### What changes were proposed in this pull request?
Group all messages of `AnalysisExcpetions` created and thrown directly in org.apache.spark.sql.catalyst.analysis.Analyzer in one file.
* Create a new object: `org.apache.spark.sql.CatalystErrors` with many exception-creating functions.
* When the `Analyzer` wants to create and throw a new `AnalysisException`, call functions of `CatalystErrors`
### Why are the changes needed?
This is the sample PR that groups exception messages together in several files. It will largely help with standardization of error messages and its maintenance.
### Does this PR introduce _any_ user-facing change?
No. Error messages remain unchanged.
### How was this patch tested?
No new tests - pass all original tests to make sure it doesn't break any existing behavior.
### Naming of exception functions
All function names ended with `Error`.
* For specific errors like `groupingIDMismatch` and `groupingColInvalid`, directly use them as name, just like `groupingIDMismatchError` and `groupingColInvalidError`.
* For generic errors like `dataTypeMismatch`,
* if confident with the context, prefix and condition can be added, like `pivotValDataTypeMismatchError`
* if not sure about the context, add a `For` suffix of the specific component that this exception is related to, like `dataTypeMismatchForDeserializerError`
Closes#29497 from anchovYu/32670.
Lead-authored-by: anchovYu <aureole@sjtu.edu.cn>
Co-authored-by: anchovYu <xyyu15@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
With `ParseUrl`, instead of return null we throw exception if input string is not a vaild url.
### Why are the changes needed?
For ANSI mode.
### Does this PR introduce _any_ user-facing change?
Yes, user will get exception if `set spark.sql.ansi.enabled=true`.
### How was this patch tested?
Add test.
Closes#30399 from ulysses-you/SPARK-33468.
Lead-authored-by: ulysses <youxiduo@weidian.com>
Co-authored-by: ulysses-you <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Explicitly convert `scala.collection.mutable.Buffer` to `Seq`. In Scala 2.13 `Seq` is an alias of `scala.collection.immutable.Seq` instead of `scala.collection.Seq`.
### Why are the changes needed?
Without the change build with Scala 2.13 fails with the following:
```
[error] /home/runner/work/spark/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala:1417:41: type mismatch;
[error] found : scala.collection.mutable.Buffer[org.apache.spark.unsafe.types.UTF8String]
[error] required: Seq[org.apache.spark.unsafe.types.UTF8String]
[error] case null => LikeAll(e, patterns)
[error] ^
[error] /home/runner/work/spark/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala:1418:41: type mismatch;
[error] found : scala.collection.mutable.Buffer[org.apache.spark.unsafe.types.UTF8String]
[error] required: Seq[org.apache.spark.unsafe.types.UTF8String]
[error] case _ => NotLikeAll(e, patterns)
[error] ^
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#30431 from sunchao/SPARK-33045-followup.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Spark already support `LIKE ALL` syntax, but it will throw `StackOverflowError` if there are many elements(more than 14378 elements). We should implement built-in function for LIKE ALL to fix this issue.
Why the stack overflow can happen in the current approach ?
The current approach uses reduceLeft to connect each `Like(e, p)`, this will lead the the call depth of the thread is too large, causing `StackOverflowError` problems.
Why the fix in this PR can avoid the error?
This PR support built-in function for `LIKE ALL` and avoid this issue.
### Why are the changes needed?
1.Fix the `StackOverflowError` issue.
2.Support built-in function `like_all`.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#29999 from beliefer/SPARK-33045-like_all.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change `CombineLimits` name to `EliminateLimits` and add check if `Limit` child max row <= limit.
### Why are the changes needed?
In Add-hoc scene, we always add limit for the query if user have no special limit value, but not all limit is nesessary.
A general negative example is
```
select count(*) from t limit 100000;
```
It will be great if we can eliminate limit at Spark side.
Also, we make a benchmark for this case
```
runBenchmark("Sort and Limit") {
val N = 100000
val benchmark = new Benchmark("benchmark sort and limit", N)
benchmark.addCase("TakeOrderedAndProject", 3) { _ =>
spark.range(N).toDF("c").repartition(200).sort("c").take(200000)
}
benchmark.addCase("Sort And Limit", 3) { _ =>
withSQLConf("spark.sql.execution.topKSortFallbackThreshold" -> "-1") {
spark.range(N).toDF("c").repartition(200).sort("c").take(200000)
}
}
benchmark.addCase("Sort", 3) { _ =>
spark.range(N).toDF("c").repartition(200).sort("c").collect()
}
benchmark.run()
}
```
and the result is
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_191-b12 on Mac OS X 10.15.6
Intel(R) Core(TM) i5-5257U CPU 2.70GHz
benchmark sort and limit: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
TakeOrderedAndProject 1833 2259 382 0.1 18327.1 1.0X
Sort And Limit 1417 1658 285 0.1 14167.5 1.3X
Sort 1324 1484 225 0.1 13238.3 1.4X
```
It shows that it makes sense to replace `TakeOrderedAndProjectExec` with `Sort + Project`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#30368 from ulysses-you/SPARK-33442.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR is a follow up for #30093 to updates the config `spark.sql.execution.removeRedundantSorts` version to 2.4.8.
### Why are the changes needed?
To update the rule version it has been backported to 2.4. #30194
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#30420 from allisonwang-db/spark-33183-follow-up.
Authored-by: allisonwang-db <66282705+allisonwang-db@users.noreply.github.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This pr add a new Scala compile arg to `pom.xml` to defense against new unused imports:
- `-Ywarn-unused-import` for Scala 2.12
- `-Wconf:cat=unused-imports:e` for Scala 2.13
The other fIles change are remove all unused imports in Spark code
### Why are the changes needed?
Cleanup code and add guarantee to defense against new unused imports
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#30351 from LuciferYang/remove-imports-core-module.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add missing license headers for new files added in #28027.
### Why are the changes needed?
To fix licenses.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This is a purely non-functional change.
Closes#30415 from rdblue/license-headers.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Similar to `InterpretedUnsafeProjection`, this patch proposes to extend interpreted subexpression elimination to `InterpretedMutableProjection` and `InterpretedSafeProjection`.
### Why are the changes needed?
Enabling subexpression elimination can improve the performance of interpreted projections, as shown in `InterpretedUnsafeProjection`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#30406 from viirya/SPARK-33473.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This followup is to prevent possible test flakyness of `SubExprEvaluationRuntimeSuite`.
### Why are the changes needed?
Because HashMap doesn't guarantee the order, in `proxyExpressions` the proxy expression id is not deterministic. So in `SubExprEvaluationRuntimeSuite` we should not test against it.
### Does this PR introduce _any_ user-facing change?
No, dev only.
### How was this patch tested?
Unit test.
Closes#30414 from viirya/SPARK-33427-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
In section 6.13 of the ANSI SQL standard, there are syntax rules for valid combinations of the source and target data types.
Comparing the ANSI CAST syntax rules with the current default behavior of Spark:
To make Spark's ANSI mode more ANSI SQL Compatible，I propose to disallow the following casting in ANSI mode:
```
TimeStamp <=> Boolean
Date <=> Boolean
Numeric <=> Timestamp
Numeric <=> Date
Numeric <=> Binary
String <=> Array
String <=> Map
String <=> Struct
```
The following castings are considered invalid in ANSI SQL standard, but they are quite straight forward. Let's Allow them for now
```
Numeric <=> Boolean
String <=> Binary
```
### Why are the changes needed?
Better ANSI SQL compliance
### Does this PR introduce _any_ user-facing change?
Yes, the following castings will not be allowed in ANSI mode:
```
TimeStamp <=> Boolean
Date <=> Boolean
Numeric <=> Timestamp
Numeric <=> Date
Numeric <=> Binary
String <=> Array
String <=> Map
String <=> Struct
```
### How was this patch tested?
Unit test
The ANSI Compliance doc preview:
Closes#30260 from gengliangwang/ansiCanCast.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This adds support for metadata columns to DataSourceV2. If a source implements `SupportsMetadataColumns` it must also implement `SupportsPushDownRequiredColumns` to support projecting those columns.
The analyzer is updated to resolve metadata columns from `LogicalPlan.metadataOutput`, and this adds a rule that will add metadata columns to the output of `DataSourceV2Relation` if one is used.
### Why are the changes needed?
This is the solution discussed for exposing additional data in the Kafka source. It is also needed for a generic `MERGE INTO` plan.
### Does this PR introduce any user-facing change?
Yes. Users can project additional columns from sources that implement the new API. This also updates `DescribeTableExec` to show metadata columns.
### How was this patch tested?
Will include new unit tests.
Closes#28027 from rdblue/add-dsv2-metadata-columns.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
### What changes were proposed in this pull request?
This change adds MapType support for PySpark with Arrow, if using pyarrow >= 2.0.0.
### Why are the changes needed?
MapType was previous unsupported with Arrow.
### Does this PR introduce _any_ user-facing change?
User can now enable MapType for `createDataFrame()`, `toPandas()` with Arrow optimization, and with Pandas UDFs.
### How was this patch tested?
Added new PySpark tests for createDataFrame(), toPandas() and Scalar Pandas UDFs.
Closes#30393 from BryanCutler/arrow-add-MapType-SPARK-24554.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch proposes to add subexpression elimination for interpreted expression evaluation. Interpreted expression evaluation is used when codegen was not able to work, for example complex schema.
### Why are the changes needed?
Currently we only do subexpression elimination for codegen. For some reasons, we may need to run interpreted expression evaluation. For example, codegen fails to compile and fallbacks to interpreted mode, or complex input/output schema of expressions. It is commonly seen for complex schema from expressions that is possibly caused by the query optimizer too, e.g. SPARK-32945.
We should also support subexpression elimination for interpreted evaluation. That could reduce performance difference when Spark fallbacks from codegen to interpreted expression evaluation, and improve Spark usability.
#### Benchmark
Update `SubExprEliminationBenchmark`:
Before:
```
OpenJDK 64-Bit Server VM 1.8.0_265-b01 on Mac OS X 10.15.6
Intel(R) Core(TM) i7-9750H CPU 2.60GHz
from_json as subExpr: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------------------------------------
subexpressionElimination on, codegen off 24707 25688 903 0.0 247068775.9 1.0X
```
After:
```
OpenJDK 64-Bit Server VM 1.8.0_265-b01 on Mac OS X 10.15.6
Intel(R) Core(TM) i7-9750H CPU 2.60GHz
from_json as subExpr: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------------------------------------
subexpressionElimination on, codegen off 2360 2435 87 0.0 23604320.7 11.2X
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test. Benchmark manually.
Closes#30341 from viirya/SPARK-33427.
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 [rewrite](5197c5d2e7/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala (L722-L724)) `In`/`InSet` predicate to `or` expressions when pruning Hive partitions. That will cause Hive metastore stack over flow if there are a lot of values.
This pr rewrite `InSet` predicate to `GreaterThanOrEqual` min value and `LessThanOrEqual ` max value when pruning Hive partitions to avoid Hive metastore stack overflow.
From our experience, `spark.sql.hive.metastorePartitionPruningInSetThreshold` should be less than 10000.
### Why are the changes needed?
Avoid Hive metastore stack overflow when `InSet` predicate have many values.
Especially DPP, it may generate many values.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual test.
Closes#30325 from wangyum/SPARK-33416.
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 proposes to simplify the exception messages from Python UDFS.
Currently, the exception message from Python UDFs is as below:
```python
from pyspark.sql.functions import udf; spark.range(10).select(udf(lambda x: x/0)("id")).collect()
```
```python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../python/pyspark/sql/dataframe.py", line 427, in show
print(self._jdf.showString(n, 20, vertical))
File "/.../python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
File "/.../python/pyspark/sql/utils.py", line 127, in deco
raise_from(converted)
File "<string>", line 3, in raise_from
pyspark.sql.utils.PythonException:
An exception was thrown from Python worker in the executor:
Traceback (most recent call last):
File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
process()
File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
serializer.dump_stream(out_iter, outfile)
File "/.../python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
self.serializer.dump_stream(self._batched(iterator), stream)
File "/.../python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
for obj in iterator:
File "/.../python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
for item in iterator:
File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
return lambda *a: f(*a)
File "/.../python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
return f(*args, **kwargs)
File "<stdin>", line 1, in <lambda>
ZeroDivisionError: division by zero
```
Actually, almost all cases, users only care about `ZeroDivisionError: division by zero`. We don't really have to show the internal stuff in 99% cases.
This PR adds a configuration `spark.sql.execution.pyspark.udf.simplifiedException.enabled` (disabled by default) that hides the internal tracebacks related to Python worker, (de)serialization, etc.
```python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../python/pyspark/sql/dataframe.py", line 427, in show
print(self._jdf.showString(n, 20, vertical))
File "/.../python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
File "/.../python/pyspark/sql/utils.py", line 127, in deco
raise_from(converted)
File "<string>", line 3, in raise_from
pyspark.sql.utils.PythonException:
An exception was thrown from Python worker in the executor:
Traceback (most recent call last):
File "<stdin>", line 1, in <lambda>
ZeroDivisionError: division by zero
```
The trackback will be shown from the point when any non-PySpark file is seen in the traceback.
### Why are the changes needed?
Without this configuration. such internal tracebacks are exposed to users directly especially for shall or notebook users in PySpark. 99% cases people don't care about the internal Python worker, (de)serialization and related tracebacks. It just makes the exception more difficult to read. For example, one statement of `x/0` above shows a very long traceback and most of them are unnecessary.
This configuration enables the ability to show simplified tracebacks which users will likely be most interested in.
### Does this PR introduce _any_ user-facing change?
By default, no. It adds one configuration that simplifies the exception message. See the example above.
### How was this patch tested?
Manually tested:
```bash
$ pyspark --conf spark.sql.execution.pyspark.udf.simplifiedException.enabled=true
```
```python
from pyspark.sql.functions import udf; spark.sparkContext.setLogLevel("FATAL"); spark.range(10).select(udf(lambda x: x/0)("id")).collect()
```
and unittests were also added.
Closes#30309 from HyukjinKwon/SPARK-33407.
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 from https://github.com/apache/spark/pull/30076 to refactor unit test of stream-stream join in `UnsupportedOperationsSuite`, where we had a lot of duplicated code for stream-stream join unit test, for each join type.
### Why are the changes needed?
Help reduce duplicated code and make it easier for developers to read and add code in the future.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit test in `UnsupportedOperationsSuite.scala` (pure refactoring).
Closes#30347 from c21/stream-test.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Instead of returning NULL, throws runtime NoSuchElementException towards invalid key accessing in map-like functions, such as element_at, GetMapValue, when ANSI mode is on.
### Why are the changes needed?
For ANSI mode.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT and Existing UT.
Closes#30386 from leanken/leanken-SPARK-33460.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Move `SHOW PARTITIONS` parsing tests to `ShowPartitionsParserSuite`
2. Place Hive tests for `SHOW PARTITIONS` from `HiveCommandSuite` to the base test suite `v1.ShowPartitionsSuiteBase`. This will allow to run the tests w/ and w/o Hive.
The changes follow the approach of https://github.com/apache/spark/pull/30287.
### Why are the changes needed?
- The unification will allow to run common `SHOW PARTITIONS` tests for both DSv1 and Hive DSv1, DSv2
- We can detect missing features and differences between DSv1 and DSv2 implementations.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running:
- new test suites `build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *ShowPartitionsSuite"`
- and old one `build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly org.apache.spark.sql.hive.execution.HiveCommandSuite"`
Closes#30377 from MaxGekk/unify-dsv1_v2-show-partitions-tests.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR makes internal classes of SparkSession always using active SQLConf. We should remove all `conf: SQLConf`s from ctor-parameters of this classes (`Analyzer`, `SparkPlanner`, `SessionCatalog`, `CatalogManager` `SparkSqlParser` and etc.) and use `SQLConf.get` instead.
### Why are the changes needed?
Code refine.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing test
Closes#30299 from luluorta/SPARK-33389.
Authored-by: luluorta <luluorta@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Replace `spark.sql.sources.write.useV1SourceList` by `spark.sql.sources.useV1SourceList` in the comment for `CatalogManager.v2SessionCatalog()`.
### Why are the changes needed?
To have correct comments.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running `./dev/scalastyle`.
Closes#30385 from MaxGekk/fix-comment-useV1SourceList.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Throw `NoSuchNamespaceException` in `listTables()` of the custom test catalog `InMemoryTableCatalog` if the passed namespace doesn't exist.
### Why are the changes needed?
1. To align behavior of V2 `InMemoryTableCatalog` to V1 session catalog.
2. To distinguish two situations:
1. A namespace **does exist** but does not contain any tables. In that case, `listTables()` returns empty result.
2. A namespace **does not exist**. `listTables()` throws `NoSuchNamespaceException` in this case.
### Does this PR introduce _any_ user-facing change?
Yes. For example, `SHOW TABLES` returns empty result before the changes.
### How was this patch tested?
By running V1/V2 ShowTablesSuites.
Closes#30358 from MaxGekk/show-tables-in-not-existing-namespace.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR makes SQL parser using active SQLConf instead of the one in ctor-parameters.
### Why are the changes needed?
In ANSI mode, schema string parsing should fail if the schema uses ANSI reserved keyword as attribute name:
```scala
spark.conf.set("spark.sql.ansi.enabled", "true")
spark.sql("""select from_json('{"time":"26/10/2015"}', 'time Timestamp', map('timestampFormat', 'dd/MM/yyyy'));""").show
```
output:
> Cannot parse the data type:
> no viable alternative at input 'time'(line 1, pos 0)
>
> == SQL ==
> time Timestamp
> ^^^
But this query may accidentally succeed in certain cases cause the DataType parser sticks to the configs of the first created session in the current thread:
```scala
DataType.fromDDL("time Timestamp")
val newSpark = spark.newSession()
newSpark.conf.set("spark.sql.ansi.enabled", "true")
newSpark.sql("""select from_json('{"time":"26/10/2015"}', 'time Timestamp', map('timestampFormat', 'dd/MM/yyyy'));""").show
```
output:
> +--------------------------------+
> |from_json({"time":"26/10/2015"})|
> +--------------------------------+
> | {2015-10-26 00:00...|
> +--------------------------------+
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Newly and updated UTs
Closes#30357 from luluorta/SPARK-33432.
Authored-by: luluorta <luluorta@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This is a simple followup to prevent test flakyness in SubexpressionEliminationSuite. If `getAllEquivalentExprs` returns more than 1 sequences, due to HashMap, we should use `contains` instead of assuming the order of results.
### Why are the changes needed?
Prevent test flakyness in SubexpressionEliminationSuite.
### Does this PR introduce _any_ user-facing change?
No, dev only.
### How was this patch tested?
Unit test.
Closes#30371 from viirya/SPARK-33337-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
In [SPARK-33139] we defined `setActionSession` and `clearActiveSession` as deprecated API, it turns out it is widely used, and after discussion, even if without this PR, it should work with unify view feature, it might only be a risk if user really abuse using these two API. So revert the PR is needed.
[SPARK-33139] has two commit, include a follow up. Revert them both.
### Why are the changes needed?
Revert.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#30367 from leanken/leanken-revert-SPARK-33139.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
SparkSession.sql converts a string value to a DataFrame, and the string value should be one single SQL statement ending up w/ or w/o one or more semicolons. e.g.
```sql
scala> spark.sql(" select 2").show
+---+
| 2|
+---+
| 2|
+---+
scala> spark.sql(" select 2;").show
+---+
| 2|
+---+
| 2|
+---+
scala> spark.sql(" select 2;;;;").show
+---+
| 2|
+---+
| 2|
+---+
```
If we put 2 or more statements in, it fails in the parser as expected, e.g.
```sql
scala> spark.sql(" select 2; select 1;").show
org.apache.spark.sql.catalyst.parser.ParseException:
extraneous input 'select' expecting {<EOF>, ';'}(line 1, pos 11)
== SQL ==
select 2; select 1;
-----------^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:263)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:130)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:51)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:610)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:610)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:769)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:607)
... 47 elided
```
As a very generic user scenario, users may want to change some settings before they execute
the queries. They may pass a string value like `set spark.sql.abc=2; select 1;` into this API, which creates a confusing gap between the actual effect and the user's expectations.
The user may want the query to be executed with spark.sql.abc=2, but Spark actually treats the whole part of `2; select 1;` as the value of the property 'spark.sql.abc',
e.g.
```
scala> spark.sql("set spark.sql.abc=2; select 1;").show
+-------------+------------+
| key| value|
+-------------+------------+
|spark.sql.abc|2; select 1;|
+-------------+------------+
```
What's more, the SET symbol could digest everything behind it, which makes it unstable from version to version, e.g.
#### 3.1
```sql
scala> spark.sql("set;").show
org.apache.spark.sql.catalyst.parser.ParseException:
Expected format is 'SET', 'SET key', or 'SET key=value'. If you want to include special characters in key, please use quotes, e.g., SET `ke y`=value.(line 1, pos 0)
== SQL ==
set;
^^^
at org.apache.spark.sql.execution.SparkSqlAstBuilder.$anonfun$visitSetConfiguration$1(SparkSqlParser.scala:83)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:113)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitSetConfiguration(SparkSqlParser.scala:72)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitSetConfiguration(SparkSqlParser.scala:58)
at org.apache.spark.sql.catalyst.parser.SqlBaseParser$SetConfigurationContext.accept(SqlBaseParser.java:2161)
at org.antlr.v4.runtime.tree.AbstractParseTreeVisitor.visit(AbstractParseTreeVisitor.java:18)
at org.apache.spark.sql.catalyst.parser.AstBuilder.$anonfun$visitSingleStatement$1(AstBuilder.scala:77)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:113)
at org.apache.spark.sql.catalyst.parser.AstBuilder.visitSingleStatement(AstBuilder.scala:77)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.$anonfun$parsePlan$1(ParseDriver.scala:82)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:113)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:51)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:610)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:610)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:769)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:607)
... 47 elided
scala> spark.sql("set a;").show
org.apache.spark.sql.catalyst.parser.ParseException:
Expected format is 'SET', 'SET key', or 'SET key=value'. If you want to include special characters in key, please use quotes, e.g., SET `ke y`=value.(line 1, pos 0)
== SQL ==
set a;
^^^
at org.apache.spark.sql.execution.SparkSqlAstBuilder.$anonfun$visitSetConfiguration$1(SparkSqlParser.scala:83)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:113)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitSetConfiguration(SparkSqlParser.scala:72)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitSetConfiguration(SparkSqlParser.scala:58)
at org.apache.spark.sql.catalyst.parser.SqlBaseParser$SetConfigurationContext.accept(SqlBaseParser.java:2161)
at org.antlr.v4.runtime.tree.AbstractParseTreeVisitor.visit(AbstractParseTreeVisitor.java:18)
at org.apache.spark.sql.catalyst.parser.AstBuilder.$anonfun$visitSingleStatement$1(AstBuilder.scala:77)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:113)
at org.apache.spark.sql.catalyst.parser.AstBuilder.visitSingleStatement(AstBuilder.scala:77)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.$anonfun$parsePlan$1(ParseDriver.scala:82)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:113)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:51)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:610)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:610)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:769)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:607)
... 47 elided
```
#### 2.4
```sql
scala> spark.sql("set;").show
+---+-----------+
|key| value|
+---+-----------+
| ;|<undefined>|
+---+-----------+
scala> spark.sql("set a;").show
+---+-----------+
|key| value|
+---+-----------+
| a;|<undefined>|
+---+-----------+
```
In this PR,
1. make `set spark.sql.abc=2; select 1;` in `SparkSession.sql` fail directly, user should call `.sql` for each statement separately.
