### What changes were proposed in this pull request?
This patch sorts equivalent expressions based on their child-parent relation.
### Why are the changes needed?
`EquivalentExpressions` maintains a map of equivalent expressions. It is `HashMap` now so the insertion order is not guaranteed to be preserved later. Subexpression elimination relies on retrieving subexpressions from the map. If there is child-parent relationships among the subexpressions, we want the child expressions come first than parent expressions, so we can replace child expressions in parent expressions with subexpression evaluation.
For example, we have two different expressions `Add(Literal(1), Literal(2))` and `Add(Literal(3), add)`.
Case 1: child subexpr comes first.
```scala
addExprTree(add)
addExprTree(Add(Literal(3), add))
addExprTree(Add(Literal(3), add))
```
Case 2: parent subexpr comes first. For this case, we need to sort equivalent expressions.
```
addExprTree(Add(Literal(3), add)) => We add `Add(Literal(3), add)` into the map first, then add `add` into the map
addExprTree(add)
addExprTree(Add(Literal(3), add))
```
As we are going to sort equivalent expressions at all, we don't need `LinkedHashMap` but just do sorting.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added tests.
Closes#32586 from viirya/use-listhashmap.
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?
### Why are the changes needed?
Fix scala compile error.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass GA
Closes#32617 from ulysses-you/scala2-13.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR group exception messages in `sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst`.
### Why are the changes needed?
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.
Closes#32478 from beliefer/SPARK-35063.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
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 proposes to format strings correctly for `PushedFilters`. For example, `explain()` for a query below prints `v in (array('a'))` as `PushedFilters: [In(v, [WrappedArray(a)])]`;
```
scala> sql("create table t (v array<string>) using parquet")
scala> sql("select * from t where v in (array('a'), null)").explain()
== Physical Plan ==
*(1) Filter v#4 IN ([a],null)
+- FileScan parquet default.t[v#4] Batched: false, DataFilters: [v#4 IN ([a],null)], Format: Parquet, Location: InMemoryFileIndex[file:/Users/maropu/Repositories/spark/spark-3.1.1-bin-hadoop2.7/spark-warehouse/t], PartitionFilters: [], PushedFilters: [In(v, [WrappedArray(a),null])], ReadSchema: struct<v:array<string>>
```
This PR makes `explain()` print it as `PushedFilters: [In(v, [[a]])]`;
```
scala> sql("select * from t where v in (array('a'), null)").explain()
== Physical Plan ==
*(1) Filter v#4 IN ([a],null)
+- FileScan parquet default.t[v#4] Batched: false, DataFilters: [v#4 IN ([a],null)], Format: Parquet, Location: InMemoryFileIndex[file:/Users/maropu/Repositories/spark/spark-3.1.1-bin-hadoop2.7/spark-warehouse/t], PartitionFilters: [], PushedFilters: [In(v, [[a],null])], ReadSchema: struct<v:array<string>>
```
NOTE: This PR includes a bugfix caused by #32577 (See the cloud-fan comment: https://github.com/apache/spark/pull/32577/files#r636108150).
### Why are the changes needed?
To improve explain strings.
### Does this PR introduce _any_ user-facing change?
Yes, this PR improves the explain strings for pushed-down filters.
### How was this patch tested?
Added tests in `SQLQueryTestSuite`.
Closes#32615 from maropu/ExplainPartitionFilters.
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 reduces the execution time of `DeduplicateRelations` by:
1) use `Set` instead `Seq` to check duplicate relations
2) avoid plan output traverse and attribute rewrites when there are no changes in the children plan
### Why are the changes needed?
Rule `DeduplicateRelations` is slow.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Run `TPCDSQuerySuite` and checked the run time of `DeduplicateRelations`. The time has been reduced by 77.9% after this PR.
Closes#32590 from Ngone51/improve-dedup.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Gengliang Wang <ltnwgl@gmail.com>
### What changes were proposed in this pull request?
CTAS with location clause acts as an insert overwrite. This can cause problems when there are subdirectories within a location directory.
This causes some users to accidentally wipe out directories with very important data. We should not allow CTAS with location to a non-empty directory.
### Why are the changes needed?
Hive already handled this scenario: HIVE-11319
Steps to reproduce:
```scala
sql("""create external table `demo_CTAS`( `comment` string) PARTITIONED BY (`col1` string, `col2` string) STORED AS parquet location '/tmp/u1/demo_CTAS'""")
sql("""INSERT OVERWRITE TABLE demo_CTAS partition (col1='1',col2='1') VALUES ('abc')""")
sql("select* from demo_CTAS").show
sql("""create table ctas1 location '/tmp/u2/ctas1' as select * from demo_CTAS""")
sql("select* from ctas1").show
sql("""create table ctas2 location '/tmp/u2' as select * from demo_CTAS""")
```
Before the fix: Both create table operations will succeed. But values in table ctas1 will be replaced by ctas2 accidentally.
