query 10 output
1 2
1 2
-148,15 +148,15 NULL NULL
-- !query 11
-SELECT * FROM tab1
+SELECT udf(k), udf(v) FROM tab1
EXCEPT
-SELECT * FROM tab2
+SELECT udf(k), v FROM tab2
EXCEPT
-SELECT * FROM tab1
+SELECT k, udf(v) FROM tab1
INTERSECT ALL
-SELECT * FROM tab2
+SELECT udf(k), udf(udf(v)) FROM tab2
-- !query 11 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 11 output
1 3
-165,38 +165,38 struct<k:int,v:int>
(
(
(
- SELECT * FROM tab1
+ SELECT udf(k), v FROM tab1
EXCEPT
- SELECT * FROM tab2
+ SELECT k, udf(v) FROM tab2
)
EXCEPT
- SELECT * FROM tab1
+ SELECT udf(k), udf(v) FROM tab1
)
INTERSECT ALL
- SELECT * FROM tab2
+ SELECT udf(k), udf(v) FROM tab2
)
-- !query 12 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,v:int>
-- !query 12 output
-- !query 13
SELECT *
-FROM (SELECT tab1.k,
- tab2.v
+FROM (SELECT udf(tab1.k),
+ udf(tab2.v)
FROM tab1
JOIN tab2
- ON tab1.k = tab2.k)
+ ON udf(udf(tab1.k)) = tab2.k)
INTERSECT ALL
SELECT *
-FROM (SELECT tab1.k,
- tab2.v
+FROM (SELECT udf(tab1.k),
+ udf(tab2.v)
FROM tab1
JOIN tab2
- ON tab1.k = tab2.k)
+ ON udf(tab1.k) = udf(udf(tab2.k)))
-- !query 13 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 13 output
1 2
1 2
-211,30 +211,30 struct<k:int,v:int>
-- !query 14
SELECT *
-FROM (SELECT tab1.k,
- tab2.v
+FROM (SELECT udf(tab1.k),
+ udf(tab2.v)
FROM tab1
JOIN tab2
- ON tab1.k = tab2.k)
+ ON udf(tab1.k) = udf(tab2.k))
INTERSECT ALL
SELECT *
-FROM (SELECT tab2.v AS k,
- tab1.k AS v
+FROM (SELECT udf(tab2.v) AS k,
+ udf(tab1.k) AS v
FROM tab1
JOIN tab2
- ON tab1.k = tab2.k)
+ ON tab1.k = udf(tab2.k))
-- !query 14 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 14 output
-- !query 15
-SELECT v FROM tab1 GROUP BY v
+SELECT udf(v) FROM tab1 GROUP BY v
INTERSECT ALL
-SELECT k FROM tab2 GROUP BY k
+SELECT udf(udf(k)) FROM tab2 GROUP BY k
-- !query 15 schema
-struct<v:int>
+struct<CAST(udf(cast(v as string)) AS INT):int>
-- !query 15 output
2
3
-250,15 +250,15 spark.sql.legacy.setopsPrecedence.enabled true
-- !query 17
-SELECT * FROM tab1
+SELECT udf(k), v FROM tab1
EXCEPT
-SELECT * FROM tab2
+SELECT k, udf(v) FROM tab2
UNION ALL
-SELECT * FROM tab1
+SELECT udf(k), udf(v) FROM tab1
INTERSECT ALL
-SELECT * FROM tab2
+SELECT udf(udf(k)), udf(v) FROM tab2
-- !query 17 schema
-struct<k:int,v:int>
+struct<CAST(udf(cast(k as string)) AS INT):int,v:int>
-- !query 17 output
1 2
1 2
-268,15 +268,15 NULL NULL
-- !query 18
-SELECT * FROM tab1
+SELECT k, udf(v) FROM tab1
EXCEPT
-SELECT * FROM tab2
+SELECT udf(k), v FROM tab2
UNION ALL
-SELECT * FROM tab1
+SELECT udf(k), udf(v) FROM tab1
INTERSECT
-SELECT * FROM tab2
+SELECT udf(k), udf(udf(v)) FROM tab2
-- !query 18 schema
-struct<k:int,v:int>
+struct<k:int,CAST(udf(cast(v as string)) AS INT):int>
-- !query 18 output
1 2
2 3
```
</p>
</details>
## How was this patch tested?
Tested as guided in [SPARK-27921](https://issues.apache.org/jira/browse/SPARK-27921).
Closes#25119 from imback82/intersect-all-sql.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR adds some tests converted from `cross-join.sql'` to test UDFs.