2. make the semicolon as the separator of statements, and if users want to use it as part of the property value, shall use quotes too.
### Why are the changes needed?
1. disambiguation for `SparkSession.sql`
2. make semicolon work same both w/ `SET` and other statements
### Does this PR introduce _any_ user-facing change?
yes,
the semicolon works as a separator of statements now, it will be trimmed if it is at the end of the statement and fail the statement if it is in the middle. you need to use quotes if you want it to be part of the property value
### How was this patch tested?
new tests
Closes#30332 from yaooqinn/SPARK-33419.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change `Aggregate` max rows to 1 if grouping is empty.
### Why are the changes needed?
If `Aggregate` grouping is empty, the result is always one row.
Then we don't need push down limit in `LimitPushDown` with such case
```
select count(*) from t1
union
select count(*) from t2
limit 1
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#30356 from ulysses-you/SPARK-33433.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This patch proposes to disable the streaming query with possible correctness issue in chained stateful operators. The behavior can be controlled by a SQL config, so if users understand the risk and still want to run the query, they can disable the check.
### Why are the changes needed?
The possible correctness in chained stateful operators in streaming query is not straightforward for users. From users perspective, it will be considered as a Spark bug. It is also possible the worse case, users are not aware of the correctness issue and use wrong results.
A better approach should be to disable such queries and let users choose to run the query if they understand there is such risk, instead of implicitly running the query and let users to find out correctness issue by themselves and report this known to Spark community.
### Does this PR introduce _any_ user-facing change?
Yes. Streaming query with possible correctness issue will be blocked to run, except for users explicitly disable the SQL config.
### How was this patch tested?
Unit test.
Closes#30210 from viirya/SPARK-33259.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
In #30097, many rules changed from case class to object, but if the rule
is stateful, there will be a problem. For example, if an object rule uses a
`val` to refer to a config, it will be unchanged after initialization even if
other spark session uses a different config value.
### Why are the changes needed?
Avoid potential bug
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UT
Closes#30354 from linhongliu-db/SPARK-33140-followup-2.
Lead-authored-by: Linhong Liu <67896261+linhongliu-db@users.noreply.github.com>
Co-authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/29800 provides a performance improvement for `NTH_VALUE`.
`FIRST_VALUE` also could use the `UnboundedOffsetWindowFunctionFrame` and `UnboundedPrecedingOffsetWindowFunctionFrame`.
### Why are the changes needed?
Improve the performance for `FIRST_VALUE`.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#30178 from beliefer/SPARK-33278.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `Greatest` and `Least` check in `Canonicalize`.
### Why are the changes needed?
The children of both `Greatest` and `Least` are order Irrelevant.
Let's say we have `greatest(1, 2)` and `greatest(2, 1)`. We can get the same canonicalized expression in this case.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#30330 from ulysses-you/SPARK-33421.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Instead of returning NULL, throws runtime ArrayIndexOutOfBoundsException when ansiMode is enable for `element_at`，`elt`, `GetArrayItem` functions.
### Why are the changes needed?
For ansiMode.
### Does this PR introduce any user-facing change?
When `spark.sql.ansi.enabled` = true, Spark will throw `ArrayIndexOutOfBoundsException` if out-of-range index when accessing array elements
### How was this patch tested?
Added UT and existing UT.
Closes#30297 from leanken/leanken-SPARK-33386.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch is trying to add `AlterTableAddPartitionExec` and `AlterTableDropPartitionExec` with the new table partition API, defined in #28617.
### Does this PR introduce _any_ user-facing change?
Yes. User can use `alter table add partition` or `alter table drop partition` to create/drop partition in V2Table.
### How was this patch tested?
Run suites and fix old tests.
Closes#29339 from stczwd/SPARK-32512-new.
Lead-authored-by: stczwd <qcsd2011@163.com>
Co-authored-by: Jacky Lee <qcsd2011@163.com>
Co-authored-by: Jackey Lee <qcsd2011@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Make a special case in `ResolveReferences`, which resolves `OverwriteByExpression`'s condition expression based on the table relation instead of the input query.
### Why are the changes needed?
The condition expression is passed to the table implementation at the end, so we should resolve it using table schema. Previously it works because we have a hack in `ResolveReferences` to delay the resolution if `outputResolved == false`. However, this hack doesn't work for tables accepting any schema like https://github.com/delta-io/delta/pull/521 . We may wrongly resolve the delete condition using input query's outout columns which don't match the table column names.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing tests and updated test in v2 write.
Closes#30318 from cloud-fan/v2-write.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to migrate `SHOW CREATE TABLE` to use `UnresolvedTableOrView` to resolve the table identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
Note that `SHOW CREATE TABLE` works only with a v1 table and a permanent view, and not supported for v2 tables.
### Why are the changes needed?
The changes allow consistent resolution behavior when resolving the table identifier. For example, the following is the current behavior:
```scala
sql("CREATE TEMPORARY VIEW t AS SELECT 1")
sql("CREATE DATABASE db")
sql("CREATE TABLE t (key INT, value STRING) USING hive")
sql("USE db")
sql("SHOW CREATE TABLE t AS SERDE") // Succeeds
```
With this change, `SHOW CREATE TABLE ... AS SERDE` above fails with the following:
```
org.apache.spark.sql.AnalysisException: t is a temp view not table or permanent view.; line 1 pos 0
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveTempViews$$anonfun$apply$7.$anonfun$applyOrElse$43(Analyzer.scala:883)
at scala.Option.map(Option.scala:230)
```
, which is expected since temporary view is resolved first and `SHOW CREATE TABLE ... AS SERDE` doesn't support a temporary view.
Note that there is no behavior change for `SHOW CREATE TABLE` without `AS SERDE` since it was already resolving to a temporary view first. See below for more detail.
### Does this PR introduce _any_ user-facing change?
After this PR, `SHOW CREATE TABLE t AS SERDE` is resolved to a temp view `t` instead of table `db.t` in the above scenario.
Note that there is no behavior change for `SHOW CREATE TABLE` without `AS SERDE`, but the exception message changes from `SHOW CREATE TABLE is not supported on a temporary view` to `t is a temp view not table or permanent view`.
### How was this patch tested?
Updated existing tests.
Closes#30321 from imback82/show_create_table.
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 gather common `SHOW TABLES` tests into one trait `org.apache.spark.sql.execution.command.ShowTablesSuite`, and put datasource specific tests to the `v1.ShowTablesSuite` and `v2.ShowTablesSuite`. Also tests for parsing `SHOW TABLES` are extracted to `ShowTablesParserSuite`.
### Why are the changes needed?
- The unification will allow to run common `SHOW TABLES` tests for both DSv1 and DSv2
- We can detect missing features and differences between DSv1 and DSv2 implementations.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running new test suites:
- `org.apache.spark.sql.execution.command.v1.ShowTablesSuite`
- `org.apache.spark.sql.execution.command.v2.ShowTablesSuite`
- `ShowTablesParserSuite`
Closes#30287 from MaxGekk/unify-dsv1_v2-tests.
Lead-authored-by: Max Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Make Literal support char array.
### Why are the changes needed?
We always use `Literal()` to create foldable value, and `char[]` is a usual data type. We can make it easy that support create String Literal with `char[]`.
### Does this PR introduce _any_ user-facing change?
Yes, user can call `Literal()` with `char[]`.
### How was this patch tested?
Add test.
Closes#30295 from ulysses-you/SPARK-33390.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The following query produces incorrect results:
```
SELECT date_trunc('minute', '1769-10-17 17:10:02')
```
Spark currently incorrectly returns
```
1769-10-17 17:10:02
```
against the expected return value of
```
1769-10-17 17:10:00
```
**Steps to repro**
Run the following commands in spark-shell:
```
spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
spark.sql("SELECT date_trunc('minute', '1769-10-17 17:10:02')").show()
```
This happens as `truncTimestamp` in package `org.apache.spark.sql.catalyst.util.DateTimeUtils` incorrectly assumes that time zone offsets can never have the granularity of a second and thus does not account for time zone adjustment when truncating the given timestamp to `minute`.
This assumption is currently used when truncating the timestamps to `microsecond, millisecond, second, or minute`.
This PR fixes this issue and always uses time zone knowledge when truncating timestamps regardless of the truncation unit.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added new tests to `DateTimeUtilsSuite` which previously failed and pass now.
Closes#30303 from utkarsh39/trunc-timestamp-fix.
Authored-by: Utkarsh <utkarsh.agarwal@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Currently we skip subexpression elimination in branches of conditional expressions including `If`, `CaseWhen`, and `Coalesce`. Actually we can do subexpression elimination for such branches if the subexpression is common across all branches. This patch proposes to support subexpression elimination in branches of conditional expressions.
### Why are the changes needed?
We may miss subexpression elimination chances in branches of conditional expressions. This kind of subexpression is frequently seen. It may be written manually by users or come from query optimizer. For example, project collapsing could embed expressions between two `Project`s and produces conditional expression like:
```
CASE WHEN jsonToStruct(json).a = '1' THEN 1.0 WHEN jsonToStruct(json).a = '2' THEN 2.0 ... ELSE 1.2 END
```
If `jsonToStruct(json)` is time-expensive expression, we don't eliminate the duplication and waste time on running it repeatedly now.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#30245 from viirya/SPARK-33337.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Push down filter through expand. For case below:
```
create table t1(pid int, uid int, sid int, dt date, suid int) using parquet;
create table t2(pid int, vs int, uid int, csid int) using parquet;
SELECT
years,
appversion,
SUM(uusers) AS users
FROM (SELECT
Date_trunc('year', dt) AS years,
CASE
WHEN h.pid = 3 THEN 'iOS'
WHEN h.pid = 4 THEN 'Android'
ELSE 'Other'
END AS viewport,
h.vs AS appversion,
Count(DISTINCT u.uid) AS uusers
,Count(DISTINCT u.suid) AS srcusers
FROM t1 u
join t2 h
ON h.uid = u.uid
GROUP BY 1,
2,
3) AS a
WHERE viewport = 'iOS'
GROUP BY 1,
2
```
Plan. before this pr:
```
== Physical Plan ==
*(5) HashAggregate(keys=[years#30, appversion#32], functions=[sum(uusers#33L)])
+- Exchange hashpartitioning(years#30, appversion#32, 200), true, [id=#251]
+- *(4) HashAggregate(keys=[years#30, appversion#32], functions=[partial_sum(uusers#33L)])
+- *(4) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12], functions=[count(if ((gid#44 = 1)) u.`uid`#47 else null)])
+- Exchange hashpartitioning(date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, 200), true, [id=#246]
+- *(3) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12], functions=[partial_count(if ((gid#44 = 1)) u.`uid`#47 else null)])
+- *(3) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44], functions=[])
+- Exchange hashpartitioning(date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44, 200), true, [id=#241]
+- *(2) HashAggregate(keys=[date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44], functions=[])
+- *(2) Filter (CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46 = iOS)
+- *(2) Expand [ArrayBuffer(date_trunc(year, cast(dt#9 as timestamp), Some(Etc/GMT+7)), CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END, vs#12, uid#7, null, 1), ArrayBuffer(date_trunc(year, cast(dt#9 as timestamp), Some(Etc/GMT+7)), CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END, vs#12, null, suid#10, 2)], [date_trunc('year', CAST(u.`dt` AS TIMESTAMP))#45, CASE WHEN (h.`pid` = 3) THEN 'iOS' WHEN (h.`pid` = 4) THEN 'Android' ELSE 'Other' END#46, vs#12, u.`uid`#47, u.`suid`#48, gid#44]
+- *(2) Project [uid#7, dt#9, suid#10, pid#11, vs#12]
+- *(2) BroadcastHashJoin [uid#7], [uid#13], Inner, BuildRight
:- *(2) Project [uid#7, dt#9, suid#10]
: +- *(2) Filter isnotnull(uid#7)
: +- *(2) ColumnarToRow
: +- FileScan parquet default.t1[uid#7,dt#9,suid#10] Batched: true, DataFilters: [isnotnull(uid#7)], Format: Parquet, Location: InMemoryFileIndex[file:/root/spark-3.0.0-bin-hadoop3.2/spark-warehouse/t1], PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<uid:int,dt:date,suid:int>
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[2, int, true] as bigint))), [id=#233]
+- *(1) Project [pid#11, vs#12, uid#13]
+- *(1) Filter isnotnull(uid#13)
+- *(1) ColumnarToRow
+- FileScan parquet default.t2[pid#11,vs#12,uid#13] Batched: true, DataFilters: [isnotnull(uid#13)], Format: Parquet, Location: InMemoryFileIndex[file:/root/spark-3.0.0-bin-hadoop3.2/spark-warehouse/t2], PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<pid:int,vs:int,uid:int>
```
Plan. after. this pr. :
```
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- HashAggregate(keys=[years#0, appversion#2], functions=[sum(uusers#3L)], output=[years#0, appversion#2, users#5L])
+- Exchange hashpartitioning(years#0, appversion#2, 5), true, [id=#71]
+- HashAggregate(keys=[years#0, appversion#2], functions=[partial_sum(uusers#3L)], output=[years#0, appversion#2, sum#22L])
+- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12], functions=[count(distinct uid#7)], output=[years#0, appversion#2, uusers#3L])
+- Exchange hashpartitioning(date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, 5), true, [id=#67]
+- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12], functions=[partial_count(distinct uid#7)], output=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, count#27L])
+- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7], functions=[], output=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7])
+- Exchange hashpartitioning(date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7, 5), true, [id=#63]
+- HashAggregate(keys=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles)) AS date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END AS CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7], functions=[], output=[date_trunc(year, cast(dt#9 as timestamp), Some(America/Los_Angeles))#23, CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END#24, vs#12, uid#7])
+- Project [uid#7, dt#9, pid#11, vs#12]
+- BroadcastHashJoin [uid#7], [uid#13], Inner, BuildRight, false
:- Filter isnotnull(uid#7)
: +- FileScan parquet default.t1[uid#7,dt#9] Batched: true, DataFilters: [isnotnull(uid#7)], Format: Parquet, Location: InMemoryFileIndex[file:/private/var/folders/4l/7_c5c97s1_gb0d9_d6shygx00000gn/T/warehouse-c069d87..., PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<uid:int,dt:date>
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[2, int, false] as bigint)),false), [id=#58]
+- Filter ((CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END = iOS) AND isnotnull(uid#13))
+- FileScan parquet default.t2[pid#11,vs#12,uid#13] Batched: true, DataFilters: [(CASE WHEN (pid#11 = 3) THEN iOS WHEN (pid#11 = 4) THEN Android ELSE Other END = iOS), isnotnull..., Format: Parquet, Location: InMemoryFileIndex[file:/private/var/folders/4l/7_c5c97s1_gb0d9_d6shygx00000gn/T/warehouse-c069d87..., PartitionFilters: [], PushedFilters: [IsNotNull(uid)], ReadSchema: struct<pid:int,vs:int,uid:int>
```
### Why are the changes needed?
Improve performance, filter more data.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#30278 from AngersZhuuuu/SPARK-33302.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
element_at with CreateArray not respect one based index.
repo step:
```
var df = spark.sql("select element_at(array(3, 2, 1), 0)")
df.printSchema()
df = spark.sql("select element_at(array(3, 2, 1), 1)")
df.printSchema()
df = spark.sql("select element_at(array(3, 2, 1), 2)")
df.printSchema()
df = spark.sql("select element_at(array(3, 2, 1), 3)")
df.printSchema()
root
– element_at(array(3, 2, 1), 0): integer (nullable = false)
root
– element_at(array(3, 2, 1), 1): integer (nullable = false)
root
– element_at(array(3, 2, 1), 2): integer (nullable = false)
root
– element_at(array(3, 2, 1), 3): integer (nullable = true)
correct answer should be
0 true which is outOfBounds return default true.
1 false
2 false
3 false
```
For expression eval, it respect the oneBasedIndex, but within checking the nullable, it calculates with zeroBasedIndex using `computeNullabilityFromArray`.
### Why are the changes needed?
Correctness issue.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT and existing UT.
Closes#30296 from leanken/leanken-SPARK-33391.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to migrate `LOAD DATA` to use `UnresolvedTable` to resolve the table identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
Note that `LOAD DATA` is not supported for v2 tables.
### Why are the changes needed?
The changes allow consistent resolution behavior when resolving the table identifier. For example, the following is the current behavior:
```scala
sql("CREATE TEMPORARY VIEW t AS SELECT 1")
sql("CREATE DATABASE db")
sql("CREATE TABLE t (key INT, value STRING) USING hive")
sql("USE db")
sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE t") // Succeeds
```
With this change, `LOAD DATA` above fails with the following:
```
org.apache.spark.sql.AnalysisException: t is a temp view not table.; line 1 pos 0
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveTempViews$$anonfun$apply$7.$anonfun$applyOrElse$39(Analyzer.scala:865)
at scala.Option.foreach(Option.scala:407)
```
, which is expected since temporary view is resolved first and `LOAD DATA` doesn't support a temporary view.
### Does this PR introduce _any_ user-facing change?
After this PR, `LOAD DATA ... t` is resolved to a temp view `t` instead of table `db.t` in the above scenario.
### How was this patch tested?
Updated existing tests.
Closes#30270 from imback82/load_data_cmd.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When TableProvider.supportsExternalMetadata() is true, Spark will use the input Dataframe's schema in `DataframeWriter.save()`/`DataStreamWriter.start()` and skip schema/partitioning inference.
### Why are the changes needed?
For all the v2 data sources which are not FileDataSourceV2, Spark always infers the table schema/partitioning on `DataframeWriter.save()`/`DataStreamWriter.start()`.
The inference of table schema/partitioning can be expensive. However, there is no such trait or flag for indicating a V2 source can use the input DataFrame's schema on `DataframeWriter.save()`/`DataStreamWriter.start()`. We can resolve the problem by adding a new expected behavior for the method `TableProvider.supportsExternalMetadata()`.
### Does this PR introduce _any_ user-facing change?
Yes, a new behavior for the data source v2 API `TableProvider.supportsExternalMetadata()` when it returns true.
### How was this patch tested?
Unit test
Closes#30273 from gengliangwang/supportsExternalMetadata.
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 makes `DataFrameWriterV2` to create query plans with `UnresolvedRelation` and leave the table resolution work to the analyzer.
### Why are the changes needed?
Table resolution work should be done by the analyzer. After this PR, the behavior is more consistent between different APIs (DataFrameWriter, DataFrameWriterV2 and SQL). See the next section for behavior changes.
### Does this PR introduce _any_ user-facing change?
Yes.
1. writes to a temp view of v2 relation: previously it fails with table not found exception, now it works if the v2 relation is writable. This is consistent with `DataFrameWriter` and SQL INSERT.
2. writes to other temp views: previously it fails with table not found exception, now it fails with a more explicit error message, saying that writing to a temp view of non-v2-relation is not allowed.
3. writes to a view: previously it fails with table not writable error, now it fails with a more explicit error message, saying that writing to a view is not allowed.
4. writes to a v1 table: previously it fails with table not writable error, now it fails with a more explicit error message, saying that writing to a v1 table is not allowed. (We can allow it later, by falling back to v1 command)
### How was this patch tested?
new tests
Closes#29970 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?
There are two similar compilation warnings about procedure-like declaration in Scala 2.13:
```
[WARNING] [Warn] /spark/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala:70: procedure syntax is deprecated for constructors: add `=`, as in method definition
```
and
```
[WARNING] [Warn] /spark/core/src/main/scala/org/apache/spark/storage/BlockManagerDecommissioner.scala:211: procedure syntax is deprecated: instead, add `: Unit =` to explicitly declare `run`'s return type
```
this pr is the first part to resolve SPARK-33352：
- For constructors method definition add `=` to convert to function syntax
- For without `return type` methods definition add `: Unit =` to convert to function syntax
### Why are the changes needed?
Eliminate compilation warnings in Scala 2.13 and this change should be compatible with Scala 2.12
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#30255 from LuciferYang/SPARK-29392-FOLLOWUP.1.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Javadocs updated for the encoder to include maps as a collection type
### Why are the changes needed?
The javadocs were not updated with fix SPARK-16706
### Does this PR introduce _any_ user-facing change?
Yes, the javadocs are updated
### How was this patch tested?
sbt was run to ensure it meets scalastyle
Closes#30274 from hannahkamundson/SPARK-32860.
Lead-authored-by: Hannah Amundson <amundson.hannah@heb.com>
Co-authored-by: Hannah <48397717+hannahkamundson@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The changes in [SPARK-32501 Inconsistent NULL conversions to strings](https://issues.apache.org/jira/browse/SPARK-32501) introduced some behavior that I'd like to clean up a bit.
Here's sample code to illustrate the behavior I'd like to clean up:
```scala
val rows = Seq[String](null)
.toDF("value")
.withColumn("struct1", struct('value as "value1"))
.withColumn("struct2", struct('value as "value1", 'value as "value2"))
.withColumn("array1", array('value))
.withColumn("array2", array('value, 'value))
// Show the DataFrame using the "first" codepath.
rows.show(truncate=false)
+-----+-------+-------------+------+--------+
|value|struct1|struct2 |array1|array2 |
+-----+-------+-------------+------+--------+
|null |{ null}|{ null, null}|[] |[, null]|
+-----+-------+-------------+------+--------+
// Write the DataFrame to disk, then read it back and show it to trigger the "codegen" code path:
rows.write.parquet("rows")
spark.read.parquet("rows").show(truncate=false)
+-----+-------+-------------+-------+-------------+
|value|struct1|struct2 |array1 |array2 |
+-----+-------+-------------+-------+-------------+
|null |{ null}|{ null, null}|[ null]|[ null, null]|
+-----+-------+-------------+-------+-------------+
```
Notice:
1. If the first element of a struct is null, it is printed with a leading space (e.g. "\{ null\}"). I think it's preferable to print it without the leading space (e.g. "\{null\}"). This is consistent with how non-null values are printed inside a struct.
2. If the first element of an array is null, it is not printed at all in the first code path, and the "codegen" code path prints it with a leading space. I think both code paths should be consistent and print it without a leading space (e.g. "[null]").
The desired result of this PR is to product the following output via both code paths:
```
+-----+-------+------------+------+------------+
|value|struct1|struct2 |array1|array2 |
+-----+-------+------------+------+------------+
|null |{null} |{null, null}|[null]|[null, null]|
+-----+-------+------------+------+------------+
```
This contribution is my original work and I license the work to the project under the project’s open source license.
### Why are the changes needed?
To correct errors and inconsistencies in how DataFrame.show() displays nulls inside arrays and structs.
### Does this PR introduce _any_ user-facing change?
Yes. This PR changes what is printed out by DataFrame.show().
### How was this patch tested?
I added new test cases in CastSuite.scala to cover the cases addressed by this PR.