After the fix: `create table ctas2...` will throw `AnalysisException`:
```
org.apache.spark.sql.AnalysisException: CREATE-TABLE-AS-SELECT cannot create table with location to a non-empty directory /tmp/u2 . To allow overwriting the existing non-empty directory, set 'spark.sql.legacy.allowNonEmptyLocationInCTAS' to true.
```
### Does this PR introduce _any_ user-facing change?
Yes, if the location directory is not empty, CTAS with location will throw AnalysisException
```
sql("""create table ctas2 location '/tmp/u2' as select * from demo_CTAS""")
```
```
org.apache.spark.sql.AnalysisException: CREATE-TABLE-AS-SELECT cannot create table with location to a non-empty directory /tmp/u2 . To allow overwriting the existing non-empty directory, set 'spark.sql.legacy.allowNonEmptyLocationInCTAS' to true.
```
`CREATE TABLE AS SELECT` with non-empty `LOCATION` will throw `AnalysisException`. To restore the behavior before Spark 3.2, need to set `spark.sql.legacy.allowNonEmptyLocationInCTAS` to `true`. , default value is `false`.
Updated SQL migration guide.
### How was this patch tested?
Test case added in SQLQuerySuite.scala
Closes#32411 from vinodkc/br_fixCTAS_nonempty_dir.
Authored-by: Vinod KC <vinod.kc.in@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Updating column stats for Union operator stats estimation
### Why are the changes needed?
This is a followup PR to update the null count also in the Union stats operator estimation. https://github.com/apache/spark/pull/30334
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Updated UTs, manual testing
Closes#32494 from shahidki31/shahid/updateNullCountForUnion.
Lead-authored-by: shahid <shahidki31@gmail.com>
Co-authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Update histogram statistics for RANGE operator stats estimation
### Why are the changes needed?
If histogram optimization is enabled, this statistics can be used in various cost based optimizations.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UTs. Manual test.
Closes#32498 from shahidki31/shahid/histogram.
Lead-authored-by: shahid <shahidki31@gmail.com>
Co-authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
1. In HadoopMapReduceCommitProtocol, create parent directory before renaming custom partition path staging files
2. In InMemoryCatalog and HiveExternalCatalog, create new partition directory before renaming old partition path
3. Check return value of FileSystem#rename, if false, throw exception to avoid silent data loss cause by rename failure
4. Change DebugFilesystem#rename behavior to make it match HDFS's behavior (return false without rename when dst parent directory not exist)
### Why are the changes needed?
Depends on FileSystem#rename implementation, when destination directory does not exist, file system may
1. return false without renaming file nor throwing exception (e.g. HDFS), or
2. create destination directory, rename files, and return true (e.g. LocalFileSystem)
In the first case above, renames in HadoopMapReduceCommitProtocol for custom partition path will fail silently if the destination partition path does not exist. Failed renames can happen when
1. dynamicPartitionOverwrite == true, the custom partition path directories are deleted by the job before the rename; or
2. the custom partition path directories do not exist before the job; or
3. something else is wrong when file system handle `rename`
The renames in MemoryCatalog and HiveExternalCatalog for partition renaming also have similar issue.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Modified DebugFilesystem#rename, and added new unit tests.
Without the fix in src code, five InsertSuite tests and one AlterTableRenamePartitionSuite test failed:
InsertSuite.SPARK-20236: dynamic partition overwrite with custom partition path (existing test with modified FS)
```
== Results ==
!== Correct Answer - 1 == == Spark Answer - 0 ==
struct<> struct<>
![2,1,1]
```
InsertSuite.SPARK-35106: insert overwrite with custom partition path
```
== Results ==
!== Correct Answer - 1 == == Spark Answer - 0 ==
struct<> struct<>
![2,1,1]
```
InsertSuite.SPARK-35106: dynamic partition overwrite with custom partition path
```
== Results ==
!== Correct Answer - 2 == == Spark Answer - 1 ==
!struct<> struct<i:int,part1:int,part2:int>
[1,1,1] [1,1,1]
![1,1,2]
```
InsertSuite.SPARK-35106: Throw exception when rename custom partition paths returns false
```
Expected exception org.apache.spark.SparkException to be thrown, but no exception was thrown
```
InsertSuite.SPARK-35106: Throw exception when rename dynamic partition paths returns false
```
Expected exception org.apache.spark.SparkException to be thrown, but no exception was thrown
```
AlterTableRenamePartitionSuite.ALTER TABLE .. RENAME PARTITION V1: multi part partition (existing test with modified FS)
```
== Results ==
!== Correct Answer - 1 == == Spark Answer - 0 ==
struct<> struct<>
![3,123,3]
```
Closes#32530 from YuzhouSun/SPARK-35106.
Authored-by: Yuzhou Sun <yuzhosun@amazon.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
SPARK-35253 upgraded janino from 3.0.16 to 3.1.4, `ClassBodyEvaluator` provides the `getBytecodes` method to get
the mapping from `ClassFile#getThisClassName` to `ClassFile#toByteArray` directly in this version and we don't need to get this variable by reflection api anymore.
So the main purpose of this pr is simplify the way to get `bytecodes` from `ClassBodyEvaluator` in `CodeGenerator#updateAndGetCompilationStats` method.