<details><summary>Diff comparing to 'cross-join.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/cross-join.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-cross-join.sql.out
index 3833c42bdf..11c1e01d54 100644
--- a/sql/core/src/test/resources/sql-tests/results/cross-join.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-cross-join.sql.out
-43,7 +43,7 two 2 two 22
-- !query 3
-SELECT * FROM nt1 cross join nt2 where nt1.k = nt2.k
+SELECT * FROM nt1 cross join nt2 where udf(nt1.k) = udf(nt2.k)
-- !query 3 schema
struct<k:string,v1:int,k:string,v2:int>
-- !query 3 output
-53,7 +53,7 two 2 two 22
-- !query 4
-SELECT * FROM nt1 cross join nt2 on (nt1.k = nt2.k)
+SELECT * FROM nt1 cross join nt2 on (udf(nt1.k) = udf(nt2.k))
-- !query 4 schema
struct<k:string,v1:int,k:string,v2:int>
-- !query 4 output
-63,7 +63,7 two 2 two 22
-- !query 5
-SELECT * FROM nt1 cross join nt2 where nt1.v1 = 1 and nt2.v2 = 22
+SELECT * FROM nt1 cross join nt2 where udf(nt1.v1) = "1" and udf(nt2.v2) = "22"
-- !query 5 schema
struct<k:string,v1:int,k:string,v2:int>
-- !query 5 output
-71,12 +71,12 one 1 two 22
-- !query 6
-SELECT a.key, b.key FROM
-(SELECT k key FROM nt1 WHERE v1 < 2) a
+SELECT udf(a.key), udf(b.key) FROM
+(SELECT udf(k) key FROM nt1 WHERE v1 < 2) a
CROSS JOIN
-(SELECT k key FROM nt2 WHERE v2 = 22) b
+(SELECT udf(k) key FROM nt2 WHERE v2 = 22) b
-- !query 6 schema
-struct<key:string,key:string>
+struct<udf(key):string,udf(key):string>
-- !query 6 output
one two
-114,23 +114,29 struct<>
-- !query 11
-select * from ((A join B on (a = b)) cross join C) join D on (a = d)
+select * from ((A join B on (udf(a) = udf(b))) cross join C) join D on (udf(a) = udf(d))
-- !query 11 schema
-struct<a:string,va:int,b:string,vb:int,c:string,vc:int,d:string,vd:int>
+struct<>
-- !query 11 output
-one 1 one 1 one 1 one 1
-one 1 one 1 three 3 one 1
-one 1 one 1 two 2 one 1
-three 3 three 3 one 1 three 3
-three 3 three 3 three 3 three 3
-three 3 three 3 two 2 three 3
-two 2 two 2 one 1 two 2
-two 2 two 2 three 3 two 2
-two 2 two 2 two 2 two 2
+org.apache.spark.sql.AnalysisException
+Detected implicit cartesian product for INNER join between logical plans
+Filter (udf(a#x) = udf(b#x))
++- Join Inner
+ :- Project [k#x AS a#x, v1#x AS va#x]
+ : +- LocalRelation [k#x, v1#x]
+ +- Project [k#x AS b#x, v1#x AS vb#x]
+ +- LocalRelation [k#x, v1#x]
+and
+Project [k#x AS d#x, v1#x AS vd#x]
++- LocalRelation [k#x, v1#x]
+Join condition is missing or trivial.
+Either: use the CROSS JOIN syntax to allow cartesian products between these
+relations, or: enable implicit cartesian products by setting the configuration
+variable spark.sql.crossJoin.enabled=true;
-- !query 12
-SELECT * FROM nt1 CROSS JOIN nt2 ON (nt1.k > nt2.k)
+SELECT * FROM nt1 CROSS JOIN nt2 ON (udf(nt1.k) > udf(nt2.k))
-- !query 12 schema
struct<k:string,v1:int,k:string,v2:int>
-- !query 12 output
```
</p>
</details>
## How was this patch tested?
Added test.
Closes#25168 from viirya/SPARK-28276.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
PostgreSQL doesn't have `TINYINT`, which would map directly, but `SMALLINT`s are sufficient for uni-directional translation.
A side-effect of this fix is that `AggregatedDialect` is now usable with multiple dialects targeting `jdbc:postgresql`, as `PostgresDialect.getJDBCType` no longer throws (for which reason backporting this fix would be lovely):
1217996f15/sql/core/src/main/scala/org/apache/spark/sql/jdbc/AggregatedDialect.scala (L42)
`dialects.flatMap` currently throws on the first attempt to get a JDBC type preventing subsequent dialects in the chain from providing an alternative.
## How was this patch tested?
Unit tests.
Closes#24845 from mojodna/postgres-byte-type-mapping.
Authored-by: Seth Fitzsimmons <seth@mojodna.net>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Current UDFs available in `IntegratedUDFTestUtils` are not exactly no-op. It converts input column to strings and outputs to strings.
It causes some issues when we convert and port the tests at SPARK-27921. Integrated UDF test cases share one output file and it should outputs the same. However,
1. Special values are converted into strings differently:
| Scala | Python |
| ---------- | ------ |
| `null` | `None` |
| `Infinity` | `inf` |
| `-Infinity`| `-inf` |
| `NaN` | `nan` |
2. Due to float limitation at Python (see https://docs.python.org/3/tutorial/floatingpoint.html), if float is passed into Python and sent back to JVM, the values are potentially not exactly correct. See https://github.com/apache/spark/pull/25128 and https://github.com/apache/spark/pull/25110
To work around this, this PR targets to change the current UDF to be wrapped by cast. So, Input column is casted into string, UDF returns strings as are, and then output column is casted back to the input column.
Roughly:
**Before:**
```
JVM (col1) -> (cast to string within Python) Python (string) -> (string) JVM
```
**After:**
```
JVM (cast col1 to string) -> (string) Python (string) -> (cast back to col1's type) JVM
```
In this way, UDF is virtually no-op although there might be some subtleties due to roundtrip in string cast. I believe this is good enough.
Python native functions and Scala native functions will take strings and output strings as are. So, there will be no potential test failures due to differences of conversion between Python and Scala.