Closes#30189 from stwhit/show_nulls.
Authored-by: Stuart White <stuart.white1@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to introduce the `purge` option in `TableCatalog.dropTable` so that v2 catalogs can use the option if needed.
Related discussion: https://github.com/apache/spark/pull/30079#discussion_r510594110
### Why are the changes needed?
Spark DDL supports passing the purge option to `DROP TABLE` command. However, the option is not used (ignored) for v2 catalogs.
### Does this PR introduce _any_ user-facing change?
This PR introduces a new API in `TableCatalog`.
### How was this patch tested?
Added a test.
Closes#30267 from imback82/purge_table.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
1. Rename `OffsetWindowSpec` to `OffsetWindowFunction`, as it's the base class for all offset based window functions.
2. Refine and add more comments.
3. Remove `isRelative` as it's useless.
### Why are the changes needed?
code refinement
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests
Closes#30261 from cloud-fan/window.
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 fixes incorrect JIRA ids in `Analyzer.scala` introduced by SPARK-31670 (https://github.com/apache/spark/pull/28490)
```scala
- // SPARK-31607: Resolve Struct field in selectedGroupByExprs/groupByExprs and aggregations
+ // SPARK-31670: Resolve Struct field in selectedGroupByExprs/groupByExprs and aggregations
```
### Why are the changes needed?
Fix the wrong information.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This is a comment change. Manually review.
Closes#30269 from dongjoon-hyun/SPARK-31670-MINOR.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Removing duplicated code in `ResolveOutputRelation`, by adding `V2WriteCommand.withNewQuery`
### Why are the changes needed?
code cleanup
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing tests
Closes#30264 from cloud-fan/ds-minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Fix a small bug in `V2WriteCommand.resolved`. It should always require the `table` and `query` to be resolved.
### Why are the changes needed?
To prevent potential bugs that we skip resolve the input query.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
a new test
Closes#30265 from cloud-fan/ds-minor-2.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR aims to fix `semanticEquals` works correctly on `GetMapValue` expressions having literal maps with `ArrayBasedMapData` and `GenericArrayData`.
### Why are the changes needed?
This is a regression from Apache Spark 1.6.x.
```scala
scala> sc.version
res1: String = 1.6.3
scala> sqlContext.sql("SELECT map('k1', 'v1')[k] FROM t GROUP BY map('k1', 'v1')[k]").show
+---+
|_c0|
+---+
| v1|
+---+
```
Apache Spark 2.x ~ 3.0.1 raise`RuntimeException` for the following queries.
```sql
CREATE TABLE t USING ORC AS SELECT map('k1', 'v1') m, 'k1' k
SELECT map('k1', 'v1')[k] FROM t GROUP BY 1
SELECT map('k1', 'v1')[k] FROM t GROUP BY map('k1', 'v1')[k]
SELECT map('k1', 'v1')[k] a FROM t GROUP BY a
```
**BEFORE**
```scala
Caused by: java.lang.RuntimeException: Couldn't find k#3 in [keys: [k1], values: [v1][k#3]#6]
at scala.sys.package$.error(package.scala:27)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:85)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:79)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
```
**AFTER**
```sql
spark-sql> SELECT map('k1', 'v1')[k] FROM t GROUP BY 1;
v1
Time taken: 1.278 seconds, Fetched 1 row(s)
spark-sql> SELECT map('k1', 'v1')[k] FROM t GROUP BY map('k1', 'v1')[k];
v1
Time taken: 0.313 seconds, Fetched 1 row(s)
spark-sql> SELECT map('k1', 'v1')[k] a FROM t GROUP BY a;
v1
Time taken: 0.265 seconds, Fetched 1 row(s)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CIs with the newly added test case.
Closes#30246 from dongjoon-hyun/SPARK-33338.
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 proposes to migrate `ANALYZE TABLE` and `ANALYZE TABLE ... FOR COLUMNS` to use `UnresolvedTableOrView` to resolve the table/view identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
Note that `ANALYZE TABLE` is not supported for v2 tables.
### Why are the changes needed?
The changes allow consistent resolution behavior when resolving the table/view identifier. For example, the following is the current behavior:
```scala
sql("create temporary view t as select 1")
sql("create database db")
sql("create table db.t using csv as select 1")
sql("use db")
sql("ANALYZE TABLE t compute statistics") // Succeeds
```
With this change, ANALYZE TABLE above fails with the following:
```
org.apache.spark.sql.AnalysisException: t is a temp view not table or permanent view.; line 1 pos 0
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveTempViews$$anonfun$apply$7.$anonfun$applyOrElse$40(Analyzer.scala:872)
at scala.Option.map(Option.scala:230)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveTempViews$$anonfun$apply$7.applyOrElse(Analyzer.scala:870)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveTempViews$$anonfun$apply$7.applyOrElse(Analyzer.scala:856)
```
, which is expected since temporary view is resolved first and ANALYZE TABLE doesn't support a temporary view.
### Does this PR introduce _any_ user-facing change?
After this PR, `ANALYZE TABLE t` is resolved to a temp view `t` instead of table `db.t`.
### How was this patch tested?
Updated existing tests.
Closes#30229 from imback82/parse_v1table.
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 group `DateType` and `TimestampType` together in checking time zone needs in the `Cast.needsTimeZone()` method.
### Why are the changes needed?
To improve code maintainability.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By the existing test `"SPARK-33306: Timezone is needed when cast Date to String"`.
Closes#30223 from MaxGekk/WangGuangxin-SPARK-33306-followup.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
When `spark.sql.legacy.typeCoercion.datetimeToString.enabled` is enabled, spark will cast date to string when compare date with string. In Spark3, timezone is needed when casting date to string as 72ad9dcd5d/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala (L309).
Howerver, the timezone may not be set because `CastBase.needsTimeZone` returns false for this kind of casting.
A simple way to reproduce this is
```
spark-shell --conf spark.sql.legacy.typeCoercion.datetimeToString.enabled=true
```
when we execute the following sql,
```
select a.d1 from
(select to_date(concat('2000-01-0', id)) as d1 from range(1, 2)) a
join
(select concat('2000-01-0', id) as d2 from range(1, 2)) b
on a.d1 = b.d2
```
it will throw
```
java.util.NoSuchElementException: None.get
at scala.None$.get(Option.scala:529)
at scala.None$.get(Option.scala:527)
at org.apache.spark.sql.catalyst.expressions.TimeZoneAwareExpression.zoneId(datetimeExpressions.scala:56)
at org.apache.spark.sql.catalyst.expressions.TimeZoneAwareExpression.zoneId$(datetimeExpressions.scala:56)
at org.apache.spark.sql.catalyst.expressions.CastBase.zoneId$lzycompute(Cast.scala:253)
at org.apache.spark.sql.catalyst.expressions.CastBase.zoneId(Cast.scala:253)
at org.apache.spark.sql.catalyst.expressions.CastBase.dateFormatter$lzycompute(Cast.scala:287)
at org.apache.spark.sql.catalyst.expressions.CastBase.dateFormatter(Cast.scala:287)
```
### Why are the changes needed?
As described above, it's a bug here.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Add more UT
Closes#30213 from WangGuangxin/SPARK-33306.
Authored-by: wangguangxin.cn <wangguangxin.cn@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add a configuration to control the legacy behavior of whether need to pad null value when value size less then schema size.
Since we can't decide whether it's a but and some use need it behavior same as Hive.
### Why are the changes needed?
Provides a compatible choice between historical behavior and Hive
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existed UT
Closes#30156 from AngersZhuuuu/SPARK-33284.
Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
# What changes were proposed in this pull request?
In the PR, I propose to improve the error message from `from_json`/`from_csv` by combining errors from all schema parsers:
- DataType.fromJson (except CSV)
- CatalystSqlParser.parseDataType
- CatalystSqlParser.parseTableSchema
Before the changes, `from_json` does not show error messages from the first parser in the chain that could mislead users.
### Why are the changes needed?
Currently, `from_json` outputs the error message from the fallback schema parser which can confuse end-users. For example:
```scala
val invalidJsonSchema = """{"fields": [{"a":123}], "type": "struct"}"""
df.select(from_json($"json", invalidJsonSchema, Map.empty[String, String])).show()
```
The JSON schema has an issue in `{"a":123}` but the error message doesn't point it out:
```
mismatched input '{' expecting {'ADD', 'AFTER', ...}(line 1, pos 0)
== SQL ==
{"fields": [{"a":123}], "type": "struct"}
^^^
org.apache.spark.sql.catalyst.parser.ParseException:
mismatched input '{' expecting {'ADD', 'AFTER', ... }(line 1, pos 0)
== SQL ==
{"fields": [{"a":123}], "type": "struct"}
^^^
```
### Does this PR introduce _any_ user-facing change?
Yes, after the changes for the example above:
```
Cannot parse the schema in JSON format: Failed to convert the JSON string '{"a":123}' to a field.
Failed fallback parsing: Cannot parse the data type:
mismatched input '{' expecting {'ADD', 'AFTER', ...}(line 1, pos 0)
== SQL ==
{"fields": [{"a":123}], "type": "struct"}
^^^
Failed fallback parsing:
mismatched input '{' expecting {'ADD', 'AFTER', ...}(line 1, pos 0)
== SQL ==
{"fields": [{"a":123}], "type": "struct"}
^^^
```
### How was this patch tested?
- By existing tests suites like `JsonFunctionsSuite` and `JsonExpressionsSuite`.
- Add new test to `JsonFunctionsSuite`.
- Re-gen results for `json-functions.sql`.
Closes#30183 from MaxGekk/fromDDL-error-msg.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to wrap `ArrayBasedMapData` literal representation with `map(...)`.
### Why are the changes needed?
Literal ArrayBasedMapData has inconsistent string representation from `LogicalPlan` to `Optimized Logical Plan/Physical Plan`. Also, the representation at `Optimized Logical Plan` and `Physical Plan` is ambiguous like `[1 AS a#0, keys: [key1], values: [value1] AS b#1]`.
**BEFORE**
```scala
scala> spark.version
res0: String = 2.4.7
scala> sql("SELECT 1 a, map('key1', 'value1') b").explain(true)
== Parsed Logical Plan ==
'Project [1 AS a#0, 'map(key1, value1) AS b#1]
+- OneRowRelation
== Analyzed Logical Plan ==
a: int, b: map<string,string>
Project [1 AS a#0, map(key1, value1) AS b#1]
+- OneRowRelation
== Optimized Logical Plan ==
Project [1 AS a#0, keys: [key1], values: [value1] AS b#1]
+- OneRowRelation
== Physical Plan ==
*(1) Project [1 AS a#0, keys: [key1], values: [value1] AS b#1]
+- Scan OneRowRelation[]
```
**AFTER**
```scala
scala> spark.version
res0: String = 3.1.0-SNAPSHOT
scala> sql("SELECT 1 a, map('key1', 'value1') b").explain(true)
== Parsed Logical Plan ==
'Project [1 AS a#4, 'map(key1, value1) AS b#5]
+- OneRowRelation
== Analyzed Logical Plan ==
a: int, b: map<string,string>
Project [1 AS a#4, map(key1, value1) AS b#5]
+- OneRowRelation
== Optimized Logical Plan ==
Project [1 AS a#4, map(keys: [key1], values: [value1]) AS b#5]
+- OneRowRelation
== Physical Plan ==
*(1) Project [1 AS a#4, map(keys: [key1], values: [value1]) AS b#5]
+- *(1) Scan OneRowRelation[]
```
### Does this PR introduce _any_ user-facing change?
Yes. This changes the query plan's string representation in `explain` command and UI. However, this is a bug fix.
### How was this patch tested?
Pass the CI with the newly added test case.
Closes#30190 from dongjoon-hyun/SPARK-33292.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In ANSI mode, when a division by zero occurs performing a divide-like operation (Divide, IntegralDivide, Remainder or Pmod), we are returning an incorrect value. Instead, we should throw an exception, as stated in the SQL standard.
### Why are the changes needed?
Result corrupt.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
added UT + existing UTs (improved)
Closes#29882 from luluorta/SPARK-33008.
Authored-by: luluorta <luluorta@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch proposes to make StateStore compression codec configurable.
### Why are the changes needed?
Currently the compression codec of StateStore is not configurable and hard-coded to be lz4. It is better if we can follow Spark other modules to configure the compression codec of StateStore. For example, we can choose zstd codec and zstd is configurable with different compression level.
### Does this PR introduce _any_ user-facing change?
Yes, after this change users can config different codec for StateStore.
### How was this patch tested?
Unit test.
Closes#30162 from viirya/SPARK-33263.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Return schema in SQL format instead of Catalog string from the SchemaOfCsv expression.
### Why are the changes needed?
To unify output of the `schema_of_json()` and `schema_of_csv()`.
### Does this PR introduce _any_ user-facing change?
Yes, they can but `schema_of_csv()` is usually used in combination with `from_csv()`, so, the format of schema shouldn't be much matter.
Before:
```
> SELECT schema_of_csv('1,abc');
struct<_c0:int,_c1:string>
```
After:
```
> SELECT schema_of_csv('1,abc');
STRUCT<`_c0`: INT, `_c1`: STRING>
```
### How was this patch tested?
By existing test suites `CsvFunctionsSuite` and `CsvExpressionsSuite`.
Closes#30180 from MaxGekk/schema_of_csv-sql-schema.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Return schema in SQL format instead of Catalog string from the `SchemaOfJson` expression.
### Why are the changes needed?
In some cases, `from_json()` cannot parse schemas returned by `schema_of_json`, for instance, when JSON fields have spaces (gaps). Such fields will be quoted after the changes, and can be parsed by `from_json()`.
Here is the example:
```scala
val in = Seq("""{"a b": 1}""").toDS()
in.select(from_json('value, schema_of_json("""{"a b": 100}""")) as "parsed")
```
raises the exception:
```
== SQL ==
struct<a b:bigint>
------^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:263)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:130)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parseTableSchema(ParseDriver.scala:76)
at org.apache.spark.sql.types.DataType$.fromDDL(DataType.scala:131)
at org.apache.spark.sql.catalyst.expressions.ExprUtils$.evalTypeExpr(ExprUtils.scala:33)
at org.apache.spark.sql.catalyst.expressions.JsonToStructs.<init>(jsonExpressions.scala:537)
at org.apache.spark.sql.functions$.from_json(functions.scala:4141)
```
### Does this PR introduce _any_ user-facing change?
Yes. For example, `schema_of_json` for the input `{"col":0}`.
Before: `struct<col:bigint>`
After: `STRUCT<`col`: BIGINT>`
### How was this patch tested?
By existing test suites `JsonFunctionsSuite` and `JsonExpressionsSuite`.
Closes#30172 from MaxGekk/schema_of_json-sql-schema.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
For complex query plans, `QueryPlan.transformUpWithNewOutput` will keep accumulating the attributes mapping to be propagated, which may hurt performance. This PR prunes the attributes mapping before propagating.
### Why are the changes needed?
A simple perf improvement.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing tests
Closes#30173 from cloud-fan/bug.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR proposes to fix the NPE issue on `In` filter when one of values contain null. In real case, you can trigger this issue when you try to push down the filter with `in (..., null)` against V2 source table. `DataSourceStrategy` caches the mapping (filter instance -> expression) in HashMap, which leverages hash code on the key, hence it could trigger the NPE issue.
### Why are the changes needed?
This is an obvious bug as `In` filter doesn't care about null value when calculating hash code.
### Does this PR introduce _any_ user-facing change?
Yes, previously the query with having `null` in "in" condition against data source V2 source table supporting push down filter failed with NPE, whereas after the PR the query will not fail.
### How was this patch tested?
UT added. The new UT fails without the PR and passes with the PR.
Closes#30170 from HeartSaVioR/SPARK-33267.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR intends to fix bus for casting data from/to PythonUserDefinedType. A sequence of queries to reproduce this issue is as follows;
```
>>> from pyspark.sql import Row
>>> from pyspark.sql.functions import col
>>> from pyspark.sql.types import *
>>> from pyspark.testing.sqlutils import *
>>>
>>> row = Row(point=ExamplePoint(1.0, 2.0))
>>> df = spark.createDataFrame([row])
>>> df.select(col("point").cast(PythonOnlyUDT()))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/maropu/Repositories/spark/spark-master/python/pyspark/sql/dataframe.py", line 1402, in select
jdf = self._jdf.select(self._jcols(*cols))
File "/Users/maropu/Repositories/spark/spark-master/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
File "/Users/maropu/Repositories/spark/spark-master/python/pyspark/sql/utils.py", line 111, in deco
return f(*a, **kw)
File "/Users/maropu/Repositories/spark/spark-master/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o44.select.
: java.lang.NullPointerException
at org.apache.spark.sql.types.UserDefinedType.acceptsType(UserDefinedType.scala:84)
at org.apache.spark.sql.catalyst.expressions.Cast$.canCast(Cast.scala:96)
at org.apache.spark.sql.catalyst.expressions.CastBase.checkInputDataTypes(Cast.scala:267)
at org.apache.spark.sql.catalyst.expressions.CastBase.resolved$lzycompute(Cast.scala:290)
at org.apache.spark.sql.catalyst.expressions.CastBase.resolved(Cast.scala:290)
```
A root cause of this issue is that, since `PythonUserDefinedType#userClassis` always null, `isAssignableFrom` in `UserDefinedType#acceptsType` throws a null exception. To fix it, this PR defines `acceptsType` in `PythonUserDefinedType` and filters out the null case in `UserDefinedType#acceptsType`.
### Why are the changes needed?
Bug fixes.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added tests.
Closes#30169 from maropu/FixPythonUDTCast.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Spark SQL supports some window function like `NTH_VALUE`.
If we specify window frame like `UNBOUNDED PRECEDING AND CURRENT ROW` or `UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING`, we can elimate some calculations.
For example: if we execute the SQL show below:
```
SELECT NTH_VALUE(col,
2) OVER(ORDER BY rank UNBOUNDED PRECEDING
AND CURRENT ROW)
FROM tab;
```
The output for row number greater than 1, return the fixed value. otherwise, return null. So we just calculate the value once and notice whether the row number less than 2.
`UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING` is simpler.
### Why are the changes needed?
Improve the performance for `NTH_VALUE`, `FIRST_VALUE` and `LAST_VALUE`.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#29800 from beliefer/optimize-nth_value.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to fix a correctness bug in the optimizer rule `EliminateSorts`. It also adds a new physical rule to remove redundant sorts that cannot be eliminated in the Optimizer rule after the bugfix.
### Why are the changes needed?
A global sort should not be eliminated even if its child is ordered since we don't know if its child ordering is global or local. For example, in the following scenario, the first sort shouldn't be removed because it has a stronger guarantee than the second sort even if the sort orders are the same for both sorts.
```
Sort(orders, global = True, ...)
Sort(orders, global = False, ...)
```
Since there is no straightforward way to identify whether a node's output ordering is local or global, we should not remove a global sort even if its child is already ordered.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
Unit tests
Closes#30093 from allisonwang-db/fix-sort.
Authored-by: allisonwang-db <66282705+allisonwang-db@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to migrate `DROP TABLE` to use `UnresolvedTableOrView` to resolve the table/view identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
### Why are the changes needed?
The current behavior is not consistent between v1 and v2 commands when resolving a temp view.
In v2, the `t` in the following example is resolved to a table:
```scala
sql("CREATE TABLE testcat.ns.t (id bigint) USING foo")
sql("CREATE TEMPORARY VIEW t AS SELECT 2")
sql("USE testcat.ns")
sql("DROP TABLE t") // 't' is resolved to testcat.ns.t
```
whereas in v1, the `t` is resolved to a temp view:
```scala
sql("CREATE DATABASE test")
sql("CREATE TABLE spark_catalog.test.t (id bigint) USING csv")
sql("CREATE TEMPORARY VIEW t AS SELECT 2")
sql("USE spark_catalog.test")
sql("DROP TABLE t") // 't' is resolved to a temp view
```
### Does this PR introduce _any_ user-facing change?
After this PR, for v2, `DROP TABLE t` is resolved to a temp view `t` instead of `testcat.ns.t`, consistent with v1 behavior.
### How was this patch tested?
Added a new test
Closes#30079 from imback82/drop_table_consistent.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch proposes to change the behavior on failing fast when Spark fails to instantiate configured v2 session catalog.
### Why are the changes needed?
The Spark behavior is against the intention of the end users - if end users configure session catalog which Spark would fail to initialize, Spark would swallow the error with only logging the error message and silently use the default catalog implementation.
This follows the voices on [discussion thread](https://lists.apache.org/thread.html/rdfa22a5ebdc4ac66e2c5c8ff0cd9d750e8a1690cd6fb456d119c2400%40%3Cdev.spark.apache.org%3E) in dev mailing list.
### Does this PR introduce _any_ user-facing change?
Yes. After the PR Spark will fail immediately if Spark fails to instantiate configured session catalog.
### How was this patch tested?
New UT added.
Closes#30147 from HeartSaVioR/SPARK-33240.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The following query produces incorrect results. The query has two essential features: (1) it contains a string aggregate, resulting in a `SortExec` node, and (2) it contains a duplicate grouping key, causing `RemoveRepetitionFromGroupExpressions` to produce a sort order stored as a `Stream`.
```sql
SELECT bigint_col_1, bigint_col_9, MAX(CAST(bigint_col_1 AS string))
FROM table_4
GROUP BY bigint_col_1, bigint_col_9, bigint_col_9
```
When the sort order is stored as a `Stream`, the line `ordering.map(_.child.genCode(ctx))` in `GenerateOrdering#createOrderKeys()` produces unpredictable side effects to `ctx`. This is because `genCode(ctx)` modifies `ctx`. When ordering is a `Stream`, the modifications will not happen immediately as intended, but will instead occur lazily when the returned `Stream` is used later.
Similar bugs have occurred at least three times in the past: https://issues.apache.org/jira/browse/SPARK-24500, https://issues.apache.org/jira/browse/SPARK-25767, https://issues.apache.org/jira/browse/SPARK-26680.
The fix is to check if `ordering` is a `Stream` and force the modifications to happen immediately if so.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added a unit test for `SortExec` where `sortOrder` is a `Stream`. The test previously failed and now passes.
Closes#30160 from ankurdave/SPARK-33260.
Authored-by: Ankur Dave <ankurdave@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Extract methods related to handling Aliases to a trait.
### Why are the changes needed?
Avoid code duplication
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UTs cover this
Closes#30134 from tanelk/SPARK-33225_aliasHelper.
Lead-authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Co-authored-by: Tanel Kiis <tanel.kiis@reach-u.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Since Issue [SPARK-33139](https://issues.apache.org/jira/browse/SPARK-33139) has been done, and SQLConf.get and SparkSession.active are more reliable. We are trying to refine the existing code usage of passing SQLConf and SparkSession into sub-class of Rule[QueryPlan].
In this PR.
* remove SQLConf from ctor-parameter of all sub-class of Rule[QueryPlan].
* using SQLConf.get to replace the original SQLConf instance.
* remove SparkSession from ctor-parameter of all sub-class of Rule[QueryPlan].
* using SparkSession.active to replace the original SparkSession instance.
