## 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?
drop the table after the test `query builtin functions don't call the external catalog` executed
This is required for [SPARK-25464](https://github.com/apache/spark/pull/22466)
## How was this patch tested?
existing UT
Closes#25427 from sandeep-katta/cleanuptable.
Authored-by: sandeep katta <sandeep.katta2007@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### 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?
Disallow conversions between `timestamp` type and `long` type in table insertion with ANSI store assignment policy.
### Why are the changes needed?
In the PR https://github.com/apache/spark/pull/25581, timestamp type is allowed to be converted to long type, since timestamp type is represented by long type internally, and both legacy mode and strict mode allows the conversion.
After reconsideration, I think we should disallow it. As per ANSI SQL section "4.4.2 Characteristics of numbers":
> A number is assignable only to sites of numeric type.
In PostgreSQL, the conversion between timestamp and long is also disallowed.
### Does this PR introduce any user-facing change?
Conversion between timestamp and long is disallowed in table insertion with ANSI store assignment policy.
### How was this patch tested?
Unit test
Closes#25615 from gengliangwang/disallowTimeStampToLong.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### 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?
Hive 3.1.2 has been released. This PR upgrades the Hive Metastore Client to 3.1.2 for Hive 3.1.
Hive 3.1.2 release notes:
https://issues.apache.org/jira/secure/ReleaseNote.jspa?version=12344397&styleName=Html&projectId=12310843
### Why are the changes needed?
This is an improvement to support a newly release 3.1.2. Otherwise, it will throws `UnsupportedOperationException` if user `set spark.sql.hive.metastore.version=3.1.2`:
```scala
Exception in thread "main" java.lang.UnsupportedOperationException: Unsupported Hive Metastore version (3.1.2). Please set spark.sql.hive.metastore.version with a valid version.
at org.apache.spark.sql.hive.client.IsolatedClientLoader$.hiveVersion(IsolatedClientLoader.scala:109)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT
Closes#25604 from wangyum/SPARK-28890.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Revise the documentation of SQL option `spark.sql.storeAssignmentPolicy`.
### Why are the changes needed?
1. Need to point out the ANSI mode is mostly the same with PostgreSQL
2. Need to point out Legacy mode allows type coercion as long as it is valid casting
3. Better examples.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Uni test
Closes#25605 from gengliangwang/reviseDoc.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
**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?
This PR makes `spark.sql.statistics.fallBackToHdfs` not support Hive partitioned tables.
### Why are the changes needed?
The current implementation is incorrect for external partitions and it is expensive to support partitioned table with external partitions.
### Does this PR introduce any user-facing change?
Yes. But I think it will not change the join strategy because partitioned table usually very large.
### How was this patch tested?
unit test
Closes#25584 from wangyum/SPARK-28876.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR add test for set the partitioned bucketed data source table SerDe correctly.
### Why are the changes needed?
Improve test.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#25591 from wangyum/SPARK-27592-f1.
Authored-by: Yuming Wang <yumwang@ebay.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 ignores Thrift server `ThriftServerQueryTestSuite`.
### Why are the changes needed?
This ThriftServerQueryTestSuite test case led to frequent Jenkins build failure.
### Does this PR introduce any user-facing change?
Yes.
### How was this patch tested?
N/A
Closes#25592 from wangyum/SPARK-28527-f1.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### 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?
This PR implements `SparkGetCatalogsOperation` for Thrift Server metadata completeness.
### Why are the changes needed?
Thrift Server metadata completeness.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Unit test
Closes#25555 from wangyum/SPARK-28852.
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?
To follow ANSI SQL, we should support a configurable mode that throws exceptions when casting to integers causes overflow.
The behavior is similar to https://issues.apache.org/jira/browse/SPARK-26218, which throws exceptions on arithmetical operation overflow.
To unify it, the configuration is renamed from "spark.sql.arithmeticOperations.failOnOverFlow" to "spark.sql.failOnIntegerOverFlow"
## How was this patch tested?
Unit test
Closes#25461 from gengliangwang/AnsiCastIntegral.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.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?
This PR aims to annotate `HiveExternalCatalogVersionsSuite` with `ExtendedHiveTest`.
### Why are the changes needed?
`HiveExternalCatalogVersionsSuite` is an outstanding test in terms of testing time. This PR aims to allow skipping this test suite when we use `ExtendedHiveTest`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Since Jenkins doesn't exclude `ExtendedHiveTest`, there is no difference in Jenkins testing.
This PR should be tested by manually by the following.
**BEFORE**
```
$ cd sql/hive
$ mvn package -Dtest=none -DwildcardSuites=org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite -Dtest.exclude.tags=org.apache.spark.tags.ExtendedHiveTest
...
Run starting. Expected test count is: 1
HiveExternalCatalogVersionsSuite:
22:32:16.218 WARN org.apache.hadoop.util.NativeCodeLoader: Unable to load ...
```
**AFTER**
```
$ cd sql/hive
$ mvn package -Dtest=none -DwildcardSuites=org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite -Dtest.exclude.tags=org.apache.spark.tags.ExtendedHiveTest
...
Run starting. Expected test count is: 0
HiveExternalCatalogVersionsSuite:
Run completed in 772 milliseconds.
Total number of tests run: 0
Suites: completed 2, aborted 0
Tests: succeeded 0, failed 0, canceled 0, ignored 0, pending 0
No tests were executed.
...
```
Closes#25550 from dongjoon-hyun/SPARK-28847.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.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?
<!--
If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible.
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If tests were not added, please describe why they were not added and/or why it was difficult to add.
-->
new test
Closes#25536 from cloud-fan/ctas.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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### What changes were proposed in this pull request?
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The current namespace/catalog should be set to None at the beginning, so that we can read the new configs when reporting currennt namespace/catalog later.
### 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.
-->
Fix a bug in CatalogManager, to reflect the change of default catalog config when reporting current catalog.
### 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'.
-->
No. The current namespace/catalog stuff is still internal right now.
### How was this patch tested?
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a new test suite
Closes#25521 from cloud-fan/fix.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Burak Yavuz <brkyvz@gmail.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?
Introduces the collectInPlanAndSubqueries and subqueriesAll methods in QueryPlan that consider all the plans in the query plan, including the ones in nested subqueries.
## How was this patch tested?
Unit test added
Closes#25433 from dbaliafroozeh/IntroduceCollectInPlanAndSubqueries.
Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com>
Signed-off-by: herman <herman@databricks.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>