After this fix, for instance, `udf-aggregates_part1.sql` outputs exactly same as `aggregates_part1.sql`:
<details><summary>Diff comparing to 'pgSQL/aggregates_part1.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/pgSQL/aggregates_part1.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-aggregates_part1.sql.out
index 51ca1d55869..801735781c7 100644
--- a/sql/core/src/test/resources/sql-tests/results/pgSQL/aggregates_part1.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-aggregates_part1.sql.out
-3,7 +3,7
-- !query 0
-SELECT avg(four) AS avg_1 FROM onek
+SELECT avg(udf(four)) AS avg_1 FROM onek
-- !query 0 schema
struct<avg_1:double>
-- !query 0 output
-11,7 +11,7 struct<avg_1:double>
-- !query 1
-SELECT avg(a) AS avg_32 FROM aggtest WHERE a < 100
+SELECT udf(avg(a)) AS avg_32 FROM aggtest WHERE a < 100
-- !query 1 schema
struct<avg_32:double>
-- !query 1 output
-19,7 +19,7 struct<avg_32:double>
-- !query 2
-select CAST(avg(b) AS Decimal(10,3)) AS avg_107_943 FROM aggtest
+select CAST(avg(udf(b)) AS Decimal(10,3)) AS avg_107_943 FROM aggtest
-- !query 2 schema
struct<avg_107_943:decimal(10,3)>
-- !query 2 output
-27,7 +27,7 struct<avg_107_943:decimal(10,3)>
-- !query 3
-SELECT sum(four) AS sum_1500 FROM onek
+SELECT sum(udf(four)) AS sum_1500 FROM onek
-- !query 3 schema
struct<sum_1500:bigint>
-- !query 3 output
-35,7 +35,7 struct<sum_1500:bigint>
-- !query 4
-SELECT sum(a) AS sum_198 FROM aggtest
+SELECT udf(sum(a)) AS sum_198 FROM aggtest
-- !query 4 schema
struct<sum_198:bigint>
-- !query 4 output
-43,7 +43,7 struct<sum_198:bigint>
-- !query 5
-SELECT sum(b) AS avg_431_773 FROM aggtest
+SELECT udf(udf(sum(b))) AS avg_431_773 FROM aggtest
-- !query 5 schema
struct<avg_431_773:double>
-- !query 5 output
-51,7 +51,7 struct<avg_431_773:double>
-- !query 6
-SELECT max(four) AS max_3 FROM onek
+SELECT udf(max(four)) AS max_3 FROM onek
-- !query 6 schema
struct<max_3:int>
-- !query 6 output
-59,7 +59,7 struct<max_3:int>
-- !query 7
-SELECT max(a) AS max_100 FROM aggtest
+SELECT max(udf(a)) AS max_100 FROM aggtest
-- !query 7 schema
struct<max_100:int>
-- !query 7 output
-67,7 +67,7 struct<max_100:int>
-- !query 8
-SELECT max(aggtest.b) AS max_324_78 FROM aggtest
+SELECT udf(udf(max(aggtest.b))) AS max_324_78 FROM aggtest
-- !query 8 schema
struct<max_324_78:float>
-- !query 8 output
-75,237 +75,238 struct<max_324_78:float>
-- !query 9
-SELECT stddev_pop(b) FROM aggtest
+SELECT stddev_pop(udf(b)) FROM aggtest
-- !query 9 schema
-struct<stddev_pop(CAST(b AS DOUBLE)):double>
+struct<stddev_pop(CAST(CAST(udf(cast(b as string)) AS FLOAT) AS DOUBLE)):double>
-- !query 9 output
131.10703231895047
-- !query 10
-SELECT stddev_samp(b) FROM aggtest
+SELECT udf(stddev_samp(b)) FROM aggtest
-- !query 10 schema
-struct<stddev_samp(CAST(b AS DOUBLE)):double>
+struct<CAST(udf(cast(stddev_samp(cast(b as double)) as string)) AS DOUBLE):double>
-- !query 10 output
151.38936080399804
-- !query 11
-SELECT var_pop(b) FROM aggtest
+SELECT var_pop(udf(b)) FROM aggtest
-- !query 11 schema
-struct<var_pop(CAST(b AS DOUBLE)):double>
+struct<var_pop(CAST(CAST(udf(cast(b as string)) AS FLOAT) AS DOUBLE)):double>
-- !query 11 output
17189.053923482323
-- !query 12
-SELECT var_samp(b) FROM aggtest
+SELECT udf(var_samp(b)) FROM aggtest
-- !query 12 schema
-struct<var_samp(CAST(b AS DOUBLE)):double>
+struct<CAST(udf(cast(var_samp(cast(b as double)) as string)) AS DOUBLE):double>
-- !query 12 output
22918.738564643096
-- !query 13
-SELECT stddev_pop(CAST(b AS Decimal(38,0))) FROM aggtest
+SELECT udf(stddev_pop(CAST(b AS Decimal(38,0)))) FROM aggtest
-- !query 13 schema
-struct<stddev_pop(CAST(CAST(b AS DECIMAL(38,0)) AS DOUBLE)):double>
+struct<CAST(udf(cast(stddev_pop(cast(cast(b as decimal(38,0)) as double)) as string)) AS DOUBLE):double>
-- !query 13 output
131.18117242958306
-- !query 14
-SELECT stddev_samp(CAST(b AS Decimal(38,0))) FROM aggtest
+SELECT stddev_samp(CAST(udf(b) AS Decimal(38,0))) FROM aggtest
-- !query 14 schema
-struct<stddev_samp(CAST(CAST(b AS DECIMAL(38,0)) AS DOUBLE)):double>
+struct<stddev_samp(CAST(CAST(CAST(udf(cast(b as string)) AS FLOAT) AS DECIMAL(38,0)) AS DOUBLE)):double>
-- !query 14 output
151.47497042966097
-- !query 15