### Why are the changes needed?
Code refine.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing test
Closes#30097 from leanken/leanken-SPARK-33140.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR is to enable auto bucketed table scan by default, with exception to only disable for cached query (similar to AQE). The reason why disabling auto scan for cached query is that, the cached query output partitioning can be leveraged later to avoid shuffle and sort when doing join and aggregate.
### Why are the changes needed?
Enable auto bucketed table scan by default is useful as it can optimize query automatically under the hood, without users interaction.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit test for cached query in `DisableUnnecessaryBucketedScanSuite.scala`. Also change a bunch of unit tests which should disable auto bucketed scan to make them work.
Closes#30138 from c21/enable-auto-bucket.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Make changes to `spark.sql.analyzer.maxIterations` take effect at runtime.
### Why are the changes needed?
`spark.sql.analyzer.maxIterations` is not a static conf. However, before this patch, changing `spark.sql.analyzer.maxIterations` at runtime does not take effect.
### Does this PR introduce _any_ user-facing change?
Yes. Before this patch, changing `spark.sql.analyzer.maxIterations` at runtime does not take effect.
### How was this patch tested?
modified unit test
Closes#30108 from yuningzh-db/dynamic-analyzer-max-iterations.
Authored-by: Yuning Zhang <yuning.zhang@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This is to support left semi join in stream-stream join. The implementation of left semi join is (mostly in `StreamingSymmetricHashJoinExec` and `SymmetricHashJoinStateManager`):
* For left side input row, check if there's a match on right side state store.
* if there's a match, output the left side row, but do not put the row in left side state store (no need to put in state store).
* if there's no match, output nothing, but put the row in left side state store (with "matched" field to set to false in state store).
* For right side input row, check if there's a match on left side state store.
* For all matched left rows in state store, output the rows with "matched" field as false. Set all left rows with "matched" field to be true. Only output the left side rows matched for the first time to guarantee left semi join semantics.
* State store eviction: evict rows from left/right side state store below watermark, same as inner join.
Note a followup optimization can be to evict matched left side rows from state store earlier, even when the rows are still above watermark. However this needs more change in `SymmetricHashJoinStateManager`, so will leave this as a followup.
### Why are the changes needed?
Current stream-stream join supports inner, left outer and right outer join (https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala#L166 ). We do see internally a lot of users are using left semi stream-stream join (not spark structured streaming), e.g. I want to get the ad impression (join left side) which has click (joint right side), but I don't care how many clicks per ad (left semi semantics).
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit tests in `UnsupportedOperationChecker.scala` and `StreamingJoinSuite.scala`.
Closes#30076 from c21/stream-join.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
`PLAN_CHANGE_LOG_LEVEL` config document is wrong. This is to fix it.
### Why are the changes needed?
Fix wrong doc.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Only doc change.
Closes#30136 from viirya/minor-sqlconf.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
- Building spark internally in orgs where access to outside internet is not allowed takes a long time because unsuccessful attempts are made to download artifacts from repositories which are not accessible. The unsuccessful attempts unnecessarily add significant amount of time to the build. I have seen a difference of up-to 1hr for some runs.
- Adding 1 environment variables that should be present that the start of the build and if they exist, override the default repos defined in the code and scripts.
envVariables:
- DEFAULT_ARTIFACT_REPOSITORY=https://artifacts.internal.com/libs-release/
### Why are the changes needed?
To allow orgs to build spark internally without relying on external repositories for artifact downloads.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Multiple builds with and without env variables set.
Closes#29874 from ankits/SPARK-32998.
Authored-by: Ankit Srivastava <ankit_srivastava@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Support query auto cancelling when running too long on thriftserver.
This is the rework of #28991 and the credit should be the original author, leoluan2009.
Closes#28991
### Why are the changes needed?
For some cases, we use thriftserver as long-running applications.
Some times we want all the query need not to run more than given time.
In these cases, we can enable auto cancel for time-consumed query.Which can let us release resources for other queries to run.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added tests.
Closes#29933 from maropu/pr28991.
Lead-authored-by: Xuedong Luan <luanxuedong2009@gmail.com>
Co-authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Co-authored-by: Luan <luanxuedong2009@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
`REGEXP_REPLACE` could replace all substrings of string that match regexp with replacement string.
But `REGEXP_REPLACE` lost some flexibility. such as: converts camel case strings to a string containing lower case words separated by an underscore:
AddressLine1 -> address_line_1
If we support the parameter position, we can do like this(e.g. Oracle):
```
WITH strings as (
SELECT 'AddressLine1' s FROM dual union all
SELECT 'ZipCode' s FROM dual union all
SELECT 'Country' s FROM dual
)
SELECT s "STRING",
lower(regexp_replace(s, '([A-Z0-9])', '_\1', 2)) "MODIFIED_STRING"
FROM strings;
```
The output:
```
STRING MODIFIED_STRING
-------------------- --------------------
AddressLine1 address_line_1
ZipCode zip_code
Country country
```
There are some mainstream database support the syntax.
**Oracle**
https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlrf/REGEXP_REPLACE.html#GUID-EA80A33C-441A-4692-A959-273B5A224490
**Vertica**
https://www.vertica.com/docs/9.2.x/HTML/Content/Authoring/SQLReferenceManual/Functions/RegularExpressions/REGEXP_REPLACE.htm?zoom_highlight=regexp_replace
**Redshift**
https://docs.aws.amazon.com/redshift/latest/dg/REGEXP_REPLACE.html
### Why are the changes needed?
The parameter position for `REGEXP_REPLACE` is very useful.
### Does this PR introduce _any_ user-facing change?
'Yes'.
### How was this patch tested?
Jenkins test.
Closes#29891 from beliefer/add-position-for-regex_replace.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This switches Spark to use shaded Hadoop clients, namely hadoop-client-api and hadoop-client-runtime, for Hadoop 3.x. For Hadoop 2.7, we'll still use the same modules such as hadoop-client.
In order to still keep default Hadoop profile to be hadoop-3.2, this defines the following Maven properties:
```
hadoop-client-api.artifact
hadoop-client-runtime.artifact
hadoop-client-minicluster.artifact
```
which default to:
```
hadoop-client-api
hadoop-client-runtime
hadoop-client-minicluster
```
but all switch to `hadoop-client` when the Hadoop profile is hadoop-2.7. A side affect from this is we'll import the same dependency multiple times. For this I have to disable Maven enforcer `banDuplicatePomDependencyVersions`.
Besides above, there are the following changes:
- explicitly add a few dependencies which are imported via transitive dependencies from Hadoop jars, but are removed from the shaded client jars.
- removed the use of `ProxyUriUtils.getPath` from `ApplicationMaster` which is a server-side/private API.
- modified `IsolatedClientLoader` to exclude `hadoop-auth` jars when Hadoop version is 3.x. This change should only matter when we're not sharing Hadoop classes with Spark (which is _mostly_ used in tests).
### Why are the changes needed?
This serves two purposes:
- to unblock Spark from upgrading to Hadoop 3.2.2/3.3.0+. Latest Hadoop versions have upgraded to use Guava 27+ and in order to adopt the latest Hadoop versions in Spark, we'll need to resolve the Guava conflicts. This takes the approach by switching to shaded client jars provided by Hadoop.
- avoid pulling 3rd party dependencies from Hadoop and avoid potential future conflicts.
### Does this PR introduce _any_ user-facing change?
When people use Spark with `hadoop-provided` option, they should make sure class path contains `hadoop-client-api` and `hadoop-client-runtime` jars. In addition, they may need to make sure these jars appear before other Hadoop jars in the order. Otherwise, classes may be loaded from the other non-shaded Hadoop jars and cause potential conflicts.
### How was this patch tested?
Relying on existing tests.
Closes#29843 from sunchao/SPARK-29250.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
1. Set the default value for the SQL configs `spark.sql.legacy.parquet.int96RebaseModeInWrite` and `spark.sql.legacy.parquet.int96RebaseModeInRead` to `EXCEPTION`.
2. Update the SQL migration guide.
### Why are the changes needed?
Current default value `LEGACY` may lead to shifting timestamps in read or in write. We should leave the decision about rebasing to users.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By existing test suites like `ParquetIOSuite`.
Closes#30121 from MaxGekk/int96-exception-by-default.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Add the SQL config `spark.sql.legacy.parquet.int96RebaseModeInWrite` to control timestamps rebasing in saving them as INT96. It supports the same set of values as `spark.sql.legacy.parquet.datetimeRebaseModeInWrite` but the default value is `LEGACY` to preserve backward compatibility with Spark <= 3.0.
2. Write the metadata key `org.apache.spark.int96NoRebase` to parquet files if the files are saved with `spark.sql.legacy.parquet.int96RebaseModeInWrite` isn't set to `LEGACY`.
3. Add the SQL config `spark.sql.legacy.parquet.datetimeRebaseModeInRead` to control loading INT96 timestamps when parquet metadata doesn't have enough info (the `org.apache.spark.int96NoRebase` tag) about parquet writer - either INT96 was written by Proleptic Gregorian system or some Julian one.
4. Modified Vectorized and Parquet-mr Readers to support loading/saving INT96 timestamps w/o rebasing depending on SQL config and the metadata tag:
- **No rebasing** in testing when the SQL config `spark.test.forceNoRebase` is set to `true`
- **No rebasing** if parquet metadata contains the tag `org.apache.spark.int96NoRebase`. This is the case when parquet files are saved by Spark >= 3.1 with `spark.sql.legacy.parquet.datetimeRebaseModeInWrite` is set to `CORRECTED`, or saved by other systems with the tag `org.apache.spark.int96NoRebase`.
- **With rebasing** if parquet files saved by Spark (any versions) without the metadata tag `org.apache.spark.int96NoRebase`.
- Rebasing depend on the SQL config `spark.sql.legacy.parquet.datetimeRebaseModeInRead` if there are no metadata tags `org.apache.spark.version` and `org.apache.spark.int96NoRebase`.
New SQL configs are added instead of re-using existing `spark.sql.legacy.parquet.datetimeRebaseModeInWrite` and `spark.sql.legacy.parquet.datetimeRebaseModeInRead` because of:
- To allow users have different modes for INT96 and for TIMESTAMP_MICROS (MILLIS). For example, users might want to save INT96 as LEGACY but TIMESTAMP_MICROS as CORRECTED.
- To have different modes for INT96 and DATE in load (or in save).
- To be backward compatible with Spark 2.4. For now, `spark.sql.legacy.parquet.datetimeRebaseModeInWrite/Read` are set to `EXCEPTION` by default.
### Why are the changes needed?
1. Parquet spec says that INT96 must be stored as Julian days (see https://github.com/apache/parquet-format/pull/49). This doesn't mean that a reader ( or a writer) is based on the Julian calendar. So, rebasing from Proleptic Gregorian to Julian calendar can be not needed.
2. Rebasing from/to Julian calendar can loose information because dates in one calendar don't exist in another one. Like 1582-10-04..1582-10-15 exist in Proleptic Gregorian calendar but not in the hybrid calendar (Julian + Gregorian), and visa versa, Julian date 1000-02-29 doesn't exist in Proleptic Gregorian calendar. We should allow users to save timestamps without loosing such dates (rebasing shifts such dates to the next valid date).
3. It would also make Spark compatible with other systems such as Impala and newer versions of Hive that write proleptic Gregorian based INT96 timestamps.
### Does this PR introduce _any_ user-facing change?
It can when `spark.sql.legacy.parquet.int96RebaseModeInWrite` is set non-default value `LEGACY`.
### How was this patch tested?
- Added a test to check the metadata key `org.apache.spark.int96NoRebase`
- By `ParquetIOSuite`
Closes#30056 from MaxGekk/parquet-rebase-int96.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch proposes to add more optimization to `UpdateFields` expression chain. And optimize `UpdateFields` early in analysis phase.
### Why are the changes needed?
`UpdateFields` can manipulate complex nested data, but using `UpdateFields` can easily create inefficient expression chain. We should optimize it further.
Because when manipulating deeply nested schema, the `UpdateFields` expression tree could be too complex to analyze, this change optimizes `UpdateFields` early in analysis phase.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#29812 from viirya/SPARK-32941.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Mark `CollectList` and `CollectSet` as non-nullable.
### Why are the changes needed?
`CollectList` and `CollectSet` SQL expressions never return null value. Marking them as non-nullable can have some performance benefits, because some optimizer rules apply only to non-nullable expressions
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Did not find any existing tests on the nullability of aggregate functions.
Closes#30087 from tanelk/SPARK-33177_collect.
Authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch proposes to add a config we can control fast-fail behavior in FileFormatWriter and set it false by default.
### Why are the changes needed?
In SPARK-29649, we catch `FileAlreadyExistsException` in `FileFormatWriter` and fail fast for the task set to prevent task retry.
Due to latest discussion, it is important to be able to keep original behavior that is to retry tasks even `FileAlreadyExistsException` is thrown, because `FileAlreadyExistsException` could be recoverable in some cases.
We are going to add a config we can control this behavior and set it false for fast-fail by default.
### Does this PR introduce _any_ user-facing change?
Yes. By default the task in FileFormatWriter will retry even if `FileAlreadyExistsException` is thrown. This is the behavior before Spark 3.0. User can control fast-fail behavior by enabling it.
### How was this patch tested?
Unit test.
Closes#30073 from viirya/SPARK-33170.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is a minor change to update structured-streaming-programming-guide and typos in code.
### Why are the changes needed?
Keep the user-facing document correct and updated.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests.
Closes#30074 from viirya/ss-minor.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
SPARK-29358 added support for `unionByName` to work when the two datasets didn't necessarily have the same schema, but it does not work with nested columns like structs. This patch adds the support to work with struct columns.
The behavior before this PR:
```scala
scala> val df1 = spark.range(1).selectExpr("id c0", "named_struct('c', id + 1, 'b', id + 2, 'a', id + 3) c1")
scala> val df2 = spark.range(1).selectExpr("id c0", "named_struct('c', id + 1, 'b', id + 2) c1")
scala> df1.unionByName(df2, true).printSchema
org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the compatible column types. struct<c:bigint,b:bigint> <> struct<c:bigint,b:bigint,a:bigint> at the second column of the second table;;
'Union false, false
:- Project [id#0L AS c0#2L, named_struct(c, (id#0L + cast(1 as bigint)), b, (id#0L + cast(2 as bigint)), a, (id#0L + cast(3 as bigint))) AS c1#3]
: +- Range (0, 1, step=1, splits=Some(12))
+- Project [c0#8L, c1#9]
+- Project [id#6L AS c0#8L, named_struct(c, (id#6L + cast(1 as bigint)), b, (id#6L + cast(2 as bigint))) AS c1#9]
+- Range (0, 1, step=1, splits=Some(12))
```
The behavior after this PR:
```scala
scala> df1.unionByName(df2, true).printSchema
root
|-- c0: long (nullable = false)
|-- c1: struct (nullable = false)
| |-- a: long (nullable = true)
| |-- b: long (nullable = false)
| |-- c: long (nullable = false)
scala> df1.unionByName(df2, true).show()
+---+-------------+
| c0| c1|
+---+-------------+
| 0| {3, 2, 1}|
| 0|{ null, 2, 1}|
+---+-------------+
```
### Why are the changes needed?
The `allowMissingColumns` of `unionByName` is a feature allowing merging different schema from two datasets when unioning them together. Nested column support makes the feature more general and flexible for usage.
### Does this PR introduce _any_ user-facing change?
Yes, after this change users can union two datasets with different schema with different structs.
### How was this patch tested?
Unit tests.
Closes#29587 from viirya/SPARK-32376.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Correct the resolution of having clause.
### Why are the changes needed?
Grouping sets construct new aggregate lost the qualified name of grouping expression. Here is a example:
```
-- Works resolved by `ResolveReferences`
select c1 from values (1) as t1(c1) group by grouping sets(t1.c1) having c1 = 1
-- Works because of the extra expression c1
select c1 as c2 from values (1) as t1(c1) group by grouping sets(t1.c1) having t1.c1 = 1
-- Failed
select c1 from values (1) as t1(c1) group by grouping sets(t1.c1) having t1.c1 = 1
```
It wroks with `Aggregate` without grouping sets through `ResolveReferences`, but Grouping sets not works since the exprId has been changed.
### Does this PR introduce _any_ user-facing change?
Yes, bug fix.
### How was this patch tested?
add test.
Closes#30029 from ulysses-you/SPARK-33131.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The current `Lead`/`Lag` extends `OffsetWindowFunction`. `OffsetWindowFunction` contains field `direction` and use `direction` to calculates the `boundary`.
We can use single literal expression unify the two properties.
For example:
3 means `direction` is Asc and `boundary` is 3.
-3 means `direction` is Desc and `boundary` is -3.
### Why are the changes needed?
Improve the current implement of `Lead`/`Lag`.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#30023 from beliefer/SPARK-33126.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR is a sub-task of [SPARK-33138](https://issues.apache.org/jira/browse/SPARK-33138). In order to make SQLConf.get reliable and stable, we need to make sure user can't pollute the SQLConf and SparkSession Context via calling setActiveSession and clearActiveSession.
Change of the PR:
* add legacy config spark.sql.legacy.allowModifyActiveSession to fallback to old behavior if user do need to call these two API.
* by default, if user call these two API, it will throw exception
* add extra two internal and private API setActiveSessionInternal and clearActiveSessionInternal for current internal usage
* change all internal reference to new internal API except for SQLContext.setActive and SQLContext.clearActive
### Why are the changes needed?
Make SQLConf.get reliable and stable.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
* Add UT in SparkSessionBuilderSuite to test the legacy config
* Existing test
Closes#30042 from leanken/leanken-SPARK-33139.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Current solution in build file to enable build failure on compilation warnings with exclusion of deprecation ones is not portable after SBT version 1.3.13 (build import fails with compilation error with SBT 1.4) and could be replaced with more robust and maintainable, especially since Scala 2.13.2 with similar built-in functionality.
Additionally, warnings were fixed to pass the build, with as few changes as possible:
warnings in 2.12 compilation fixed in code,
warnings in 2.13 compilation covered by configuration to be addressed separately
### Why are the changes needed?
Unblocks upgrade to SBT after 1.3.13.
Enhances build file maintainability.
Allows fine tune of warnings configuration in scope of Scala 2.13 compilation.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
`build/sbt`'s `compile` and `Test/compile` for both Scala 2.12 and 2.13 profiles.
Closes#29995 from gemelen/feature/warnings-reporter.
Authored-by: Denis Pyshev <git@gemelen.net>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This proposes to add a config for json expression optimization.
### Why are the changes needed?
For the new Json expression optimization rules, it is safer if we can disable it using SQL config.
### Does this PR introduce _any_ user-facing change?
Yes, users can disable json expression optimization rule.
### How was this patch tested?
Unit test
Closes#30047 from viirya/SPARK-33078.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR proposes to fix a bug on calling `DataType.equalsIgnoreCompatibleNullability` with mistakenly swapped parameters in `V2WriteCommand.outputResolved`. The order of parameters for `DataType.equalsIgnoreCompatibleNullability` are `from` and `to`, which says that the right order of matching variables are `inAttr` and `outAttr`.
### Why are the changes needed?
Spark throws AnalysisException due to unresolved operator in v2 write, while the operator is unresolved due to a bug that parameters to call `DataType.equalsIgnoreCompatibleNullability` in `outputResolved` have been swapped.
### Does this PR introduce _any_ user-facing change?
Yes, end users no longer suffer on unresolved operator in v2 write if they're trying to write dataframe containing non-nullable complex types against table matching complex types as nullable.
### How was this patch tested?
New UT added.
Closes#30033 from HeartSaVioR/SPARK-33136.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose to restrict the partial result feature only by root JSON objects. JSON datasource as well as `from_json()` will return `null` for malformed nested JSON objects.
### Why are the changes needed?
1. To not raise exception to users in the PERMISSIVE mode
2. To fix a regression and to have the same behavior as Spark 2.4.x has
3. Current implementation of partial result is supposed to work only for root (top-level) JSON objects, and not tested for bad nested complex JSON fields.
### Does this PR introduce _any_ user-facing change?
Yes. Before the changes, the code below:
```scala
val pokerhand_raw = Seq("""[{"cards": [19], "playerId": 123456}]""").toDF("events")
val event = new StructType().add("playerId", LongType).add("cards", ArrayType(new StructType().add("id", LongType).add("rank", StringType)))
val pokerhand_events = pokerhand_raw.select(from_json($"events", ArrayType(event)).as("event"))
pokerhand_events.show
```
throws the exception even in the default **PERMISSIVE** mode:
```java
java.lang.ClassCastException: java.lang.Long cannot be cast to org.apache.spark.sql.catalyst.util.ArrayData
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getArray(rows.scala:48)
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getArray$(rows.scala:48)
at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.getArray(rows.scala:195)
```
After the changes:
```
+-----+
|event|
+-----+
| null|
+-----+
```
### How was this patch tested?
Added a test to `JsonFunctionsSuite`.
Closes#30031 from MaxGekk/json-skip-row-wrong-schema.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
As [SPARK-13860](https://issues.apache.org/jira/browse/SPARK-13860) stated, TPCDS Query 39 returns wrong results using SparkSQL. The root cause is that when stddev_samp is applied to a single element set, with TPCDS answer, it return null; as in SparkSQL, it return Double.NaN which caused the wrong result.
Add an extra legacy config to fallback into the NaN logical, and return null by default to align with TPCDS standard.
### Why are the changes needed?
SQL correctness issue.
### Does this PR introduce any user-facing change?
Yes. See sql-migration-guide
In Spark 3.1, statistical aggregation function includes `std`, `stddev`, `stddev_samp`, `variance`, `var_samp`, `skewness`, `kurtosis`, `covar_samp`, `corr` will return `NULL` instead of `Double.NaN` when `DivideByZero` occurs during expression evaluation, for example, when `stddev_samp` applied on a single element set. In Spark version 3.0 and earlier, it will return `Double.NaN` in such case. To restore the behavior before Spark 3.1, you can set `spark.sql.legacy.statisticalAggregate` to `true`.
### How was this patch tested?
Updated DataFrameAggregateSuite/DataFrameWindowFunctionsSuite to test both default and legacy behavior.
Adjust DataFrameWindowFunctionsSuite/SQLQueryTestSuite and some R case to update to the default return null behavior.
Closes#29983 from leanken/leanken-SPARK-13860.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Except for Postgresql, other data sources (for example: vertica, oracle, redshift, mysql, presto) are not allowed to specify window frame for the Lead and Lag functions.
But the current error message is not clear enough.
`Window Frame $f must match the required frame`
This PR will use the following error message.
`Cannot specify window frame for lead function`
### Why are the changes needed?
Make clear error message.
### Does this PR introduce _any_ user-facing change?
Yes
Users will see the clearer error message.
### How was this patch tested?
Jenkins test.
Closes#30021 from beliefer/SPARK-33125.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
- Override the default SQL strings in the DB2 Dialect for:
* ALTER TABLE UPDATE COLUMN TYPE
* ALTER TABLE UPDATE COLUMN NULLABILITY
- Add new docker integration test suite jdbc/v2/DB2IntegrationSuite.scala
### Why are the changes needed?
In SPARK-24907, we implemented JDBC v2 Table Catalog but it doesn't support some ALTER TABLE at the moment. This PR supports DB2 specific ALTER TABLE.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By running new integration test suite:
$ ./build/sbt -Pdocker-integration-tests "test-only *.DB2IntegrationSuite"
Closes#29972 from huaxingao/db2_docker.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In SPARK-24994 we implemented unwrapping cast for **integral types**. This extends it to support **numeric types** such as float/double/decimal, so that filters involving these types can be better pushed down to data sources.
Unlike the cases of integral types, conversions between numeric types can result to rounding up or downs. Consider the following case:
```sql
cast(e as double) < 1.9
```
assume type of `e` is short, since 1.9 is not representable in the type, the casting will either truncate or round. Now suppose the literal is truncated, we cannot convert the expression to:
```sql
e < cast(1.9 as short)
```
as in the previous implementation, since if `e` is 1, the original expression evaluates to true, but converted expression will evaluate to false.
To resolve the above, this PR first finds out whether casting from the wider type to the narrower type will result to truncate or round, by comparing a _roundtrip value_ derived from **converting the literal first to the narrower type, and then to the wider type**, versus the original literal value. For instance, in the above, we'll first obtain a roundtrip value via the conversion (double) 1.9 -> (short) 1 -> (double) 1.0, and then compare it against 1.9.
<img width="1153" alt="Screen Shot 2020-09-28 at 3 30 27 PM" src="https://user-images.githubusercontent.com/506679/94492719-bd29e780-019f-11eb-9111-71d6e3d157f7.png">
Now in the case of truncate, we'd convert the original expression to:
```sql
e <= cast(1.9 as short)
```
instead, so that the conversion also is valid when `e` is 1.
For more details, please check [this blog post](https://prestosql.io/blog/2019/05/21/optimizing-the-casts-away.html) by Presto which offers a very good explanation on how it works.
### Why are the changes needed?
For queries such as:
```sql
SELECT * FROM tbl WHERE short_col < 100.5
```
The predicate `short_col < 100.5` can't be pushed down to data sources because it involves casts. This eliminates the cast so these queries can run more efficiently.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests
Closes#29792 from sunchao/SPARK-32858.
Lead-authored-by: Chao Sun <sunchao@apple.com>
Co-authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `And(IsNotNull(e), GreaterThan(Size(e), Literal(0)))` filter before Explode, PosExplode and Inline, when `outer = false`.
Removed unused `InferFiltersFromConstraints` from `operatorOptimizationRuleSet` to avoid confusion that happened during the review process.
### Why are the changes needed?
Predicate pushdown will be able to move this new filter down through joins and into data sources for performance improvement.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test
Closes#29092 from tanelk/SPARK-32295.
Lead-authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Co-authored-by: Tanel Kiis <tanel.kiis@reach-u.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR intends to correct version values (`3.0.0` -> `3.1.0`) of three configs below in `SQLConf`:
- spark.sql.planChangeLog.level
- spark.sql.planChangeLog.rules
- spark.sql.planChangeLog.batches
This PR comes from https://github.com/apache/spark/pull/29544#discussion_r503049350.
### Why are the changes needed?
Bugfix.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes#30015 from maropu/pr29544-FOLLOWUP.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
At the moment there is no possibility to turn off JDBC authentication providers which exists on the classpath. This can be problematic because service providers are loaded with service loader. In this PR I've added `spark.sql.sources.disabledJdbcConnProviderList` configuration possibility (default: empty).
### Why are the changes needed?
No possibility to turn off JDBC authentication providers.
### Does this PR introduce _any_ user-facing change?
Yes, it introduces new configuration option.
### How was this patch tested?
* Existing + newly added unit tests.
* Existing integration tests.
Closes#29964 from gaborgsomogyi/SPARK-32047.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to add `DataStreamWriter.table` to specify the output "table" to write from the streaming query.
### Why are the changes needed?
For now, there's no way to write to the table (especially catalog table) even the table is capable to handle streaming write, so even with Spark 3, writing to the catalog table via SS should go through the `DataStreamWriter.format(provider)` and wish the provider can handle it as same as we do with catalog table.
With the new API, we can directly point to the catalog table which supports streaming write. Some of usages are covered with tests - simply saying, end users can do the following:
```scala
// assuming `testcat` is a custom catalog, and `ns` is a namespace in the catalog
spark.sql("CREATE TABLE testcat.ns.table1 (id bigint, data string) USING foo")
val query = inputDF
.writeStream
.table("testcat.ns.table1")
.option(...)
.start()
```
### Does this PR introduce _any_ user-facing change?
Yes, as this adds a new public API in DataStreamWriter. This doesn't bring backward incompatible change.
### How was this patch tested?
New unit tests.
Closes#29767 from HeartSaVioR/SPARK-32896.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add distinct info at `UnresolvedFunction.toString`.
### Why are the changes needed?
Make `UnresolvedFunction` info complete.
```
create table test (c1 int, c2 int);
explain extended select sum(distinct c1) from test;
-- before this pr
== Parsed Logical Plan ==
'Project [unresolvedalias('sum('c1), None)]
+- 'UnresolvedRelation [test]
-- after this pr
== Parsed Logical Plan ==
'Project [unresolvedalias('sum(distinct 'c1), None)]
+- 'UnresolvedRelation [test]
```
### Does this PR introduce _any_ user-facing change?
Yes, get distinct info during sql parse.
### How was this patch tested?
manual test.
Closes#29586 from ulysses-you/SPARK-32743.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
1. Add new method to the `JdbcDialect` class - `classifyException()`. It converts dialect specific exception to Spark's `AnalysisException` or its sub-classes.
2. Replace H2 exception `org.h2.jdbc.JdbcSQLException` in `JDBCTableCatalogSuite` by `AnalysisException`.
3. Add `H2Dialect`
### Why are the changes needed?
Currently JDBC v2 Table Catalog implementation throws dialect specific exception and ignores exceptions defined in the `TableCatalog` interface. This PR adds new method for converting dialect specific exception, and assumes that follow up PRs will implement `classifyException()`.
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
By running existing test suites `JDBCTableCatalogSuite` and `JDBCV2Suite`.
Closes#29952 from MaxGekk/jdbcv2-classify-exception.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Adds a SQL function `raise_error` which underlies the refactored `assert_true` function. `assert_true` now also (optionally) accepts a custom error message field.
`raise_error` is exposed in SQL, Python, Scala, and R.
`assert_true` was previously only exposed in SQL; it is now also exposed in Python, Scala, and R.
### Why are the changes needed?
Improves usability of `assert_true` by clarifying error messaging, and adds the useful helper function `raise_error`.
### Does this PR introduce _any_ user-facing change?
Yes:
- Adds `raise_error` function to the SQL, Python, Scala, and R APIs.
- Adds `assert_true` function to the SQL, Python and R APIs.
### How was this patch tested?
Adds unit tests in SQL, Python, Scala, and R for `assert_true` and `raise_error`.
Closes#29947 from karenfeng/spark-32793.
Lead-authored-by: Karen Feng <karen.feng@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR intends to refactor code in `RewriteCorrelatedScalarSubquery` for replacing `ExprId`s in a bottom-up manner instead of doing in a top-down one.
This PR comes from the talk with cloud-fan in https://github.com/apache/spark/pull/29585#discussion_r490371252.
### Why are the changes needed?
To improve code.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#29913 from maropu/RefactorRewriteCorrelatedScalarSubquery.
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 migrate `DESCRIBE tbl colname` to use `UnresolvedTableOrView` to resolve the table/view identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
### Why are the changes needed?
The current behavior is not consistent between v1 and v2 commands when resolving a temp view.
In v2, the `t` in the following example is resolved to a table:
```scala
sql("CREATE TABLE testcat.ns.t (id bigint) USING foo")
sql("CREATE TEMPORARY VIEW t AS SELECT 2 as i")
sql("USE testcat.ns")
sql("DESCRIBE t i") // 't' is resolved to testcat.ns.t
Describing columns is not supported for v2 tables.;
org.apache.spark.sql.AnalysisException: Describing columns is not supported for v2 tables.;
```
whereas in v1, the `t` is resolved to a temp view:
```scala
sql("CREATE DATABASE test")
sql("CREATE TABLE spark_catalog.test.t (id bigint) USING csv")
sql("CREATE TEMPORARY VIEW t AS SELECT 2 as i")
sql("USE spark_catalog.test")
sql("DESCRIBE t i").show // 't' is resolved to a temp view
+---------+----------+
|info_name|info_value|
+---------+----------+
| col_name| i|
|data_type| int|
| comment| NULL|
+---------+----------+
```
### Does this PR introduce _any_ user-facing change?
After this PR, `DESCRIBE t i` is resolved to a temp view `t` instead of `testcat.ns.t`.
### How was this patch tested?
Added a new test
Closes#29880 from imback82/describe_column_consistent.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This proposes to simplify named_struct + get struct field + from_json expression chain from `struct(from_json.col1, from_json.col2, from_json.col3...)` to `struct(from_json)`.
### Why are the changes needed?
Simplify complex expression tree that could be produced by query optimization or user.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#29942 from viirya/SPARK-33007.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR fixes the broken build for Scala 2.13 with Maven.
https://github.com/apache/spark/pull/29913/checks?check_run_id=1187826966#29795 was merged though it doesn't successfully finish the build for Scala 2.13
### Why are the changes needed?
To fix the build.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
`build/mvn -Pscala-2.13 -Phive -Phive-thriftserver -DskipTests package`
Closes#29954 from sarutak/hotfix-seq.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
When we create a UDAF function use class extended `UserDefinedAggregeteFunction`, when we call the function, in support hive mode, in HiveSessionCatalog, it will call super.makeFunctionExpression,
but it will catch error such as the function need 2 parameter and we only give 1, throw exception only show
```
No handler for UDF/UDAF/UDTF xxxxxxxx
```
This is confused for develop , we should show error thrown by super method too,
For this pr's UT :
Before change, throw Exception like
```
No handler for UDF/UDAF/UDTF 'org.apache.spark.sql.hive.execution.LongProductSum'; line 1 pos 7
```
After this pr, throw exception
```
Spark UDAF Error: Invalid number of arguments for function longProductSum. Expected: 2; Found: 1;
Hive UDF/UDAF/UDTF Error: No handler for UDF/UDAF/UDTF 'org.apache.spark.sql.hive.execution.LongProductSum'; line 1 pos 7
```
### Why are the changes needed?
Show more detail error message when define UDAF
### Does this PR introduce _any_ user-facing change?
People will see more detail error message when use spark sql's UDAF in hive support Mode
### How was this patch tested?
Added UT
Closes#29054 from AngersZhuuuu/SPARK-32243.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Refactored `WithFields` Expression to make it more extensible (now `UpdateFields`).
2. Added a new `dropFields` method to the `Column` class. This method should allow users to drop a `StructField` in a `StructType` column (with similar semantics to the `drop` method on `Dataset`).
### Why are the changes needed?
Often Spark users have to work with deeply nested data e.g. to fix a data quality issue with an existing `StructField`. To do this with the existing Spark APIs, users have to rebuild the entire struct column.
For example, let's say you have the following deeply nested data structure which has a data quality issue (`5` is missing):
```
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val data = spark.createDataFrame(sc.parallelize(
Seq(Row(Row(Row(1, 2, 3), Row(Row(4, null, 6), Row(7, 8, 9), Row(10, 11, 12)), Row(13, 14, 15))))),
StructType(Seq(
StructField("a", StructType(Seq(
StructField("a", StructType(Seq(
StructField("a", IntegerType),
StructField("b", IntegerType),
StructField("c", IntegerType)))),
StructField("b", StructType(Seq(
StructField("a", StructType(Seq(
StructField("a", IntegerType),
StructField("b", IntegerType),
StructField("c", IntegerType)))),
StructField("b", StructType(Seq(
StructField("a", IntegerType),
StructField("b", IntegerType),
StructField("c", IntegerType)))),
StructField("c", StructType(Seq(
StructField("a", IntegerType),
StructField("b", IntegerType),
StructField("c", IntegerType))))
))),
StructField("c", StructType(Seq(
StructField("a", IntegerType),
StructField("b", IntegerType),
StructField("c", IntegerType))))
)))))).cache
data.show(false)
+---------------------------------+
|a |
+---------------------------------+
|[[1, 2, 3], [[4,, 6], [7, 8, 9]]]|
+---------------------------------+
```
Currently, to drop the missing value users would have to do something like this:
```
val result = data.withColumn("a",
struct(
$"a.a",
struct(
struct(
$"a.b.a.a",
$"a.b.a.c"
).as("a"),
$"a.b.b",
$"a.b.c"
).as("b"),
$"a.c"
))
result.show(false)
+---------------------------------------------------------------+
|a |
+---------------------------------------------------------------+
|[[1, 2, 3], [[4, 6], [7, 8, 9], [10, 11, 12]], [13, 14, 15]]|
+---------------------------------------------------------------+
```
As you can see above, with the existing methods users must call the `struct` function and list all fields, including fields they don't want to change. This is not ideal as:
>this leads to complex, fragile code that cannot survive schema evolution.
[SPARK-16483](https://issues.apache.org/jira/browse/SPARK-16483)
In contrast, with the method added in this PR, a user could simply do something like this to get the same result:
```
val result = data.withColumn("a", 'a.dropFields("b.a.b"))
result.show(false)
+---------------------------------------------------------------+
|a |
+---------------------------------------------------------------+
|[[1, 2, 3], [[4, 6], [7, 8, 9], [10, 11, 12]], [13, 14, 15]]|
+---------------------------------------------------------------+
```
This is the second of maybe 3 methods that could be added to the `Column` class to make it easier to manipulate nested data.
Other methods under discussion in [SPARK-22231](https://issues.apache.org/jira/browse/SPARK-22231) include `withFieldRenamed`.
However, this should be added in a separate PR.
### Does this PR introduce _any_ user-facing change?
The documentation for `Column.withField` method has changed to include an additional note about how to write optimized queries when adding multiple nested Column directly.
### How was this patch tested?
New unit tests were added. Jenkins must pass them.
### Related JIRAs:
More discussion on this topic can be found here:
- https://issues.apache.org/jira/browse/SPARK-22231
- https://issues.apache.org/jira/browse/SPARK-16483Closes#29795 from fqaiser94/SPARK-32511-dropFields-second-try.
Authored-by: fqaiser94@gmail.com <fqaiser94@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR intends to fix corner-case bugs in the `QueryPlan#transformUpWithNewOutput` that is used to propagate updated `ExprId`s in a bottom-up way. Let's say we have a rule to simply assign new `ExprId`s in a projection list like this;
```
case class TestRule extends Rule[LogicalPlan] {
override def apply(plan: LogicalPlan): LogicalPlan = plan.transformUpWithNewOutput {
case p Project(projList, _) =>
val newPlan = p.copy(projectList = projList.map { _.transform {
// Assigns a new `ExprId` for references
case a: AttributeReference => Alias(a, a.name)()
}}.asInstanceOf[Seq[NamedExpression]])
val attrMapping = p.output.zip(newPlan.output)
newPlan -> attrMapping
}
}
```
Then, this rule is applied into a plan below;
```
(3) Project [a#5, b#6]
+- (2) Project [a#5, b#6]
+- (1) Project [a#5, b#6]
+- LocalRelation <empty>, [a#5, b#6]
```
In the first transformation, the rule assigns new `ExprId`s in `(1) Project` (e.g., a#5 AS a#7, b#6 AS b#8). In the second transformation, the rule corrects the input references of `(2) Project` first by using attribute mapping given from `(1) Project` (a#5->a#7 and b#6->b#8) and then assigns new `ExprId`s (e.g., a#7 AS a#9, b#8 AS b#10). But, in the third transformation, the rule fails because it tries to correct the references of `(3) Project` by using incorrect attribute mapping (a#7->a#9 and b#8->b#10) even though the correct one is a#5->a#9 and b#6->b#10. To fix this issue, this PR modified the code to update the attribute mapping entries that are obsoleted by generated entries in a given rule.
### Why are the changes needed?
bugfix.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added tests in `QueryPlanSuite`.
Closes#29911 from maropu/QueryPlanBug.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add a test case to ensure changes to `spark.sql.optimizer.maxIterations` take effect at runtime.
### Why are the changes needed?
Currently, there is only one related test case: https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/internal/SQLConfSuite.scala#L156
However, this test case only checks the value of the conf can be changed at runtime. It does not check the updated value is actually used by the Optimizer.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
unit test
Closes#29919 from yuningzh-db/add_optimizer_test.
Authored-by: Yuning Zhang <yuning.zhang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch proposes to do column pruning for `JsonToStructs` expression if we only require some fields from it.
### Why are the changes needed?
`JsonToStructs` takes a schema parameter used to tell `JacksonParser` what fields are needed to parse. If `JsonToStructs` is followed by `GetStructField`. We can prune the schema to only parse certain field.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test
Closes#29900 from viirya/SPARK-32958.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
(This is a followup PR of #29585) The PR modified `RuleExecutor#isPlanIntegral` code for checking if a plan has globally-unique attribute IDs, but this check made Jenkins maven test jobs much longer (See [the Dongjoon comment](https://github.com/apache/spark/pull/29585#issuecomment-702461314) and thanks, dongjoon-hyun !). To recover running time for the Jenkins tests, this PR intends to update the code to run plan integrity check only for effective plans.
### Why are the changes needed?
To recover running time for Jenkins tests.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#29928 from maropu/PR29585-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 is to add support to decide bucketed table scan dynamically based on actual query plan. Currently bucketing is enabled by default (`spark.sql.sources.bucketing.enabled`=true), so for all bucketed tables in the query plan, we will use bucket table scan (all input files per the bucket will be read by same task). This has the drawback that if the bucket table scan is not benefitting at all (no join/groupby/etc in the query), we don't need to use bucket table scan as it would restrict the # of tasks to be # of buckets and might hurt parallelism.
The feature is to add a physical plan rule right after `EnsureRequirements`:
The rule goes through plan nodes. For all operators which has "interesting partition" (i.e., require `ClusteredDistribution` or `HashClusteredDistribution`), check if the sub-plan for operator has `Exchange` and bucketed table scan (and only allow certain operators in plan (i.e. `Scan/Filter/Project/Sort/PartialAgg/etc`.), see details in `DisableUnnecessaryBucketedScan.disableBucketWithInterestingPartition`). If yes, disable the bucketed table scan in the sub-plan. In addition, disabling bucketed table scan if there's operator with interesting partition along the sub-plan.
Why the algorithm works is that if there's a shuffle between the bucketed table scan and operator with interesting partition, then bucketed table scan partitioning will be destroyed by the shuffle operator in the middle, and we don't need bucketed table scan for sure.
The idea of "interesting partition" is inspired from "interesting order" in "Access Path Selection in a Relational Database Management System"(http://www.inf.ed.ac.uk/teaching/courses/adbs/AccessPath.pdf), after discussion with cloud-fan .
### Why are the changes needed?
To avoid unnecessary bucketed scan in the query, and this is prerequisite for https://github.com/apache/spark/pull/29625 (decide bucketed sorted scan dynamically will be added later in that PR).
### Does this PR introduce _any_ user-facing change?
A new config `spark.sql.sources.bucketing.autoBucketedScan.enabled` is introduced which set to false by default (the rule is disabled by default as it can regress cached bucketed table query, see discussion in https://github.com/apache/spark/pull/29804#issuecomment-701151447). User can opt-in/opt-out by enabling/disabling the config, as we found in prod, some users rely on assumption of # of tasks == # of buckets when reading bucket table to precisely control # of tasks. This is a bad assumption but it does happen on our side, so leave a config here to allow them opt-out for the feature.
### How was this patch tested?
Added unit tests in `DisableUnnecessaryBucketedScanSuite.scala`
Closes#29804 from c21/bucket-rule.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Add code in `ScalaReflection` to support scala enumeration and make enumeration type as string type in Spark.
### Why are the changes needed?
We support java enum but failed with scala enum, it's better to keep the same behavior.
Here is a example.
```
package test
object TestEnum extends Enumeration {
type TestEnum = Value
val E1, E2, E3 = Value
}
import TestEnum._
case class TestClass(i: Int, e: TestEnum) {
}
import test._
Seq(TestClass(1, TestEnum.E1)).toDS
```
Before this PR
```
Exception in thread "main" java.lang.UnsupportedOperationException: No Encoder found for test.TestEnum.TestEnum
- field (class: "scala.Enumeration.Value", name: "e")
- root class: "test.TestClass"
at org.apache.spark.sql.catalyst.ScalaReflection$.$anonfun$serializerFor$1(ScalaReflection.scala:567)
at scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:69)
at org.apache.spark.sql.catalyst.ScalaReflection.cleanUpReflectionObjects(ScalaReflection.scala:882)
at org.apache.spark.sql.catalyst.ScalaReflection.cleanUpReflectionObjects$(ScalaReflection.scala:881)
```
After this PR
`org.apache.spark.sql.Dataset[test.TestClass] = [i: int, e: string]`
### Does this PR introduce _any_ user-facing change?
Yes, user can make case class which include scala enumeration field as dataset.
### How was this patch tested?
Add test.
Closes#29403 from ulysses-you/SPARK-32585.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
### What changes were proposed in this pull request?
After `SPARK-32851` set `CODEGEN_FACTORY_MODE` to `CODEGEN_ONLY` of `sparkConf` in `SharedSparkSessionBase` to construction `SparkSession` in test, the test suite `SPARK-32459: UDF should not fail on WrappedArray` in s.sql.UDFSuite exposed a codegen fallback issue in Scala 2.13 as follow:
```
- SPARK-32459: UDF should not fail on WrappedArray *** FAILED ***
Caused by: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 47, Column 99: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 47, Column 99: No applicable constructor/method found for zero actual parameters; candidates are: "public scala.collection.mutable.Builder scala.collection.mutable.ArraySeq$.newBuilder(java.lang.Object)", "public scala.collection.mutable.Builder scala.collection.mutable.ArraySeq$.newBuilder(scala.reflect.ClassTag)", "public abstract scala.collection.mutable.Builder scala.collection.EvidenceIterableFactory.newBuilder(java.lang.Object)"
```
The root cause is `WrappedArray` represent `mutable.ArraySeq` in Scala 2.13 and has a different constructor of `newBuilder` method.
The main change of is pr is add Scala 2.13 only code part to deal with `case match WrappedArray` in Scala 2.13.
### Why are the changes needed?
We need to support a Scala 2.13 build
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Scala 2.12: Pass the Jenkins or GitHub Action
- Scala 2.13: All tests passed.
Do the following:
```
dev/change-scala-version.sh 2.13
mvn clean install -DskipTests -pl sql/core -Pscala-2.13 -am
mvn test -pl sql/core -Pscala-2.13
```
**Before**
```
Tests: succeeded 8540, failed 1, canceled 1, ignored 52, pending 0
*** 1 TEST FAILED ***
```
**After**
```
Tests: succeeded 8541, failed 0, canceled 1, ignored 52, pending 0
All tests passed.
```
Closes#29903 from LuciferYang/fix-udfsuite.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Some plan transformations (e.g., `RemoveNoopOperators`) implicitly assume the same `ExprId` refers to the unique attribute. But, `RuleExecutor` does not check this integrity between logical plan transformations. So, this PR intends to add this check in `isPlanIntegral` of `Analyzer`/`Optimizer`.