-SELECT var_pop(CAST(b AS Decimal(38,0))) FROM aggtest
+SELECT udf(var_pop(CAST(b AS Decimal(38,0)))) FROM aggtest
-- !query 15 schema
-struct<var_pop(CAST(CAST(b AS DECIMAL(38,0)) AS DOUBLE)):double>
+struct<CAST(udf(cast(var_pop(cast(cast(b as decimal(38,0)) as double)) as string)) AS DOUBLE):double>
-- !query 15 output
17208.5
-- !query 16
-SELECT var_samp(CAST(b AS Decimal(38,0))) FROM aggtest
+SELECT var_samp(udf(CAST(b AS Decimal(38,0)))) FROM aggtest
-- !query 16 schema
-struct<var_samp(CAST(CAST(b AS DECIMAL(38,0)) AS DOUBLE)):double>
+struct<var_samp(CAST(CAST(udf(cast(cast(b as decimal(38,0)) as string)) AS DECIMAL(38,0)) AS DOUBLE)):double>
-- !query 16 output
22944.666666666668
-- !query 17
-SELECT var_pop(1.0), var_samp(2.0)
+SELECT udf(var_pop(1.0)), var_samp(udf(2.0))
-- !query 17 schema
-struct<var_pop(CAST(1.0 AS DOUBLE)):double,var_samp(CAST(2.0 AS DOUBLE)):double>
+struct<CAST(udf(cast(var_pop(cast(1.0 as double)) as string)) AS DOUBLE):double,var_samp(CAST(CAST(udf(cast(2.0 as string)) AS DECIMAL(2,1)) AS DOUBLE)):double>
-- !query 17 output
0.0 NaN
-- !query 18
-SELECT stddev_pop(CAST(3.0 AS Decimal(38,0))), stddev_samp(CAST(4.0 AS Decimal(38,0)))
+SELECT stddev_pop(udf(CAST(3.0 AS Decimal(38,0)))), stddev_samp(CAST(udf(4.0) AS Decimal(38,0)))
-- !query 18 schema
-struct<stddev_pop(CAST(CAST(3.0 AS DECIMAL(38,0)) AS DOUBLE)):double,stddev_samp(CAST(CAST(4.0 AS DECIMAL(38,0)) AS DOUBLE)):double>
+struct<stddev_pop(CAST(CAST(udf(cast(cast(3.0 as decimal(38,0)) as string)) AS DECIMAL(38,0)) AS DOUBLE)):double,stddev_samp(CAST(CAST(CAST(udf(cast(4.0 as string)) AS DECIMAL(2,1)) AS DECIMAL(38,0)) AS DOUBLE)):double>
-- !query 18 output
0.0 NaN
-- !query 19
-select sum(CAST(null AS int)) from range(1,4)
+select sum(udf(CAST(null AS int))) from range(1,4)
-- !query 19 schema
-struct<sum(CAST(NULL AS INT)):bigint>
+struct<sum(CAST(udf(cast(cast(null as int) as string)) AS INT)):bigint>
-- !query 19 output
NULL
-- !query 20
-select sum(CAST(null AS long)) from range(1,4)
+select sum(udf(CAST(null AS long))) from range(1,4)
-- !query 20 schema
-struct<sum(CAST(NULL AS BIGINT)):bigint>
+struct<sum(CAST(udf(cast(cast(null as bigint) as string)) AS BIGINT)):bigint>
-- !query 20 output
NULL
-- !query 21
-select sum(CAST(null AS Decimal(38,0))) from range(1,4)
+select sum(udf(CAST(null AS Decimal(38,0)))) from range(1,4)
-- !query 21 schema
-struct<sum(CAST(NULL AS DECIMAL(38,0))):decimal(38,0)>
+struct<sum(CAST(udf(cast(cast(null as decimal(38,0)) as string)) AS DECIMAL(38,0))):decimal(38,0)>
-- !query 21 output
NULL
-- !query 22
-select sum(CAST(null AS DOUBLE)) from range(1,4)
+select sum(udf(CAST(null AS DOUBLE))) from range(1,4)
-- !query 22 schema
-struct<sum(CAST(NULL AS DOUBLE)):double>
+struct<sum(CAST(udf(cast(cast(null as double) as string)) AS DOUBLE)):double>
-- !query 22 output
NULL
-- !query 23
-select avg(CAST(null AS int)) from range(1,4)
+select avg(udf(CAST(null AS int))) from range(1,4)
-- !query 23 schema
-struct<avg(CAST(NULL AS INT)):double>
+struct<avg(CAST(udf(cast(cast(null as int) as string)) AS INT)):double>
-- !query 23 output
NULL
-- !query 24
-select avg(CAST(null AS long)) from range(1,4)
+select avg(udf(CAST(null AS long))) from range(1,4)
-- !query 24 schema
-struct<avg(CAST(NULL AS BIGINT)):double>
+struct<avg(CAST(udf(cast(cast(null as bigint) as string)) AS BIGINT)):double>
-- !query 24 output
NULL
-- !query 25
-select avg(CAST(null AS Decimal(38,0))) from range(1,4)
+select avg(udf(CAST(null AS Decimal(38,0)))) from range(1,4)
-- !query 25 schema
-struct<avg(CAST(NULL AS DECIMAL(38,0))):decimal(38,4)>
+struct<avg(CAST(udf(cast(cast(null as decimal(38,0)) as string)) AS DECIMAL(38,0))):decimal(38,4)>
-- !query 25 output
NULL
-- !query 26
-select avg(CAST(null AS DOUBLE)) from range(1,4)
+select avg(udf(CAST(null AS DOUBLE))) from range(1,4)
-- !query 26 schema
-struct<avg(CAST(NULL AS DOUBLE)):double>
+struct<avg(CAST(udf(cast(cast(null as double) as string)) AS DOUBLE)):double>
-- !query 26 output
NULL
-- !query 27
-select sum(CAST('NaN' AS DOUBLE)) from range(1,4)
+select sum(CAST(udf('NaN') AS DOUBLE)) from range(1,4)
-- !query 27 schema
-struct<sum(CAST(NaN AS DOUBLE)):double>
+struct<sum(CAST(CAST(udf(cast(NaN as string)) AS STRING) AS DOUBLE)):double>