This PR comes from the talk with cloud-fan viirya in https://github.com/apache/spark/pull/29485#discussion_r475346278
### Why are the changes needed?
For better logical plan integrity checking.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#29585 from maropu/PlanIntegrityTest.
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 fix estimate statistics issue if child has 0 bytes.
### Why are the changes needed?
The `sizeInBytes` can be `0` when AQE and CBO are enabled(`spark.sql.adaptive.enabled`=true, `spark.sql.cbo.enabled`=true and `spark.sql.cbo.planStats.enabled`=true). This will generate incorrect BroadcastJoin, resulting in Driver OOM. For example:
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual test.
Closes#29894 from wangyum/SPARK-33018.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch proposes to optimize from_json + to_json expression chain.
### Why are the changes needed?
To optimize json expression chain that could be manually generated or generated automatically during query optimization.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#29828 from viirya/SPARK-32948.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Explicitly document that `current_date` and `current_timestamp` are executed at the start of query evaluation. And all calls of `current_date`/`current_timestamp` within the same query return the same value
### Why are the changes needed?
Users could expect that `current_date` and `current_timestamp` return the current date/timestamp at the moment of query execution but in fact the functions are folded by the optimizer at the start of query evaluation:
0df8dd6073/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/finishAnalysis.scala (L71-L91)
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running `./dev/scalastyle`.
Closes#29892 from MaxGekk/doc-current_date.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Compute the current date at the specified time zone using timestamp taken at the start of query evaluation.
### Why are the changes needed?
According to the doc for [current_date()](http://spark.apache.org/docs/latest/api/sql/#current_date), the current date should be computed at the start of query evaluation but it can be computed multiple times. As a consequence of that, the function can return different values if the query is executed at the border of two dates.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By existing test suites `ComputeCurrentTimeSuite` and `DateExpressionsSuite`.
Closes#29889 from MaxGekk/fix-current_date.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/29604 supports the ANSI SQL NTH_VALUE.
We should override the `prettyName` and `sql`.
### Why are the changes needed?
Make the name of nth_value correct.
To show the ignoreNulls parameter correctly.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#29886 from beliefer/improve-nth_value.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add canonicalization rules for commutative bitwise operations.
### Why are the changes needed?
Canonical form is used in many other optimization rules. Reduces the number of cases, where plans with identical results are considered to be distinct.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
UT
Closes#29794 from tanelk/SPARK-32927.
Lead-authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Co-authored-by: Tanel Kiis <tanel.kiis@reach-u.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Use `Utils.getSimpleName` to avoid hitting `Malformed class name` error in `TreeNode`.
### Why are the changes needed?
On older JDK versions (e.g. JDK8u), nested Scala classes may trigger `java.lang.Class.getSimpleName` to throw an `java.lang.InternalError: Malformed class name` error.
Similar to https://github.com/apache/spark/pull/29050, we should use Spark's `Utils.getSimpleName` utility function in place of `Class.getSimpleName` to avoid hitting the issue.
### Does this PR introduce _any_ user-facing change?
Fixes a bug that throws an error when invoking `TreeNode.nodeName`, otherwise no changes.
### How was this patch tested?
Added new unit test case in `TreeNodeSuite`. Note that the test case assumes the test code can trigger the expected error, otherwise it'll skip the test safely, for compatibility with newer JDKs.
Manually tested on JDK8u and JDK11u and observed expected behavior:
- JDK8u: the test case triggers the "Malformed class name" issue and the fix works;
- JDK11u: the test case does not trigger the "Malformed class name" issue, and the test case is safely skipped.
Closes#29875 from rednaxelafx/spark-32999-getsimplename.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Unevaluable expressions are not foldable because we don't have an eval for it. This PR is to clean up the code and enforce it.
### Why are the changes needed?
Ensure that we will not hit the weird cases that trigger ConstantFolding.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
The existing tests.
Closes#29798 from gatorsmile/refactorUneval.
Lead-authored-by: gatorsmile <gatorsmile@gmail.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr aims to add a new `table` API in DataStreamReader, which is similar to the table API in DataFrameReader.
### Why are the changes needed?
Users can directly use this API to get a Streaming DataFrame on a table. Below is a simple example:
Application 1 for initializing and starting the streaming job:
```
val path = "/home/yuanjian.li/runtime/to_be_deleted"
val tblName = "my_table"
// Write some data to `my_table`
spark.range(3).write.format("parquet").option("path", path).saveAsTable(tblName)
// Read the table as a streaming source, write result to destination directory
val table = spark.readStream.table(tblName)
table.writeStream.format("parquet").option("checkpointLocation", "/home/yuanjian.li/runtime/to_be_deleted_ck").start("/home/yuanjian.li/runtime/to_be_deleted_2")
```
Application 2 for appending new data:
```
// Append new data into the path
spark.range(5).write.format("parquet").option("path", "/home/yuanjian.li/runtime/to_be_deleted").mode("append").save()
```
Check result:
```
// The desitination directory should contains all written data
spark.read.parquet("/home/yuanjian.li/runtime/to_be_deleted_2").show()
```
### Does this PR introduce _any_ user-facing change?
Yes, a new API added.
### How was this patch tested?
New UT added and integrated testing.
Closes#29756 from xuanyuanking/SPARK-32885.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to migrate `REFRESH TABLE` to use `UnresolvedTableOrView` to resolve the table/view identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
### Why are the changes needed?
The current behavior is not consistent between v1 and v2 commands when resolving a temp view.
In v2, the `t` in the following example is resolved to a table:
```scala
sql("CREATE TABLE testcat.ns.t (id bigint) USING foo")
sql("CREATE TEMPORARY VIEW t AS SELECT 2")
sql("USE testcat.ns")
sql("REFRESH TABLE t") // 't' is resolved to testcat.ns.t
```
whereas in v1, the `t` is resolved to a temp view:
```scala
sql("CREATE DATABASE test")
sql("CREATE TABLE spark_catalog.test.t (id bigint) USING csv")
sql("CREATE TEMPORARY VIEW t AS SELECT 2")
sql("USE spark_catalog.test")
sql("REFRESH TABLE t") // 't' is resolved to a temp view
```
### Does this PR introduce _any_ user-facing change?
After this PR, `REFRESH TABLE t` is resolved to a temp view `t` instead of `testcat.ns.t`.
### How was this patch tested?
Added a new test
Closes#29866 from imback82/refresh_table_consistent.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
MurmurHash3 and xxHash64 interpret sequences of bytes as integers
encoded in little-endian byte order. This requires a byte reversal
on big endian platforms.
I've left the hashInt and hashLong functions as-is for now. My
interpretation of these functions is that they perform the hash on
the integer value as if it were serialized in little-endian byte
order. Therefore no byte reversal is necessary.
### What changes were proposed in this pull request?
Modify hash functions to produce correct results on big-endian platforms.
### Why are the changes needed?
Hash functions produce incorrect results on big-endian platforms which, amongst other potential issues, causes test failures.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests run on the IBM Z (s390x) platform which uses a big-endian byte order.
Closes#29762 from mundaym/fix-hashes.
Authored-by: Michael Munday <mike.munday@ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
In the PR, I propose to replace current examples for `percentile_approx()` with **only one** input value by example **with multiple values** in the input column.
### Why are the changes needed?
Current examples are pretty trivial, and don't demonstrate function's behaviour on a sequence of values.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- by running `ExpressionInfoSuite`
- `./dev/scalastyle`
Closes#29841 from MaxGekk/example-percentile_approx.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
More precise description of the result of the `percentile_approx()` function and its synonym `approx_percentile()`. The proposed sentence clarifies that the function returns **one of elements** (or array of elements) from the input column.
### Why are the changes needed?
To improve Spark docs and avoid misunderstanding of the function behavior.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
`./dev/scalastyle`
Closes#29835 from MaxGekk/doc-percentile_approx.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR adds foldable propagation from `Aggregate` as per: https://github.com/apache/spark/pull/29771#discussion_r490412031
### Why are the changes needed?
This is an improvement as `Aggregate`'s `aggregateExpressions` can contain foldables that can be propagated up.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New UT.
Closes#29816 from peter-toth/SPARK-32951-foldable-propagation-from-aggregate.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In current mode, when explain a SQL plan with HiveTableRelation, it will show so many info about HiveTableRelation's prunedPartition, this make plan hard to read, this pr make this information simpler.
Before:
For UT
```
test("Make HiveTableScanExec message simple") {
withSQLConf("hive.exec.dynamic.partition.mode" -> "nonstrict") {
withTable("df") {
spark.range(30)
.select(col("id"), col("id").as("k"))
.write
.partitionBy("k")
.format("hive")
.mode("overwrite")
.saveAsTable("df")
val df = sql("SELECT df.id, df.k FROM df WHERE df.k < 2")
df.explain(true)
}
}
}
```
After this pr will show
```
== Parsed Logical Plan ==
'Project ['df.id, 'df.k]
+- 'Filter ('df.k < 2)
+- 'UnresolvedRelation [df], []
== Analyzed Logical Plan ==
id: bigint, k: bigint
Project [id#11L, k#12L]
+- Filter (k#12L < cast(2 as bigint))
+- SubqueryAlias spark_catalog.default.df
+- HiveTableRelation [`default`.`df`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Data Cols: [id#11L], Partition Cols: [k#12L]]
== Optimized Logical Plan ==
Filter (isnotnull(k#12L) AND (k#12L < 2))
+- HiveTableRelation [`default`.`df`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Data Cols: [id#11L], Partition Cols: [k#12L], Pruned Partitions: [(k=0), (k=1)]]
== Physical Plan ==
Scan hive default.df [id#11L, k#12L], HiveTableRelation [`default`.`df`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Data Cols: [id#11L], Partition Cols: [k#12L], Pruned Partitions: [(k=0), (k=1)]], [isnotnull(k#12L), (k#12L < 2)]
```
In my pr, I will construct `HiveTableRelation`'s `simpleString` method to avoid show too much unnecessary info in explain plan. compared to what we had before，I decrease the detail metadata of each partition and only retain the partSpec to show each partition was pruned. Since for detail information, we always don't see this in Plan but to use DESC EXTENDED statement.
### Why are the changes needed?
Make plan about HiveTableRelation more readable
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No
Closes#29739 from AngersZhuuuu/HiveTableScan-meta-location-info.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a follow-up PR to https://github.com/apache/spark/pull/29771 and just adds a new test case.
### Why are the changes needed?
To have better test coverage.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New UT.
Closes#29802 from peter-toth/SPARK-32635-fix-foldable-propagation-followup.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
After https://github.com/apache/spark/pull/29660 and https://github.com/apache/spark/pull/29689 there are 13 remaining failed cases of sql core module with Scala 2.13.
The reason for the remaining failed cases is the optimization result of `CostBasedJoinReorder` maybe different with same input in Scala 2.12 and Scala 2.13 if there are more than one same cost candidate plans.
In this pr give a way to make the optimization result deterministic as much as possible to pass all remaining failed cases of `sql/core` module in Scala 2.13, the main change of this pr as follow:
- Change to use `LinkedHashMap` instead of `Map` to store `foundPlans` in `JoinReorderDP.search` method to ensure same iteration order with same insert order because iteration order of `Map` behave differently under Scala 2.12 and 2.13
- Fixed `StarJoinCostBasedReorderSuite` affected by the above change
- Regenerate golden files affected by the above change.
### Why are the changes needed?
We need to support a Scala 2.13 build.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Scala 2.12: Pass the Jenkins or GitHub Action
- Scala 2.13: All tests passed.
Do the following:
```
dev/change-scala-version.sh 2.13
mvn clean install -DskipTests -pl sql/core -Pscala-2.13 -am
mvn test -pl sql/core -Pscala-2.13
```
**Before**
```
Tests: succeeded 8485, failed 13, canceled 1, ignored 52, pending 0
*** 13 TESTS FAILED ***
```
**After**
```
Tests: succeeded 8498, failed 0, canceled 1, ignored 52, pending 0
All tests passed.
```
Closes#29711 from LuciferYang/SPARK-32808-3.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
1. Change the target error calculation according to the paper [Space-Efficient Online Computation of Quantile Summaries](http://infolab.stanford.edu/~datar/courses/cs361a/papers/quantiles.pdf). It says that the error `e = max(gi, deltai)/2` (see the page 59). Also this has clear explanation [ε-approximate quantiles](http://www.mathcs.emory.edu/~cheung/Courses/584/Syllabus/08-Quantile/Greenwald.html#proofprop1).
2. Added a test to check different accuracies.
3. Added an input CSV file `percentile_approx-input.csv.bz2` to the resource folder `sql/catalyst/src/main/resources` for the test.
### Why are the changes needed?
To fix incorrect percentile calculation, see an example in SPARK-32908.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
- By running existing tests in `QuantileSummariesSuite` and in `ApproximatePercentileQuerySuite`.
- Added new test `SPARK-32908: maximum target error in percentile_approx` to `ApproximatePercentileQuerySuite`.
Closes#29784 from MaxGekk/fix-percentile_approx-2.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR rewrites `FoldablePropagation` rule to replace attribute references in a node with foldables coming only from the node's children.
Before this PR in the case of this example (with setting`spark.sql.optimizer.excludedRules=org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation`):
```scala
val a = Seq("1").toDF("col1").withColumn("col2", lit("1"))
val b = Seq("2").toDF("col1").withColumn("col2", lit("2"))
val aub = a.union(b)
val c = aub.filter($"col1" === "2").cache()
val d = Seq("2").toDF( "col4")
val r = d.join(aub, $"col2" === $"col4").select("col4")
val l = c.select("col2")
val df = l.join(r, $"col2" === $"col4", "LeftOuter")
df.show()
```
foldable propagation happens incorrectly:
```
Join LeftOuter, (col2#6 = col4#34) Join LeftOuter, (col2#6 = col4#34)
!:- Project [col2#6] :- Project [1 AS col2#6]
: +- InMemoryRelation [col1#4, col2#6], StorageLevel(disk, memory, deserialized, 1 replicas) : +- InMemoryRelation [col1#4, col2#6], StorageLevel(disk, memory, deserialized, 1 replicas)
: +- Union : +- Union
: :- *(1) Project [value#1 AS col1#4, 1 AS col2#6] : :- *(1) Project [value#1 AS col1#4, 1 AS col2#6]
: : +- *(1) Filter (isnotnull(value#1) AND (value#1 = 2)) : : +- *(1) Filter (isnotnull(value#1) AND (value#1 = 2))
: : +- *(1) LocalTableScan [value#1] : : +- *(1) LocalTableScan [value#1]
: +- *(2) Project [value#10 AS col1#13, 2 AS col2#15] : +- *(2) Project [value#10 AS col1#13, 2 AS col2#15]
: +- *(2) Filter (isnotnull(value#10) AND (value#10 = 2)) : +- *(2) Filter (isnotnull(value#10) AND (value#10 = 2))
: +- *(2) LocalTableScan [value#10] : +- *(2) LocalTableScan [value#10]
+- Project [col4#34] +- Project [col4#34]
+- Join Inner, (col2#6 = col4#34) +- Join Inner, (col2#6 = col4#34)
:- Project [value#31 AS col4#34] :- Project [value#31 AS col4#34]
: +- LocalRelation [value#31] : +- LocalRelation [value#31]
+- Project [col2#6] +- Project [col2#6]
+- Union false, false +- Union false, false
:- Project [1 AS col2#6] :- Project [1 AS col2#6]
: +- LocalRelation [value#1] : +- LocalRelation [value#1]
+- Project [2 AS col2#15] +- Project [2 AS col2#15]
+- LocalRelation [value#10] +- LocalRelation [value#10]
```
and so the result is wrong:
```
+----+----+
|col2|col4|
+----+----+
| 1|null|
+----+----+
```
After this PR foldable propagation will not happen incorrectly and the result is correct:
```
+----+----+
|col2|col4|
+----+----+
| 2| 2|
+----+----+
```
### Why are the changes needed?
To fix a correctness issue.
### Does this PR introduce _any_ user-facing change?
Yes, fixes a correctness issue.
### How was this patch tested?
Existing and new UTs.
Closes#29771 from peter-toth/SPARK-32635-fix-foldable-propagation.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This is a follow-up on #29565, and addresses a few issues in the last PR:
- style issue pointed by [this comment](https://github.com/apache/spark/pull/29565#discussion_r487646749)
- skip optimization when `fromExp` is foldable (by [this comment](https://github.com/apache/spark/pull/29565#discussion_r487646973)) as there could be more efficient rule to apply for this case.
- pass timezone info to the generated cast on the literal value
- a bunch of cleanups and test improvements
Originally I plan to handle this when implementing [SPARK-32858](https://issues.apache.org/jira/browse/SPARK-32858) but now think it's better to isolate these changes from that.
### Why are the changes needed?
To fix a few left over issues in the above PR.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added a test for the foldable case. Otherwise relying on existing tests.
Closes#29775 from sunchao/SPARK-24994-followup.
Authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch proposes to make GeneratePredicate eliminate common sub-expressions.
### Why are the changes needed?
Both GenerateMutableProjection and GenerateUnsafeProjection, such codegen objects can eliminate common sub-expressions. But GeneratePredicate currently doesn't do it.
We encounter a customer issue that a Filter pushed down through a Project causes performance issue, compared with not pushed down case. The issue is one expression used in Filter predicates are run many times. Due to the complex schema, the query nodes are not wholestage codegen, so it runs Filter.doExecute and then call GeneratePredicate. The common expression was run many time and became performance bottleneck. GeneratePredicate should be able to eliminate common sub-expressions for such case.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests.
Closes#29776 from viirya/filter-pushdown.
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 fixes a conflict between `RewriteDistinctAggregates` and `DecimalAggregates`.
In some cases, `DecimalAggregates` will wrap the decimal column to `UnscaledValue` using
different rules for different aggregates.
This means, same distinct column with different aggregates will change to different distinct columns
after `DecimalAggregates`. For example:
`avg(distinct decimal_col), sum(distinct decimal_col)` may change to
`avg(distinct UnscaledValue(decimal_col)), sum(distinct decimal_col)`
We assume after `RewriteDistinctAggregates`, there will be at most one distinct column in aggregates,
but `DecimalAggregates` breaks this assumption. To fix this, we have to switch the order of these two
rules.
### Why are the changes needed?
bug fix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
added test cases
Closes#29673 from linhongliu-db/SPARK-32816.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr makes cast string type to decimal decimal type fast fail if precision larger that 38.
### Why are the changes needed?
It is very slow if precision very large.
Benchmark and benchmark result:
```scala
import org.apache.spark.benchmark.Benchmark
val bd1 = new java.math.BigDecimal("6.0790316E+25569151")
val bd2 = new java.math.BigDecimal("6.0790316E+25");
val benchmark = new Benchmark("Benchmark string to decimal", 1, minNumIters = 2)
benchmark.addCase(bd1.toString) { _ =>
println(Decimal(bd1).precision)
}
benchmark.addCase(bd2.toString) { _ =>
println(Decimal(bd2).precision)
}
benchmark.run()
```
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.15.6
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Benchmark string to decimal: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
6.0790316E+25569151 9340 9381 57 0.0 9340094625.0 1.0X
6.0790316E+25 0 0 0 0.5 2150.0 4344230.1X
```
Stacktrace:
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test and benchmark test:
Dataset | Before this pr (Seconds) | After this pr (Seconds)
-- | -- | --
https://issues.apache.org/jira/secure/attachment/13011406/part-00000.parquet | 2640 | 2
Closes#29731 from wangyum/SPARK-32706.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The `LiteralGenerator` for float and double datatypes was supposed to yield special values (NaN, +-inf) among others, but the `Gen.chooseNum` method does not yield values that are outside the defined range. The `Gen.chooseNum` for a wide range of floats and doubles does not yield values in the "everyday" range as stated in https://github.com/typelevel/scalacheck/issues/113 .
There is an similar class `RandomDataGenerator` that is used in some other tests. Added `-0.0` and `-0.0f` as special values to there too.
These changes revealed an inconsistency with the equality check between `-0.0` and `0.0`.
### Why are the changes needed?
The `LiteralGenerator` is mostly used in the `checkConsistencyBetweenInterpretedAndCodegen` method in `MathExpressionsSuite`. This change would have caught the bug fixed in #29495 .
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Locally reverted #29495 and verified that the existing test cases caught the bug.
Closes#29515 from tanelk/SPARK-32688.
Authored-by: Tanel Kiis <tanel.kiis@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Add a new config `spark.sql.maxMetadataStringLength`. This config aims to limit metadata value length, e.g. file location.
### Why are the changes needed?
Some metadata have been abbreviated by `...` when I tried to add some test in `SQLQueryTestSuite`. We need to replace such value to `notIncludedMsg`. That caused we can't replace that like location value by `className` since the `className` has been abbreviated.
Here is a case:
```
CREATE table explain_temp1 (key int, val int) USING PARQUET;
EXPLAIN EXTENDED SELECT sum(distinct val) FROM explain_temp1;
-- ignore parsed,analyzed,optimized
-- The output like
== Physical Plan ==
*HashAggregate(keys=[], functions=[sum(distinct cast(val#x as bigint)#xL)], output=[sum(DISTINCT val)#xL])
+- Exchange SinglePartition, true, [id=#x]
+- *HashAggregate(keys=[], functions=[partial_sum(distinct cast(val#x as bigint)#xL)], output=[sum#xL])
+- *HashAggregate(keys=[cast(val#x as bigint)#xL], functions=[], output=[cast(val#x as bigint)#xL])
+- Exchange hashpartitioning(cast(val#x as bigint)#xL, 4), true, [id=#x]
+- *HashAggregate(keys=[cast(val#x as bigint) AS cast(val#x as bigint)#xL], functions=[], output=[cast(val#x as bigint)#xL])
+- *ColumnarToRow
+- FileScan parquet default.explain_temp1[val#x] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/home/runner/work/spark/spark/sql/core/spark-warehouse/org.apache.spark.sq...], PartitionFilters: ...
```
### Does this PR introduce _any_ user-facing change?
No, a new config.
### How was this patch tested?
new test.
Closes#29688 from ulysses-you/SPARK-32827.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Mark `BitAggregate` as order irrelevant in `EliminateSorts`.
### Why are the changes needed?
Performance improvements in some queries
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Generalized an existing UT
Closes#29740 from tanelk/SPARK-32868.
Authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Make `DataFrameReader.table` take the specified options for datasource v1.
### Why are the changes needed?
Keep the same behavior of v1/v2 datasource, the v2 fix has been done in SPARK-32592.
### Does this PR introduce _any_ user-facing change?
Yes. The DataFrameReader.table will take the specified options. Also, if there are the same key and value exists in specified options and table properties, an exception will be thrown.
### How was this patch tested?
New UT added.
Closes#29712 from xuanyuanking/SPARK-32844.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, in cases like the following:
```sql
SELECT * FROM t WHERE age < 40
```
where `age` is of short type, Spark won't be able to simplify this and can only generate filter `cast(age, int) < 40`. This won't get pushed down to datasources and therefore is not optimized.
This PR proposes a optimizer rule to improve this when the following constraints are satisfied:
- input expression is binary comparisons when one side is a cast operation and another is a literal.
- both the cast child expression and literal are of integral type (i.e., byte, short, int or long)
When this is true, it tries to do several optimizations to either simplify the expression or move the cast to the literal side, so
result filter for the above case becomes `age < cast(40 as smallint)`. This is better since the cast can be optimized away later and the filter can be pushed down to data sources.
This PR follows a similar effort in Presto (https://prestosql.io/blog/2019/05/21/optimizing-the-casts-away.html). Here we only handles integral types but plan to extend to other types as follow-ups.
### Why are the changes needed?
As mentioned in the previous section, when cast is not optimized, it cannot be pushed down to data sources which can lead
to unnecessary IO and therefore longer job time and waste of resources. This helps to improve that.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit tests for both the optimizer rule and filter pushdown on datasource level for both Orc and Parquet.
Closes#29565 from sunchao/SPARK-24994.
Authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This is an attempt to adapt Spark REPL to Scala 2.13.
It is based on a [scala-2.13 branch](https://github.com/smarter/spark/tree/scala-2.13) made by smarter.
I had to set Scala version to 2.13 in some places, and to adapt some other modules, before I could start working on the REPL itself. These are separate commits on the branch that probably would be fixed beforehand, and thus dropped before the merge of this PR.
I couldn't find a way to run the initialization code with existing REPL classes in Scala 2.13.2, so I [modified REPL in Scala](e9cc0dd547) to make it work. With this modification I managed to run Spark Shell, along with the units tests passing, which is good news.
The bad news is that it requires an upstream change in Scala, which must be accepted first. I'd be happy to change it if someone points a way to do it differently. If not, I'd propose a PR in Scala to introduce `ILoop.internalReplAutorunCode`.
### Why are the changes needed?
REPL in Scala changed quite a lot, so current version of Spark REPL needed to be adapted.
### Does this PR introduce _any_ user-facing change?
In the previous version of `SparkILoop`, a lot of Scala's `ILoop` code was [overridden and duplicated](2bc7b75537) to make the welcome message a bit more pleasant. In this PR, the message is in a bit different order, but it's still acceptable IMHO.
Before this PR:
```
20/05/15 15:32:39 WARN Utils: Your hostname, hermes resolves to a loopback address: 127.0.1.1; using 192.168.1.28 instead (on interface enp0s31f6)
20/05/15 15:32:39 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
20/05/15 15:32:39 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).
20/05/15 15:32:45 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
Spark context Web UI available at http://192.168.1.28:4041
Spark context available as 'sc' (master = local[*], app id = local-1589549565502).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.0.1-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_242)
Type in expressions to have them evaluated.
Type :help for more information.
scala>
```
With this PR:
```
20/05/15 15:32:15 WARN Utils: Your hostname, hermes resolves to a loopback address: 127.0.1.1; using 192.168.1.28 instead (on interface enp0s31f6)
20/05/15 15:32:15 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
20/05/15 15:32:15 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).
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.0.0-SNAPSHOT
/_/
Using Scala version 2.13.2-20200422-211118-706ef1b (OpenJDK 64-Bit Server VM, Java 1.8.0_242)
Type in expressions to have them evaluated.
Type :help for more information.
Spark context Web UI available at http://192.168.1.28:4040
Spark context available as 'sc' (master = local[*], app id = local-1589549541259).
Spark session available as 'spark'.
scala>
```
It seems that currently the welcoming message is still an improvement from [the original ticket](https://issues.apache.org/jira/browse/SPARK-24785), albeit in a different order. As a bonus, some fragile code duplication was removed.
### How was this patch tested?
Existing tests pass in `repl`module. The REPL runs in a terminal and the following code executed correctly:
```
scala> spark.range(1000 * 1000 * 1000).count()
val res0: Long = 1000000000
```
Closes#28545 from karolchmist/scala-2.13-repl.
Authored-by: Karol Chmist <info+github@chmist.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
fix typo in SQLConf
### Why are the changes needed?
typo fix to increase readability
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
no test
Closes#29668 from Ted-Jiang/fix_annotate.
Authored-by: yangjiang <yangjiang@ebay.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Spark SQL exists a bug show below:
```
spark.sql(
" SELECT COUNT(DISTINCT 2), COUNT(DISTINCT 2, 3)")
.show()
+-----------------+--------------------+
|count(DISTINCT 2)|count(DISTINCT 2, 3)|
+-----------------+--------------------+
| 1| 1|
+-----------------+--------------------+
spark.sql(
" SELECT COUNT(DISTINCT 2), COUNT(DISTINCT 3, 2)")
.show()
+-----------------+--------------------+
|count(DISTINCT 2)|count(DISTINCT 3, 2)|
+-----------------+--------------------+
| 1| 0|
+-----------------+--------------------+
```
The first query is correct, but the second query is not.
The root reason is the second query rewrited by `RewriteDistinctAggregates` who expand the output but lost the 2.
### Why are the changes needed?
Fix a bug.
`SELECT COUNT(DISTINCT 2), COUNT(DISTINCT 3, 2)` should return `1, 1`
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
New UT
Closes#29626 from beliefer/support-multiple-foldable-distinct-expressions.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In this PR, we add a checker for STRING form interval value ahead for parsing multiple units intervals and fail directly if the interval value contains alphabets to prevent correctness issues like `interval '1 day 2' day`=`3 days`.
### Why are the changes needed?
fix correctness issue
### Does this PR introduce _any_ user-facing change?
yes, in spark 3.0.0 `interval '1 day 2' day`=`3 days` but now we fail with ParseException
### How was this patch tested?
add a test.
Closes#29708 from yaooqinn/SPARK-32840.
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 intends to fix an existing bug below in `UserDefinedTypeSuite`;
```
[info] - SPARK-19311: UDFs disregard UDT type hierarchy (931 milliseconds)
16:22:35.936 WARN org.apache.spark.sql.catalyst.expressions.SafeProjection: Expr codegen error and falling back to interpreter mode
org.apache.spark.SparkException: Cannot cast org.apache.spark.sql.ExampleSubTypeUDT46b1771f to org.apache.spark.sql.ExampleBaseTypeUDT31e8d979.
at org.apache.spark.sql.catalyst.expressions.CastBase.nullSafeCastFunction(Cast.scala:891)
at org.apache.spark.sql.catalyst.expressions.CastBase.doGenCode(Cast.scala:852)
at org.apache.spark.sql.catalyst.expressions.Expression.$anonfun$genCode$3(Expression.scala:147)
...
```
### Why are the changes needed?
bugfix
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit tests.
Closes#29691 from maropu/FixUdtBug.
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 renames `SupportsStreamingUpdate` to `SupportsStreamingUpdateAsAppend` as the new interface name represents the actual behavior clearer. This PR also removes the `update()` method (so the interface is more likely a marker), as the implementations of `SupportsStreamingUpdateAsAppend` should support append mode by default, hence no need to trigger some flag on it.
### Why are the changes needed?
SupportsStreamingUpdate was intended to revive the functionality of Streaming update output mode for internal data sources, but despite the name, that interface isn't really used to do actual update on sink; all sinks are implementing this interface to do append, so strictly saying, it's just to support update as append. Renaming the interface would make it clear.
### Does this PR introduce _any_ user-facing change?
No, as the class is only for internal data sources.
### How was this patch tested?
Jenkins test will follow.
Closes#29693 from HeartSaVioR/SPARK-32831.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Upgrade Apache Arrow to version 1.0.1 for the Java dependency and increase minimum version of PyArrow to 1.0.0.
This release marks a transition to binary stability of the columnar format (which was already informally backward-compatible going back to December 2017) and a transition to Semantic Versioning for the Arrow software libraries. Also note that the Java arrow-memory artifact has been split to separate dependence on netty-buffer and allow users to select an allocator. Spark will continue to use `arrow-memory-netty` to maintain performance benefits.
Version 1.0.0 - 1.0.0 include the following selected fixes/improvements relevant to Spark users:
ARROW-9300 - [Java] Separate Netty Memory to its own module
ARROW-9272 - [C++][Python] Reduce complexity in python to arrow conversion
ARROW-9016 - [Java] Remove direct references to Netty/Unsafe Allocators
ARROW-8664 - [Java] Add skip null check to all Vector types
ARROW-8485 - [Integration][Java] Implement extension types integration
ARROW-8434 - [C++] Ipc RecordBatchFileReader deserializes the Schema multiple times
ARROW-8314 - [Python] Provide a method to select a subset of columns of a Table
ARROW-8230 - [Java] Move Netty memory manager into a separate module
ARROW-8229 - [Java] Move ArrowBuf into the Arrow package
ARROW-7955 - [Java] Support large buffer for file/stream IPC
ARROW-7831 - [Java] unnecessary buffer allocation when calling splitAndTransferTo on variable width vectors
ARROW-6111 - [Java] Support LargeVarChar and LargeBinary types and add integration test with C++
ARROW-6110 - [Java] Support LargeList Type and add integration test with C++
ARROW-5760 - [C++] Optimize Take implementation
ARROW-300 - [Format] Add body buffer compression option to IPC message protocol using LZ4 or ZSTD
ARROW-9098 - RecordBatch::ToStructArray cannot handle record batches with 0 column
ARROW-9066 - [Python] Raise correct error in isnull()
ARROW-9223 - [Python] Fix to_pandas() export for timestamps within structs
ARROW-9195 - [Java] Wrong usage of Unsafe.get from bytearray in ByteFunctionsHelper class
ARROW-7610 - [Java] Finish support for 64 bit int allocations
ARROW-8115 - [Python] Conversion when mixing NaT and datetime objects not working
ARROW-8392 - [Java] Fix overflow related corner cases for vector value comparison
ARROW-8537 - [C++] Performance regression from ARROW-8523
ARROW-8803 - [Java] Row count should be set before loading buffers in VectorLoader
ARROW-8911 - [C++] Slicing a ChunkedArray with zero chunks segfaults
View release notes here:
https://arrow.apache.org/release/1.0.1.htmlhttps://arrow.apache.org/release/1.0.0.html
### Why are the changes needed?
Upgrade brings fixes, improvements and stability guarantees.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests with pyarrow 1.0.0 and 1.0.1
Closes#29686 from BryanCutler/arrow-upgrade-100-SPARK-32312.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch proposes to check `ignoreNullability` parameter recursively in `equalsStructurally` method.
### Why are the changes needed?
`equalsStructurally` is used to check type equality. We can optionally ask to ignore nullability check. But the parameter `ignoreNullability` is not passed recursively down to nested types. So it produces weird error like:
```
data type mismatch: argument 3 requires array<array<string>> type, however ... is of array<array<string>> type.
```
when running the query `select aggregate(split('abcdefgh',''), array(array('')), (acc, x) -> array(array( x ) ) )`.
### Does this PR introduce _any_ user-facing change?
Yes, fixed a bug when running user query.
### How was this patch tested?
Unit tests.
Closes#29698 from viirya/SPARK-32819.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`--` method of `AttributeSet` behave differently under Scala 2.12 and 2.13 because `--` method of `LinkedHashSet` in Scala 2.13 can't maintains the insertion order.
This pr use a Scala 2.12 based code to ensure `--` method of AttributeSet have same behavior under Scala 2.12 and 2.13.
### Why are the changes needed?
The behavior of `AttributeSet` needs to be compatible with Scala 2.12 and 2.13
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Scala 2.12: Pass the Jenkins or GitHub Action
Scala 2.13: Manual test sub-suites of `PlanStabilitySuite`
- **Before** ：293 TESTS FAILED
- **After**：13 TESTS FAILED(The remaining failures are not associated with the current issue)
Closes#29689 from LuciferYang/SPARK-32755-FOLLOWUP.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The purpose of this pr is to partial resolve [SPARK-32808](https://issues.apache.org/jira/browse/SPARK-32808), total of 26 failed test cases were fixed, the related suite as follow:
- `StreamingAggregationSuite` related test cases (2 FAILED -> Pass)
- `GeneratorFunctionSuite` related test cases (2 FAILED -> Pass)
- `UDFSuite` related test cases (2 FAILED -> Pass)
- `SQLQueryTestSuite` related test cases (5 FAILED -> Pass)
- `WholeStageCodegenSuite` related test cases (1 FAILED -> Pass)
- `DataFrameSuite` related test cases (3 FAILED -> Pass)
- `OrcV1QuerySuite\OrcV2QuerySuite` related test cases (4 FAILED -> Pass)
- `ExpressionsSchemaSuite` related test cases (1 FAILED -> Pass)
- `DataFrameStatSuite` related test cases (1 FAILED -> Pass)
- `JsonV1Suite\JsonV2Suite\JsonLegacyTimeParserSuite` related test cases (6 FAILED -> Pass)
The main change of this pr as following:
- Fix Scala 2.13 compilation problems in `ShuffleBlockFetcherIterator` and `Analyzer`
- Specified `Seq` to `scala.collection.Seq` in `objects.scala` and `GenericArrayData` because internal use `Seq` maybe `mutable.ArraySeq` and not easy to call `.toSeq`
- Should specified `Seq` to `scala.collection.Seq` when we call `Row.getAs[Seq]` and `Row.get(i).asInstanceOf[Seq]` because the data maybe `mutable.ArraySeq` but `Seq` is `immutable.Seq` in Scala 2.13
- Use a compatible way to let `+` and `-` method of `Decimal` having the same behavior in Scala 2.12 and Scala 2.13
- Call `toList` in `RelationalGroupedDataset.toDF` method when `groupingExprs` is `Stream` type because `Stream` can't serialize in Scala 2.13
- Add a manual sort to `classFunsMap` in `ExpressionsSchemaSuite` because `Iterable.groupBy` in Scala 2.13 has different result with `TraversableLike.groupBy` in Scala 2.12
### Why are the changes needed?
We need to support a Scala 2.13 build.
### Does this PR introduce _any_ user-facing change?
Should specified `Seq` to `scala.collection.Seq` when we call `Row.getAs[Seq]` and `Row.get(i).asInstanceOf[Seq]` because the data maybe `mutable.ArraySeq` but the `Seq` is `immutable.Seq` in Scala 2.13
### How was this patch tested?
- Scala 2.12: Pass the Jenkins or GitHub Action
- Scala 2.13: Do the following:
```
dev/change-scala-version.sh 2.13
mvn clean install -DskipTests -pl sql/core -Pscala-2.13 -am
mvn test -pl sql/core -Pscala-2.13
```
**Before**
```
Tests: succeeded 8166, failed 319, canceled 1, ignored 52, pending 0
*** 319 TESTS FAILED ***
```
**After**
```
Tests: succeeded 8204, failed 286, canceled 1, ignored 52, pending 0
*** 286 TESTS FAILED ***
```
Closes#29660 from LuciferYang/SPARK-32808.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This is a Spark 3.0 regression introduced by https://github.com/apache/spark/pull/26761. We missed a corner case that `java.lang.Double.compare` treats 0.0 and -0.0 as different, which breaks SQL semantic.
This PR adds back the `OrderingUtil`, to provide custom compare methods that take care of 0.0 vs -0.0
### Why are the changes needed?
Fix a correctness bug.
### Does this PR introduce _any_ user-facing change?
Yes, now `SELECT 0.0 > -0.0` returns false correctly as Spark 2.x.
### How was this patch tested?
new tests
Closes#29647 from cloud-fan/float.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/29485
It moves the plan rewriting methods from `Analyzer` to `QueryPlan`, so that it can work with `SparkPlan` as well. This PR also does an improvement to support a corner case (The attribute to be replace stays together with an unresolved attribute), and make it more general, so that `WidenSetOperationTypes` can rewrite the plan in one shot like before.
### Why are the changes needed?
Code cleanup and generalize.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing test
Closes#29643 from cloud-fan/cleanup.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Only copy tags to node with no tags when transforming plans.
### Why are the changes needed?
cloud-fan [made a good point](https://github.com/apache/spark/pull/29593#discussion_r482013121) that it doesn't make sense to append tags to existing nodes when nodes are removed. That will cause such bugs as duplicate rows when deduplicating and repartitioning by the same column with AQE.
```
spark.range(10).union(spark.range(10)).createOrReplaceTempView("v1")
val df = spark.sql("select id from v1 group by id distribute by id")
println(df.collect().toArray.mkString(","))
println(df.queryExecution.executedPlan)
// With AQE
[4],[0],[3],[2],[1],[7],[6],[8],[5],[9],[4],[0],[3],[2],[1],[7],[6],[8],[5],[9]
AdaptiveSparkPlan(isFinalPlan=true)
+- CustomShuffleReader local
+- ShuffleQueryStage 0
+- Exchange hashpartitioning(id#183L, 10), true
+- *(3) HashAggregate(keys=[id#183L], functions=[], output=[id#183L])
+- Union
:- *(1) Range (0, 10, step=1, splits=2)
+- *(2) Range (0, 10, step=1, splits=2)
// Without AQE
[4],[7],[0],[6],[8],[3],[2],[5],[1],[9]
*(4) HashAggregate(keys=[id#206L], functions=[], output=[id#206L])
+- Exchange hashpartitioning(id#206L, 10), true
+- *(3) HashAggregate(keys=[id#206L], functions=[], output=[id#206L])
+- Union
:- *(1) Range (0, 10, step=1, splits=2)
+- *(2) Range (0, 10, step=1, splits=2)
```
It's too expensive to detect node removal so we make a compromise only to copy tags to node with no tags.
### Does this PR introduce _any_ user-facing change?
Yes. Fix a bug.
### How was this patch tested?
Add test.
Closes#29593 from manuzhang/spark-32753.
Authored-by: manuzhang <owenzhang1990@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change `CreateFunctionCommand` code that add class check before create function.
### Why are the changes needed?
We have different behavior between create permanent function and temporary function when function class is invaild. e.g.,
```
create function f as 'test.non.exists.udf';
-- Time taken: 0.104 seconds
create temporary function f as 'test.non.exists.udf'
-- Error in query: Can not load class 'test.non.exists.udf' when registering the function 'f', please make sure it is on the classpath;
```
And Hive also fails both of them.
### Does this PR introduce _any_ user-facing change?
Yes, user will get exception when create a invalid udf.
### How was this patch tested?
New test.
Closes#29502 from ulysses-you/function.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
bugfix for incomplete interval values, e.g. interval '1', interval '1 day 2', currently these cases will result null, but actually we should fail them with IllegalArgumentsException
### Why are the changes needed?
correctness
### Does this PR introduce _any_ user-facing change?
yes, incomplete intervals will throw exception now
#### before
```
bin/spark-sql -S -e "select interval '1', interval '+', interval '1 day -'"
NULL NULL NULL
```
#### after
```
-- !query
select interval '1'
-- !query schema
struct<>
-- !query output
org.apache.spark.sql.catalyst.parser.ParseException
Cannot parse the INTERVAL value: 1(line 1, pos 7)
== SQL ==
select interval '1'
```
### How was this patch tested?
unit tests added
Closes#29635 from yaooqinn/SPARK-32785.
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 is a followup on #29598 and removes the `ExpressionSet` class from the 2.13 branch.
### Why are the changes needed?
`ExpressionSet` does not extend Scala `Set` anymore and this class is no longer needed in the 2.13 branch.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Passes existing tests
Closes#29648 from dbaliafroozeh/RemoveExpressionSetFrom2.13Branch.
Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR intends to fix a bug where references can be missing when adding aliases to widen data types in `WidenSetOperationTypes`. For example,
```
CREATE OR REPLACE TEMPORARY VIEW t3 AS VALUES (decimal(1)) tbl(v);
SELECT t.v FROM (
SELECT v FROM t3
UNION ALL
SELECT v + v AS v FROM t3
) t;
org.apache.spark.sql.AnalysisException: Resolved attribute(s) v#1 missing from v#3 in operator !Project [v#1]. Attribute(s) with the same name appear in the operation: v. Please check if the right attribute(s) are used.;;
!Project [v#1] <------ the reference got missing
+- SubqueryAlias t
+- Union
:- Project [cast(v#1 as decimal(11,0)) AS v#3]
: +- Project [v#1]
: +- SubqueryAlias t3
: +- SubqueryAlias tbl
: +- LocalRelation [v#1]
+- Project [v#2]
+- Project [CheckOverflow((promote_precision(cast(v#1 as decimal(11,0))) + promote_precision(cast(v#1 as decimal(11,0)))), DecimalType(11,0), true) AS v#2]
+- SubqueryAlias t3
+- SubqueryAlias tbl
+- LocalRelation [v#1]
```
In the case, `WidenSetOperationTypes` added the alias `cast(v#1 as decimal(11,0)) AS v#3`, then the reference in the top `Project` got missing. This PR correct the reference (`exprId` and widen `dataType`) after adding aliases in the rule.
### Why are the changes needed?
bugfixes
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added unit tests
Closes#29485 from maropu/SPARK-32638.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR changes `AttributeSet` and `ExpressionSet` to maintain the insertion order of the elements. More specifically, we:
- change the underlying data structure of `AttributeSet` from `HashSet` to `LinkedHashSet` to maintain the insertion order.
- `ExpressionSet` already uses a list to keep track of the expressions, however, since it is extending Scala's immutable.Set class, operations such as map and flatMap are delegated to the immutable.Set itself. This means that the result of these operations is not an instance of ExpressionSet anymore, rather it's a implementation picked up by the parent class. We also remove this inheritance from `immutable.Set `and implement the needed methods directly. ExpressionSet has a very specific semantics and it does not make sense to extend `immutable.Set` anyway.
- change the `PlanStabilitySuite` to not sort the attributes, to be able to catch changes in the order of expressions in different runs.
### Why are the changes needed?
Expressions identity is based on the `ExprId` which is an auto-incremented number. This means that the same query can yield a query plan with different expression ids in different runs. `AttributeSet` and `ExpressionSet` internally use a `HashSet` as the underlying data structure, and therefore cannot guarantee the a fixed order of operations in different runs. This can be problematic in cases we like to check for plan changes in different runs.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Passes `PlanStabilitySuite` after regenerating the golden files.
Closes#29598 from dbaliafroozeh/FixOrderOfExpressions.
Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
Move StreamingRelationV2 to the catalyst module and bind with the Table interface.
### Why are the changes needed?
Currently, the StreamingRelationV2 is bind with TableProvider. Since the V2 relation is not bound with `DataSource`, to make it more flexible and have better expansibility, it should be moved to the catalyst module and bound with the Table interface. We did a similar thing for DataSourceV2Relation.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UT.
Closes#29633 from xuanyuanking/SPARK-32782.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR fails the interval values parsing when they contain non-ASCII characters which are silently omitted right now.
e.g. the case below should be invalid
```
select interval 'interval中文 1 day'
```
### Why are the changes needed?
bugfix, intervals should fail when containing invalid characters
### Does this PR introduce _any_ user-facing change?
yes,
#### before
select interval 'interval中文 1 day' results 1 day, now it fails with
```
org.apache.spark.sql.catalyst.parser.ParseException
Cannot parse the INTERVAL value: interval中文 1 day
```
### How was this patch tested?
new tests
Closes#29632 from yaooqinn/SPARK-32781.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Struct field both in GROUP BY and Aggregate Expresison with CUBE/ROLLUP/GROUPING SET will failed when analysis.