-- !query 27 output
NaN
-- !query 28
-select avg(CAST('NaN' AS DOUBLE)) from range(1,4)
+select avg(CAST(udf('NaN') AS DOUBLE)) from range(1,4)
-- !query 28 schema
-struct<avg(CAST(NaN AS DOUBLE)):double>
+struct<avg(CAST(CAST(udf(cast(NaN as string)) AS STRING) AS DOUBLE)):double>
-- !query 28 output
NaN
-- !query 30
-SELECT avg(CAST(x AS DOUBLE)), var_pop(CAST(x AS DOUBLE))
+SELECT avg(CAST(udf(x) AS DOUBLE)), var_pop(CAST(udf(x) AS DOUBLE))
FROM (VALUES ('Infinity'), ('1')) v(x)
-- !query 30 schema
-struct<avg(CAST(x AS DOUBLE)):double,var_pop(CAST(x AS DOUBLE)):double>
+struct<avg(CAST(CAST(udf(cast(x as string)) AS STRING) AS DOUBLE)):double,var_pop(CAST(CAST(udf(cast(x as string)) AS STRING) AS DOUBLE)):double>
-- !query 30 output
Infinity NaN
-- !query 31
-SELECT avg(CAST(x AS DOUBLE)), var_pop(CAST(x AS DOUBLE))
+SELECT avg(CAST(udf(x) AS DOUBLE)), var_pop(CAST(udf(x) AS DOUBLE))
FROM (VALUES ('Infinity'), ('Infinity')) v(x)
-- !query 31 schema
-struct<avg(CAST(x AS DOUBLE)):double,var_pop(CAST(x AS DOUBLE)):double>
+struct<avg(CAST(CAST(udf(cast(x as string)) AS STRING) AS DOUBLE)):double,var_pop(CAST(CAST(udf(cast(x as string)) AS STRING) AS DOUBLE)):double>
-- !query 31 output
Infinity NaN
-- !query 32
-SELECT avg(CAST(x AS DOUBLE)), var_pop(CAST(x AS DOUBLE))
+SELECT avg(CAST(udf(x) AS DOUBLE)), var_pop(CAST(udf(x) AS DOUBLE))
FROM (VALUES ('-Infinity'), ('Infinity')) v(x)
-- !query 32 schema
-struct<avg(CAST(x AS DOUBLE)):double,var_pop(CAST(x AS DOUBLE)):double>
+struct<avg(CAST(CAST(udf(cast(x as string)) AS STRING) AS DOUBLE)):double,var_pop(CAST(CAST(udf(cast(x as string)) AS STRING) AS DOUBLE)):double>
-- !query 32 output
NaN NaN
-- !query 33
-SELECT avg(CAST(x AS DOUBLE)), var_pop(CAST(x AS DOUBLE))
+SELECT avg(udf(CAST(x AS DOUBLE))), udf(var_pop(CAST(x AS DOUBLE)))
FROM (VALUES (100000003), (100000004), (100000006), (100000007)) v(x)
-- !query 33 schema
-struct<avg(CAST(x AS DOUBLE)):double,var_pop(CAST(x AS DOUBLE)):double>
+struct<avg(CAST(udf(cast(cast(x as double) as string)) AS DOUBLE)):double,CAST(udf(cast(var_pop(cast(x as double)) as string)) AS DOUBLE):double>
-- !query 33 output
1.00000005E8 2.5
-- !query 34
-SELECT avg(CAST(x AS DOUBLE)), var_pop(CAST(x AS DOUBLE))
+SELECT avg(udf(CAST(x AS DOUBLE))), udf(var_pop(CAST(x AS DOUBLE)))
FROM (VALUES (7000000000005), (7000000000007)) v(x)
-- !query 34 schema
-struct<avg(CAST(x AS DOUBLE)):double,var_pop(CAST(x AS DOUBLE)):double>
+struct<avg(CAST(udf(cast(cast(x as double) as string)) AS DOUBLE)):double,CAST(udf(cast(var_pop(cast(x as double)) as string)) AS DOUBLE):double>
-- !query 34 output
7.000000000006E12 1.0
-- !query 35
-SELECT covar_pop(b, a), covar_samp(b, a) FROM aggtest
+SELECT udf(covar_pop(b, udf(a))), covar_samp(udf(b), a) FROM aggtest
-- !query 35 schema
-struct<covar_pop(CAST(b AS DOUBLE), CAST(a AS DOUBLE)):double,covar_samp(CAST(b AS DOUBLE), CAST(a AS DOUBLE)):double>
+struct<CAST(udf(cast(covar_pop(cast(b as double), cast(cast(udf(cast(a as string)) as int) as double)) as string)) AS DOUBLE):double,covar_samp(CAST(CAST(udf(cast(b as string)) AS FLOAT) AS DOUBLE), CAST(a AS DOUBLE)):double>
-- !query 35 output
653.6289553875104 871.5052738500139
-- !query 36
-SELECT corr(b, a) FROM aggtest
+SELECT corr(b, udf(a)) FROM aggtest
-- !query 36 schema
-struct<corr(CAST(b AS DOUBLE), CAST(a AS DOUBLE)):double>
+struct<corr(CAST(b AS DOUBLE), CAST(CAST(udf(cast(a as string)) AS INT) AS DOUBLE)):double>
-- !query 36 output
0.1396345165178734
-- !query 37
-SELECT count(four) AS cnt_1000 FROM onek
+SELECT count(udf(four)) AS cnt_1000 FROM onek
-- !query 37 schema
struct<cnt_1000:bigint>
-- !query 37 output
-313,7 +314,7 struct<cnt_1000:bigint>
-- !query 38
-SELECT count(DISTINCT four) AS cnt_4 FROM onek
+SELECT udf(count(DISTINCT four)) AS cnt_4 FROM onek
-- !query 38 schema
struct<cnt_4:bigint>
-- !query 38 output
-321,10 +322,10 struct<cnt_4:bigint>
-- !query 39
-select ten, count(*), sum(four) from onek
+select ten, udf(count(*)), sum(udf(four)) from onek
group by ten order by ten
-- !query 39 schema
-struct<ten:int,count(1):bigint,sum(four):bigint>
+struct<ten:int,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint,sum(CAST(udf(cast(four as string)) AS INT)):bigint>
-- !query 39 output
0 100 100
1 100 200
-339,10 +340,10 struct<ten:int,count(1):bigint,sum(four):bigint>
-- !query 40
-select ten, count(four), sum(DISTINCT four) from onek
+select ten, count(udf(four)), udf(sum(DISTINCT four)) from onek
group by ten order by ten
-- !query 40 schema
-struct<ten:int,count(four):bigint,sum(DISTINCT four):bigint>
+struct<ten:int,count(CAST(udf(cast(four as string)) AS INT)):bigint,CAST(udf(cast(sum(distinct cast(four as bigint)) as string)) AS BIGINT):bigint>
-- !query 40 output
0 100 2
1 100 4
-357,11 +358,11 struct<ten:int,count(four):bigint,sum(DISTINCT four):bigint>
-- !query 41
-select ten, sum(distinct four) from onek a
+select ten, udf(sum(distinct four)) from onek a
group by ten
-having exists (select 1 from onek b where sum(distinct a.four) = b.four)
+having exists (select 1 from onek b where udf(sum(distinct a.four)) = b.four)
-- !query 41 schema
-struct<ten:int,sum(DISTINCT four):bigint>
+struct<ten:int,CAST(udf(cast(sum(distinct cast(four as bigint)) as string)) AS BIGINT):bigint>
-- !query 41 output
0 2
2 2
-374,23 +375,23 struct<ten:int,sum(DISTINCT four):bigint>
select ten, sum(distinct four) from onek a
group by ten
having exists (select 1 from onek b
- where sum(distinct a.four + b.four) = b.four)
+ where sum(distinct a.four + b.four) = udf(b.four))
-- !query 42 schema
struct<>
-- !query 42 output
org.apache.spark.sql.AnalysisException
Aggregate/Window/Generate expressions are not valid in where clause of the query.
-Expression in where clause: [(sum(DISTINCT CAST((outer() + b.`four`) AS BIGINT)) = CAST(b.`four` AS BIGINT))]
+Expression in where clause: [(sum(DISTINCT CAST((outer() + b.`four`) AS BIGINT)) = CAST(CAST(udf(cast(four as string)) AS INT) AS BIGINT))]
Invalid expressions: [sum(DISTINCT CAST((outer() + b.`four`) AS BIGINT))];
-- !query 43
select
- (select max((select i.unique2 from tenk1 i where i.unique1 = o.unique1)))
+ (select udf(max((select i.unique2 from tenk1 i where i.unique1 = o.unique1))))
from tenk1 o
-- !query 43 schema
struct<>
-- !query 43 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`o.unique1`' given input columns: [i.even, i.fivethous, i.four, i.hundred, i.odd, i.string4, i.stringu1, i.stringu2, i.ten, i.tenthous, i.thousand, i.twenty, i.two, i.twothousand, i.unique1, i.unique2]; line 2 pos 63
+cannot resolve '`o.unique1`' given input columns: [i.even, i.fivethous, i.four, i.hundred, i.odd, i.string4, i.stringu1, i.stringu2, i.ten, i.tenthous, i.thousand, i.twenty, i.two, i.twothousand, i.unique1, i.unique2]; line 2 pos 67
```
</p>
</details>
## How was this patch tested?
Manually tested.
Closes#25130 from HyukjinKwon/SPARK-28359.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add 4 additional agg to KeyValueGroupedDataset
## How was this patch tested?
New test in DatasetSuite for typed aggregation
Closes#24993 from nooberfsh/sqlagg.
Authored-by: nooberfsh <nooberfsh@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR enables `spark.sql.function.preferIntegralDivision` for PostgreSQL testing.
## How was this patch tested?
N/A
Closes#25170 from wangyum/SPARK-28343-2.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
PR builder failed with the following error:
```
[error] /home/jenkins/workspace/SparkPullRequestBuilder/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala:714: wrong number of arguments for pattern org.apache.spark.sql.execution.exchange.ShuffleExchangeExec(outputPartitioning: org.apache.spark.sql.catalyst.plans.physical.Partitioning,child: org.apache.spark.sql.execution.SparkPlan,canChangeNumPartitions: Boolean)
[error] ShuffleExchangeExec(HashPartitioning(leftPartitioningExpressions, _), _), _),
[error] ^
[error] /home/jenkins/workspace/SparkPullRequestBuilder/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala:716: wrong number of arguments for pattern org.apache.spark.sql.execution.exchange.ShuffleExchangeExec(outputPartitioning: org.apache.spark.sql.catalyst.plans.physical.Partitioning,child: org.apache.spark.sql.execution.SparkPlan,canChangeNumPartitions: Boolean)
[error] ShuffleExchangeExec(HashPartitioning(rightPartitioningExpressions, _), _), _)) =>
[error] ^
```
## How was this patch tested?
Existing unit test.