```
test("SPARK-31670") {
withTable("t1") {
sql(
"""
|CREATE TEMPORARY VIEW t(a, b, c) AS
|SELECT * FROM VALUES
|('A', 1, NAMED_STRUCT('row_id', 1, 'json_string', '{"i": 1}')),
|('A', 2, NAMED_STRUCT('row_id', 2, 'json_string', '{"i": 1}')),
|('A', 2, NAMED_STRUCT('row_id', 2, 'json_string', '{"i": 2}')),
|('B', 1, NAMED_STRUCT('row_id', 3, 'json_string', '{"i": 1}')),
|('C', 3, NAMED_STRUCT('row_id', 4, 'json_string', '{"i": 1}'))
""".stripMargin)
checkAnswer(
sql(
"""
|SELECT a, c.json_string, SUM(b)
|FROM t
|GROUP BY a, c.json_string
|WITH CUBE
|""".stripMargin),
Row("A", "{\"i\": 1}", 3) :: Row("A", "{\"i\": 2}", 2) :: Row("A", null, 5) ::
Row("B", "{\"i\": 1}", 1) :: Row("B", null, 1) ::
Row("C", "{\"i\": 1}", 3) :: Row("C", null, 3) ::
Row(null, "{\"i\": 1}", 7) :: Row(null, "{\"i\": 2}", 2) :: Row(null, null, 9) :: Nil)
}
}
```
Error
```
[info] - SPARK-31670 *** FAILED *** (2 seconds, 857 milliseconds)
[info] Failed to analyze query: org.apache.spark.sql.AnalysisException: expression 't.`c`' 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.;;
[info] Aggregate [a#247, json_string#248, spark_grouping_id#246L], [a#247, c#223.json_string AS json_string#241, sum(cast(b#222 as bigint)) AS sum(b)#243L]
[info] +- Expand [List(a#221, b#222, c#223, a#244, json_string#245, 0), List(a#221, b#222, c#223, a#244, null, 1), List(a#221, b#222, c#223, null, json_string#245, 2), List(a#221, b#222, c#223, null, null, 3)], [a#221, b#222, c#223, a#247, json_string#248, spark_grouping_id#246L]
[info] +- Project [a#221, b#222, c#223, a#221 AS a#244, c#223.json_string AS json_string#245]
[info] +- SubqueryAlias t
[info] +- Project [col1#218 AS a#221, col2#219 AS b#222, col3#220 AS c#223]
[info] +- Project [col1#218, col2#219, col3#220]
[info] +- LocalRelation [col1#218, col2#219, col3#220]
[info]
```
For Struct type Field, when we resolve it, it will construct with Alias. When struct field in GROUP BY with CUBE/ROLLUP etc, struct field in groupByExpression and aggregateExpression will be resolved with different exprId as below
```
'Aggregate [cube(a#221, c#223.json_string AS json_string#240)], [a#221, c#223.json_string AS json_string#241, sum(cast(b#222 as bigint)) AS sum(b)#243L]
+- SubqueryAlias t
+- Project [col1#218 AS a#221, col2#219 AS b#222, col3#220 AS c#223]
+- Project [col1#218, col2#219, col3#220]
+- LocalRelation [col1#218, col2#219, col3#220]
```
This makes `ResolveGroupingAnalytics.constructAggregateExprs()` failed to replace aggreagteExpression use expand groupByExpression attribute since there exprId is not same. then error happened.
### Why are the changes needed?
Fix analyze bug
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
Added UT
Closes#28490 from AngersZhuuuu/SPARK-31670.
Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a follow-up of #29160. This allows Spark SQL project to compile for Scala 2.13.
### Why are the changes needed?
It's needed for #28545
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
I compiled with Scala 2.13. It fails in `Spark REPL` project, which will be fixed by #28545Closes#29584 from karolchmist/SPARK-32364-scala-2.13.
Authored-by: Karol Chmist <info+github@chmist.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
PropagateEmptyRelation will not be applied to LIMIT operators in streaming queries.
### Why are the changes needed?
Right now, the limit operator in a streaming query may get optimized away when the relation is empty. This can be problematic for stateful streaming, as this empty batch will not write any state store files, and the next batch will fail when trying to read these state store files and throw a file not found error.
We should not let PropagateEmptyRelation optimize away the Limit operator for streaming queries.
This PR is intended as a small and safe fix for PropagateEmptyRelation. A fundamental fix that can prevent this from happening again in the future and in other optimizer rules is more desirable, but that's a much larger task.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
unit tests.
Closes#29623 from liwensun/spark-32776.
Authored-by: liwensun <liwen.sun@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Now three join reorder suites(`JoinReorderSuite`, `StarJoinReorderSuite`, `StarJoinCostBasedReorderSuite`) all contain an `assertEqualPlans` method and the logic is almost the same. We can extract the method to a single place for code simplicity.
### Why are the changes needed?
To reduce code redundancy.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Covered by existing tests.
Closes#29594 from wzhfy/unify_assertEqualPlans_joinReorder.
Authored-by: Zhenhua Wang <wzh_zju@163.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Use `CodeGenerator.typeName()` instead of `Class.getCanonicalName()` in `CodegenContext.addReferenceObj()` for getting the runtime class name for an object.
### Why are the changes needed?
https://github.com/apache/spark/pull/29439 fixed a bug in `CodegenContext.addReferenceObj()` for `Array[Byte]` (i.e. Spark SQL's `BinaryType`) objects, but unfortunately it introduced a regression for some nested Scala types.
For example, for `implicitly[Ordering[UTF8String]]`, after that PR `CodegenContext.addReferenceObj()` would return `((null) references[0] /* ... */)`. The actual type for `implicitly[Ordering[UTF8String]]` is `scala.math.LowPriorityOrderingImplicits$$anon$3` in Scala 2.12.10, and `Class.getCanonicalName()` returns `null` for that class.
On the other hand, `Class.getName()` is safe to use for all non-array types, and Janino will happily accept the type name returned from `Class.getName()` for nested types. `CodeGenerator.typeName()` happens to do the right thing by correctly handling arrays and otherwise use `Class.getName()`. So it's a better alternative than `Class.getCanonicalName()`.
Side note: rule of thumb for using Java reflection in Spark: it may be tempting to use `Class.getCanonicalName()`, but for functions that may need to handle Scala types, please avoid it due to potential issues with nested Scala types.
Instead, use `Class.getName()` or utility functions in `org.apache.spark.util.Utils` (e.g. `Utils.getSimpleName()` or `Utils.getFormattedClassName()` etc).
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added new unit test case for the regression case in `CodeGenerationSuite`.
Closes#29602 from rednaxelafx/spark-32624-followup.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow-up on SPARK-32721 and PR #29567. In the previous PR we missed two more cases that can be optimized:
```
if(p, false, null) ==> and(not(p), null)
if(p, true, null) ==> or(p, null)
```
### Why are the changes needed?
By transforming if to boolean conjunctions or disjunctions, we can enable more filter pushdown to datasources.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added unit tests.
Closes#29603 from sunchao/SPARK-32721-2.
Authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
The following if clause:
```sql
if(p, null, false)
```
can be simplified to:
```sql
and(p, null)
```
Similarly, the clause:
```sql
if(p, null, true)
```
can be simplified to
```sql
or(not(p), null)
```
iff the predicate `p` is non-nullable, i.e., can be evaluated to either true or false, but not null.
### Why are the changes needed?
Converting if to or/and clauses can better push filters down.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests.
Closes#29567 from sunchao/SPARK-32721.
Authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
pass specified options in DataFrameReader.table to JDBCTableCatalog.loadTable
### Why are the changes needed?
Currently, `DataFrameReader.table` ignores the specified options. The options specified like the following are lost.
```
val df = spark.read
.option("partitionColumn", "id")
.option("lowerBound", "0")
.option("upperBound", "3")
.option("numPartitions", "2")
.table("h2.test.people")
```
We need to make `DataFrameReader.table` take the specified options.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manually test for now. Will add a test after V2 JDBC read is implemented.
Closes#29535 from huaxingao/table_options.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds extended information of a function including arguments, examples, notes and the since field to the SparkGetFunctionOperation
### Why are the changes needed?
better user experience, it will help JDBC users to have a better understanding of our builtin functions
### Does this PR introduce _any_ user-facing change?
Yes, BI tools and JDBC users will get full information on a spark function instead of only fragmentary usage info.
e.g. date_part
#### before
```
date_part(field, source) - Extracts a part of the date/timestamp or interval source.
```
#### after
```
Usage:
date_part(field, source) - Extracts a part of the date/timestamp or interval source.
Arguments:
* field - selects which part of the source should be extracted, and supported string values are as same as the fields of the equivalent function `EXTRACT`.
* source - a date/timestamp or interval column from where `field` should be extracted
Examples:
> SELECT date_part('YEAR', TIMESTAMP '2019-08-12 01:00:00.123456');
2019
> SELECT date_part('week', timestamp'2019-08-12 01:00:00.123456');
33
> SELECT date_part('doy', DATE'2019-08-12');
224
> SELECT date_part('SECONDS', timestamp'2019-10-01 00:00:01.000001');
1.000001
> SELECT date_part('days', interval 1 year 10 months 5 days);
5
> SELECT date_part('seconds', interval 5 hours 30 seconds 1 milliseconds 1 microseconds);
30.001001
Note:
The date_part function is equivalent to the SQL-standard function `EXTRACT(field FROM source)`
Since: 3.0.0
```
### How was this patch tested?
New tests
Closes#29577 from yaooqinn/SPARK-32733.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Instead of deleting the data, we can move the data to trash.
Based on the configuration provided by the user it will be deleted permanently from the trash.
### Why are the changes needed?
Instead of directly deleting the data, we can provide flexibility to move data to the trash and then delete it permanently.
### Does this PR introduce _any_ user-facing change?
Yes, After truncate table the data is not permanently deleted now.
It is first moved to the trash and then after the given time deleted permanently;
### How was this patch tested?
new UTs added
Closes#29552 from Udbhav30/truncate.
Authored-by: Udbhav30 <u.agrawal30@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Remove the YEAR, MONTH, DAY, HOUR, MINUTE, SECOND keywords. They are not useful in the parser, as we need to support plural like YEARS, so the parser has to accept the general identifier as interval unit anyway.
### Why are the changes needed?
These keywords are reserved in ANSI. If Spark has these keywords, then they become reserved under ANSI mode. This makes Spark not able to run TPCDS queries as they use YEAR as alias name.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added `TPCDSQueryANSISuite`, to make sure Spark with ANSI mode can run TPCDS queries.
Closes#29560 from cloud-fan/keyword.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add missing since version for math functions, including
SPARK-8223 shiftright/shiftleft
SPARK-8215 pi
SPARK-8212 e
SPARK-6829 sin/asin/sinh/cos/acos/cosh/tan/atan/tanh/ceil/floor/rint/cbrt/signum/isignum/Fsignum/Lsignum/degrees/radians/log/log10/log1p/exp/expm1/pow/hypot/atan2
SPARK-8209 conv
SPARK-8213 factorial
SPARK-20751 cot
SPARK-2813 sqrt
SPARK-8227 unhex
SPARK-8218 log(a,b)
SPARK-8207 bin
SPARK-8214 hex
SPARK-8206 round
SPARK-14614 bround
### Why are the changes needed?
fix SQL docs
### Does this PR introduce _any_ user-facing change?
yes, doc updated
### How was this patch tested?
passing doc generation.
Closes#29571 from yaooqinn/minor.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to add a specific `AQEOptimizer` for the `AdaptiveSparkPlanExec` instead of implementing an anonymous `RuleExecutor`. At the same time, this PR also adds the configuration `spark.sql.adaptive.optimizer.excludedRules`, which follows the same pattern of `Optimizer`, to make the `AQEOptimizer` more flexible for users and developers.
### Why are the changes needed?
Currently, `AdaptiveSparkPlanExec` has implemented an anonymous `RuleExecutor` to apply the AQE optimize rules on the plan. However, the anonymous class usually could be inconvenient to maintain and extend for the long term.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
It's a pure refactor so pass existing tests should be ok.
Closes#29559 from Ngone51/impro-aqe-optimizer.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to move Arrow usage guide from Spark documentation site to PySpark documentation site (at "User Guide").
Here is the demo for reviewing quicker: https://hyukjin-spark.readthedocs.io/en/stable/user_guide/arrow_pandas.html
### Why are the changes needed?
To have a single place for PySpark users, and better documentation.
### Does this PR introduce _any_ user-facing change?
Yes, it will move https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html to our PySpark documentation.
### How was this patch tested?
```bash
cd docs
SKIP_SCALADOC=1 SKIP_RDOC=1 SKIP_SQLDOC=1 jekyll serve --watch
```
and
```bash
cd python/docs
make clean html
```
Closes#29548 from HyukjinKwon/SPARK-32183.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow up PR to #29328 to apply the same constraint where `path` option cannot coexist with path parameter to `DataFrameWriter.save()`, `DataStreamReader.load()` and `DataStreamWriter.start()`.
### Why are the changes needed?
The current behavior silently overwrites the `path` option if path parameter is passed to `DataFrameWriter.save()`, `DataStreamReader.load()` and `DataStreamWriter.start()`.
For example,
```
Seq(1).toDF.write.option("path", "/tmp/path1").parquet("/tmp/path2")
```
will write the result to `/tmp/path2`.
### Does this PR introduce _any_ user-facing change?
Yes, if `path` option coexists with path parameter to any of the above methods, it will throw `AnalysisException`:
```
scala> Seq(1).toDF.write.option("path", "/tmp/path1").parquet("/tmp/path2")
org.apache.spark.sql.AnalysisException: There is a 'path' option set and save() is called with a path parameter. Either remove the path option, or call save() without the parameter. To ignore this check, set 'spark.sql.legacy.pathOptionBehavior.enabled' to 'true'.;
```
The user can restore the previous behavior by setting `spark.sql.legacy.pathOptionBehavior.enabled` to `true`.
### How was this patch tested?
Added new tests.
Closes#29543 from imback82/path_option.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Instead of deleting the data, we can move the data to trash.
Based on the configuration provided by the user it will be deleted permanently from the trash.
### Why are the changes needed?
Instead of directly deleting the data, we can provide flexibility to move data to the trash and then delete it permanently.
### Does this PR introduce _any_ user-facing change?
Yes, After truncate table the data is not permanently deleted now.
It is first moved to the trash and then after the given time deleted permanently;
### How was this patch tested?
new UTs added
Closes#29387 from Udbhav30/tuncateTrash.
Authored-by: Udbhav30 <u.agrawal30@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Spark's CSV source can optionally ignore lines starting with a comment char. Some code paths check to see if it's set before applying comment logic (i.e. not set to default of `\0`), but many do not, including the one that passes the option to Univocity. This means that rows beginning with a null char were being treated as comments even when 'disabled'.
### Why are the changes needed?
To avoid dropping rows that start with a null char when this is not requested or intended. See JIRA for an example.
### Does this PR introduce _any_ user-facing change?
Nothing beyond the effect of the bug fix.
### How was this patch tested?
Existing tests plus new test case.
Closes#29516 from srowen/SPARK-32614.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
There is a bug in the way the optimizer rule in `SimplifyExtractValueOps` is currently written in master branch which yields incorrect results in scenarios like the following:
```
sql("SELECT CAST(NULL AS struct<a:int,b:int>) struct_col")
.select($"struct_col".withField("d", lit(4)).getField("d").as("d"))
// currently returns this:
+---+
|d |
+---+
|4 |
+---+
// when in fact it should return this:
+----+
|d |
+----+
|null|
+----+
```
The changes in this PR will fix this bug.
### Why are the changes needed?
To fix the aforementioned bug. Optimizer rules should improve the performance of the query but yield exactly the same results.
### Does this PR introduce _any_ user-facing change?
Yes, this bug will no longer occur.
That said, this isn't something to be concerned about as this bug was introduced in Spark 3.1 and Spark 3.1 has yet to be released.
### How was this patch tested?
Unit tests were added. Jenkins must pass them.
Closes#29522 from fqaiser94/SPARK-32641.
Authored-by: fqaiser94@gmail.com <fqaiser94@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Added Java docs for Long data types in the Row class.
### Why are the changes needed?
The Long datatype is somehow missing in Row.scala's `apply` and `get` methods.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UTs.
Closes#29534 from yeshengm/docs-fix.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Change to use `dataTypes.foreach` instead of get the element use specified index in `def this(dataTypes: Seq[DataType]) `constructor of `SpecificInternalRow` because the random access performance is unsatisfactory if the input argument not a `IndexSeq`.
This pr followed srowen's advice.
### Why are the changes needed?
I found that SPARK-32550 had some negative impact on performance, the typical cases is "deterministic cardinality estimation" in `HyperLogLogPlusPlusSuite` when rsd is 0.001, we found the code that is significantly slower is line 41 in `HyperLogLogPlusPlusSuite`： `new SpecificInternalRow(hll.aggBufferAttributes.map(_.dataType)) `
08b951b1cb/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/aggregate/HyperLogLogPlusPlusSuite.scala (L40-L44)
The size of "hll.aggBufferAttributes" in this case is 209716, the results of comparison before and after spark-32550 merged are as follows, The unit is ns:
| After SPARK-32550 createBuffer | After SPARK-32550 end to end | Before SPARK-32550 createBuffer | Before SPARK-32550 end to end
-- | -- | -- | -- | --
rsd 0.001, n 1000 | 52715513243 | 53004810687 | 195807999 | 773977677
rsd 0.001, n 5000 | 51881246165 | 52519358215 | 13689949 | 249974855
rsd 0.001, n 10000 | 52234282788 | 52374639172 | 14199071 | 183452846
rsd 0.001, n 50000 | 55503517122 | 55664035449 | 15219394 | 584477125
rsd 0.001, n 100000 | 51862662845 | 52116774177 | 19662834 | 166483678
rsd 0.001, n 500000 | 51619226715 | 52183189526 | 178048012 | 16681330
rsd 0.001, n 1000000 | 54861366981 | 54976399142 | 226178708 | 18826340
rsd 0.001, n 5000000 | 52023602143 | 52354615149 | 388173579 | 15446409
rsd 0.001, n 10000000 | 53008591660 | 53601392304 | 533454460 | 16033032
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
`mvn test -pl sql/catalyst -DwildcardSuites=org.apache.spark.sql.catalyst.expressions.aggregate.HyperLogLogPlusPlusSuite -Dtest=none`
**Before**:
```
Run completed in 8 minutes, 18 seconds.
Total number of tests run: 5
Suites: completed 2, aborted 0
Tests: succeeded 5, failed 0, canceled 0, ignored 0, pending 0
```
**After**
```
Run completed in 7 seconds, 65 milliseconds.
Total number of tests run: 5
Suites: completed 2, aborted 0
Tests: succeeded 5, failed 0, canceled 0, ignored 0, pending 0
```
Closes#29529 from LuciferYang/revert-spark-32550.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to make the behavior consistent for the `path` option when loading dataframes with a single path (e.g, `option("path", path).format("parquet").load(path)` vs. `option("path", path).parquet(path)`) by disallowing `path` option to coexist with `load`'s path parameters.
### Why are the changes needed?
The current behavior is inconsistent:
```scala
scala> Seq(1).toDF.write.mode("overwrite").parquet("/tmp/test")
scala> spark.read.option("path", "/tmp/test").format("parquet").load("/tmp/test").show
+-----+
|value|
+-----+
| 1|
+-----+
scala> spark.read.option("path", "/tmp/test").parquet("/tmp/test").show
+-----+
|value|
+-----+
| 1|
| 1|
+-----+
```
### Does this PR introduce _any_ user-facing change?
Yes, now if the `path` option is specified along with `load`'s path parameters, it would fail:
```scala
scala> Seq(1).toDF.write.mode("overwrite").parquet("/tmp/test")
scala> spark.read.option("path", "/tmp/test").format("parquet").load("/tmp/test").show
org.apache.spark.sql.AnalysisException: There is a path option set and load() is called with path parameters. Either remove the path option or move it into the load() parameters.;
at org.apache.spark.sql.DataFrameReader.verifyPathOptionDoesNotExist(DataFrameReader.scala:310)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:232)
... 47 elided
scala> spark.read.option("path", "/tmp/test").parquet("/tmp/test").show
org.apache.spark.sql.AnalysisException: There is a path option set and load() is called with path parameters. Either remove the path option or move it into the load() parameters.;
at org.apache.spark.sql.DataFrameReader.verifyPathOptionDoesNotExist(DataFrameReader.scala:310)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:250)
at org.apache.spark.sql.DataFrameReader.parquet(DataFrameReader.scala:778)
at org.apache.spark.sql.DataFrameReader.parquet(DataFrameReader.scala:756)
... 47 elided
```
The user can restore the previous behavior by setting `spark.sql.legacy.pathOptionBehavior.enabled` to `true`.
### How was this patch tested?
Added a test
Closes#29328 from imback82/dfw_option.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The purpose of this pr is to resolve [SPARK-32526](https://issues.apache.org/jira/browse/SPARK-32526), all remaining failed cases are fixed.
The main change of this pr as follow:
- Change of `ExecutorAllocationManager.scala` for core module compilation in Scala 2.13, it's a blocking problem
- Change `Seq[_]` to `scala.collection.Seq[_]` refer to failed cases
- Added different expected plan of `Test 4: Star with several branches` of StarJoinCostBasedReorderSuite for Scala 2.13 because the candidates plans:
```
Join Inner, (d1_pk#5 = f1_fk1#0)
:- Join Inner, (f1_fk2#1 = d2_pk#8)
: :- Join Inner, (f1_fk3#2 = d3_pk#11)
```
and
```
Join Inner, (f1_fk2#1 = d2_pk#8)
:- Join Inner, (d1_pk#5 = f1_fk1#0)
: :- Join Inner, (f1_fk3#2 = d3_pk#11)
```
have same cost `Cost(200,9200)`, but `HashMap` is rewritten in scala 2.13 and The order of iterations leads to different results.
This pr fix test cases as follow:
- LiteralExpressionSuite (1 FAILED -> PASS)
- StarJoinCostBasedReorderSuite ( 1 FAILED-> PASS)
- ObjectExpressionsSuite( 2 FAILED-> PASS)
- ScalaReflectionSuite （1 FAILED-> PASS）
- RowEncoderSuite (10 FAILED-> PASS)
- ExpressionEncoderSuite (ABORTED-> PASS)
### Why are the changes needed?
We need to support a Scala 2.13 build.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
<!--
- Scala 2.12: Pass the Jenkins or GitHub Action
- Scala 2.13: Do the following:
```
dev/change-scala-version.sh 2.13
mvn clean install -DskipTests -pl sql/catalyst -Pscala-2.13 -am
mvn test -pl sql/catalyst -Pscala-2.13
```
**Before**
```
Tests: succeeded 4035, failed 17, canceled 0, ignored 6, pending 0
*** 1 SUITE ABORTED ***
*** 15 TESTS FAILED ***
```
**After**
```
Tests: succeeded 4338, failed 0, canceled 0, ignored 6, pending 0
All tests passed.
```
Closes#29434 from LuciferYang/sql-catalyst-tests.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