Closes#25171 from gaborgsomogyi/SPARK-27485.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: herman <herman@databricks.com>
## What changes were proposed in this pull request?
Adaptive execution reduces the number of post-shuffle partitions at runtime, even for shuffles caused by repartition. However, the user likely wants to get the desired number of partition when he calls repartition even in adaptive execution. This PR adds an internal config to control this and by default adaptive execution will not change the number of post-shuffle partition for repartition.
## How was this patch tested?
New tests added.
Closes#25121 from carsonwang/AE_repartition.
Authored-by: Carson Wang <carson.wang@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When reordering joins EnsureRequirements only checks if all the join keys are present in the partitioning expression seq. This is problematic when the joins keys and and partitioning expressions both contain duplicates but not the same number of duplicates for each expression, e.g. `Seq(a, a, b)` vs `Seq(a, b, b)`. This fails with an index lookup failure in the `reorder` function.
This PR fixes this removing the equality checking logic from the `reorderJoinKeys` function, and by doing the multiset equality in the `reorder` function while building the reordered key sequences.
## How was this patch tested?
Added a unit test to the `PlannerSuite` and added an integration test to `JoinSuite`
Closes#25167 from hvanhovell/SPARK-27485.
Authored-by: herman <herman@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
A `Filter` predicate using `PythonUDF` can't be push down into join condition, currently. A predicate like that should be able to push down to join condition. For `PythonUDF`s that can't be evaluated in join condition, `PullOutPythonUDFInJoinCondition` will pull them out later.
An example like:
```scala
val pythonTestUDF = TestPythonUDF(name = "udf")
val left = Seq((1, 2), (2, 3)).toDF("a", "b")
val right = Seq((1, 2), (3, 4)).toDF("c", "d")
val df = left.crossJoin(right).where(pythonTestUDF($"a") === pythonTestUDF($"c"))
```
Query plan before the PR:
```
== Physical Plan ==
*(3) Project [a#2121, b#2122, c#2132, d#2133]
+- *(3) Filter (pythonUDF0#2142 = pythonUDF1#2143)
+- BatchEvalPython [udf(a#2121), udf(c#2132)], [pythonUDF0#2142, pythonUDF1#2143]
+- BroadcastNestedLoopJoin BuildRight, Cross
:- *(1) Project [_1#2116 AS a#2121, _2#2117 AS b#2122]
: +- LocalTableScan [_1#2116, _2#2117]
+- BroadcastExchange IdentityBroadcastMode
+- *(2) Project [_1#2127 AS c#2132, _2#2128 AS d#2133]
+- LocalTableScan [_1#2127, _2#2128]
```
Query plan after the PR:
```
== Physical Plan ==
*(3) Project [a#2121, b#2122, c#2132, d#2133]
+- *(3) BroadcastHashJoin [pythonUDF0#2142], [pythonUDF0#2143], Cross, BuildRight
:- BatchEvalPython [udf(a#2121)], [pythonUDF0#2142]
: +- *(1) Project [_1#2116 AS a#2121, _2#2117 AS b#2122]
: +- LocalTableScan [_1#2116, _2#2117]
+- BroadcastExchange HashedRelationBroadcastMode(List(input[2, string, true]))
+- BatchEvalPython [udf(c#2132)], [pythonUDF0#2143]
+- *(2) Project [_1#2127 AS c#2132, _2#2128 AS d#2133]
+- LocalTableScan [_1#2127, _2#2128]
```
After this PR, the join can use `BroadcastHashJoin`, instead of `BroadcastNestedLoopJoin`.
## How was this patch tested?
Added tests.
Closes#25106 from viirya/pythonudf-join-condition.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR aims to correct mappings in `MsSqlServerDialect`. `ShortType` is mapped to `SMALLINT` and `FloatType` is mapped to `REAL` per [JBDC mapping]( https://docs.microsoft.com/en-us/sql/connect/jdbc/using-basic-data-types?view=sql-server-2017) respectively.
ShortType and FloatTypes are not correctly mapped to right JDBC types when using JDBC connector. This results in tables and spark data frame being created with unintended types. The issue was observed when validating against SQLServer.
Refer [JBDC mapping]( https://docs.microsoft.com/en-us/sql/connect/jdbc/using-basic-data-types?view=sql-server-2017 ) for guidance on mappings between SQLServer, JDBC and Java. Note that java "Short" type should be mapped to JDBC "SMALLINT" and java Float should be mapped to JDBC "REAL".
Some example issue that can happen because of wrong mappings
- Write from df with column type results in a SQL table of with column type as INTEGER as opposed to SMALLINT.Thus a larger table that expected.
- Read results in a dataframe with type INTEGER as opposed to ShortType
- ShortType has a problem in both the the write and read path
- FloatTypes only have an issue with read path. In the write path Spark data type 'FloatType' is correctly mapped to JDBC equivalent data type 'Real'. But in the read path when JDBC data types need to be converted to Catalyst data types ( getCatalystType) 'Real' gets incorrectly gets mapped to 'DoubleType' rather than 'FloatType'.
Refer #28151 which contained this fix as one part of a larger PR. Following PR #28151 discussion it was decided to file seperate PRs for each of the fixes.
## How was this patch tested?
UnitTest added in JDBCSuite.scala and these were tested.
Integration test updated and passed in MsSqlServerDialect.scala
E2E test done with SQLServer
Closes#25146 from shivsood/float_short_type_fix.
Authored-by: shivsood <shivsood@microsoft.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
`System.currentTimeMillis` read two times in a loop in `RateStreamContinuousPartitionReader`. If the test machine is slow enough and it spends quite some time between the `while` condition check and the `Thread.sleep` then the timeout value is negative and throws `IllegalArgumentException`.
In this PR I've fixed this issue.
## How was this patch tested?
Existing unit tests.
Closes#25162 from gaborgsomogyi/SPARK-28404.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In the PR, I propose to use the `plusMonths()` method of `LocalDate` to add months to a date. This method adds the specified amount to the months field of `LocalDate` in three steps:
1. Add the input months to the month-of-year field
2. Check if the resulting date would be invalid
3. Adjust the day-of-month to the last valid day if necessary
The difference between current behavior and propose one is in handling the last day of month in the original date. For example, adding 1 month to `2019-02-28` will produce `2019-03-28` comparing to the current implementation where the result is `2019-03-31`.
The proposed behavior is implemented in MySQL and PostgreSQL.
## How was this patch tested?
By existing test suites `DateExpressionsSuite`, `DateFunctionsSuite` and `DateTimeUtilsSuite`.
Closes#25153 from MaxGekk/add-months.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR adds some traits so that we can deduplicate initialization stuff for each type of test case. For instance, see [SPARK-28343](https://issues.apache.org/jira/browse/SPARK-28343).
It's a little bit overkill but I think it will make adding test cases easier and cause less confusions.
This PR adds both:
```
private trait PgSQLTest
private trait UDFTest
```
To indicate and share the logics related to each combination of test types.
## How was this patch tested?
Manually tested.
Closes#25155 from HyukjinKwon/SPARK-28392.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This upgraded to a newer version of Pyrolite. Most updates [1] in the newer version are for dotnot. For java, it includes a bug fix to Unpickler regarding cleaning up Unpickler memo, and support of protocol 5.
After upgrading, we can remove the fix at SPARK-27629 for the bug in Unpickler.
[1] https://github.com/irmen/Pyrolite/compare/pyrolite-4.23...master
## How was this patch tested?
Manually tested on Python 3.6 in local on existing tests.
Closes#25143 from viirya/upgrade-pyrolite.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This patch proposes moving all Trigger implementations to `Triggers.scala`, to avoid exposing these implementations to the end users and let end users only deal with `Trigger.xxx` static methods. This fits the intention of deprecation of `ProcessingTIme`, and we agree to move others without deprecation as this patch will be shipped in major version (Spark 3.0.0).
## How was this patch tested?
UTs modified to work with newly introduced class.
Closes#24996 from HeartSaVioR/SPARK-28199.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This pr enables `spark.sql.crossJoin.enabled` and `spark.sql.parser.ansi.enabled` for PostgreSQL test.
## How was this patch tested?
manual tests:
Run `test.sql` in [pgSQL](https://github.com/apache/spark/tree/master/sql/core/src/test/resources/sql-tests/inputs/pgSQL) directory and in [inputs](https://github.com/apache/spark/tree/master/sql/core/src/test/resources/sql-tests/inputs) directory:
```sql
cat <<EOF > test.sql
create or replace temporary view t1 as
select * from (values(1), (2)) as v (val);
create or replace temporary view t2 as
select * from (values(2), (1)) as v (val);
select t1.*, t2.* from t1 join t2;
EOF
```
Closes#25109 from wangyum/SPARK-28343.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Terry Kim
Liang-Chi Hsieh
jiake
rongma1997
Kent Yao
Jungtaek Lim (HeartSaVioR)
Marcelo Vanzin
wenxuanguan
fuwhu
stczwd
Wenchen Fan
angerszhu
Yuanjian Li
Liang-Chi Hsieh
07ARB
Dongjoon Hyun
Burak Yavuz
DylanGuedes
Huaxin Gao
wuyi
Maxim Gekk
Yuming Wang
Dilip Biswal
Jiajia Li
Jeff Evans
Gengliang Wang
yangjie01
Peter Toth
Sean Owen
herman
Guilherme
gwang3
maryannxue
Nik Vanderhoof
s71955
HyukjinKwon
Henry D
Chris Martin
TomokoKomiyama
Jungtaek Lim (HeartSaVioR)
Wang Shuo
uncleGen
Rahij Ramsharan
Burak Yavuz
Ryan Blue
Xiao Li
windpiger
xy_xin
Takeshi Yamamuro
Holden Karau
bartosz25
Owen O'Malley
sandeep katta
turbofei
WeichenXu
LantaoJin
dengziming
Marco Gaido
gengjiaan
Mukul Murthy
Xianjin YE
younggyu chun
Matt Hawes
hemanth meka
Dilip Biswal
Ali Afroozeh
shivusondur
triplesheep
Robert (Bobby) Evans
lihao
Mick Jermsurawong
Eyal Zituny
John Zhuge
Edgar Rodriguez
Stavros Kontopoulos
Shixiong Zhu
Yishuang Lu
mcheah
Udbhav30
Kousuke Saruta
Wing Yew Poon
Lee Dongjin
shahid
Yiheng Wang
huangtianhua
Vinod KC
chitralverma
Seth Fitzsimmons
nooberfsh
Gabor Somogyi
Carson Wang
shivsood