### Why are the changes needed?
Code simplification.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Pass the Jenkins or GitHub Action
- Manual test:
1. Define a code fragment to be tested, for example:
```
val codeBody = s"""
public java.lang.Object generate(Object[] references) {
return new TestMetricCode(references);
}
class TestMetricCode {
public TestMetricCode(Object[] references) {
}
public long sumOfSquares(long left, long right) {
return left * left + right * right;
}
}
"""
```
2. Create a `ClassBodyEvaluator` and `cook` the `codeBody` as above, the process of creating `ClassBodyEvaluator` can extract from `CodeGenerator#doCompile` method.
3. Get `bytecodes` using `ClassBodyEvaluator#getBytecodes` api(after this pr) and reflection api(before this pr) respectively, then assert that they are the same. If the `bytecodes` not changed, we can be sure that metrics state will not change. The test code example as follows:
```
import scala.collection.JavaConverters._
val bytecodesFromApi = evaluator.getBytecodes.asScala
val bytecodesFromReflectionApi = {
val scField = classOf[ClassBodyEvaluator].getDeclaredField("sc")
scField.setAccessible(true)
val compiler = scField.get(evaluator).asInstanceOf[SimpleCompiler]
val loader = compiler.getClassLoader.asInstanceOf[ByteArrayClassLoader]
val classesField = loader.getClass.getDeclaredField("classes")
classesField.setAccessible(true)
classesField.get(loader).asInstanceOf[java.util.Map[String, Array[Byte]]].asScala
}
assert(bytecodesFromApi == bytecodesFromReflectionApi)
```
Closes#32536 from LuciferYang/SPARK-35253-FOLLOWUP.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Write out Seq of product objects which contain TreeNode, to avoid the cases as described in https://issues.apache.org/jira/browse/SPARK-35411 that essential information will be ignored and just written out as null values. These information are necessary to understand the query plans.
### Why are the changes needed?
Information like cteRelations in With node, and branches in CaseWhen expression are necessary to understand the query plans, they should be written out to the result json string.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
UT case added.
Closes#32557 from ivoson/plan-json-fix.
Authored-by: Tengfei Huang <tengfei.h@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
To pass the TPCDS-related plan stability tests in scala-2.13, this PR proposes to fix two things below;
- (1) Sorts elements in the predicate `InSet` and the source filter `In` for printing their nodes.
- (2) Formats nested collection elements (`Seq`, `Array`, and `Set`) recursively in `TreeNode.argString`.
As for (1), it seems v2.12/v2.13 prints `Set` elements with a different order, so we need to sort them explicitly. As for (2), the `Seq` implementation is different between v2.12/v2.13, so we need to format nested `Seq` elements correctly to hide the name of its implementation (See an example below);
```
(74) Expand [codegen id : 20]
Input [5]: [sales#41, RETURNS#42, profit#43, channel#44, id#45]
-Arguments: [ArrayBuffer(sales#41, returns#42, ... <-- scala-2.12
+Arguments: [Vector(sales#41, returns#42, ... <-- scala-2.13
+Arguments: [[(sales#41, returns#42, ... <-- the proposed fix to hide the name of its implementation
```
### Why are the changes needed?
To pass the tests in Scala v2.13.
### Does this PR introduce _any_ user-facing change?
Yes, this fix changes query explain strings.
### How was this patch tested?
Manually checked.
Closes#32577 from maropu/FixTPCDSTestIssueInScala213.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When creating `Invoke` and `StaticInvoke` for `ScalarFunction`'s magic method, set `propagateNull` to false.
### Why are the changes needed?
When `propgagateNull` is true (which is the default value), `Invoke` and `StaticInvoke` will return null if any of the argument is null. For scalar function this is incorrect, as we should leave the logic to function implementation instead.
### Does this PR introduce _any_ user-facing change?
Yes. Now null arguments shall be properly handled with magic method.
### How was this patch tested?
Added new tests.
Closes#32553 from sunchao/SPARK-35389.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/30309 added a configuration (disabled by default) that simplifies the error messages from Python UDFS, which removed internal stacktrace from Python workers:
```python
from pyspark.sql.functions import udf; spark.range(10).select(udf(lambda x: x/0)("id")).collect()
```
**Before**
```
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
```
**After**
```
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
```
Note that the traceback (`return f(*args, **kwargs)`) is almost always same - I would say more than 99%. For 1% case, we can guide developers to enable this configuration for further debugging.
In Databricks, it has been enabled for around 6 months, and I have had zero negative feedback on it.
### Why are the changes needed?
To show simplified exception messages to end users.
### Does this PR introduce _any_ user-facing change?
Yes, it will hide the internal Python worker traceback.
### How was this patch tested?
Existing test cases should cover.
Closes#32569 from HyukjinKwon/SPARK-35419.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is used to fix this bug:
```
set spark.sql.legacy.charVarcharAsString=true;
create table chartb01(a char(3));
insert into chartb01 select 'aaaaa';
```
here we expect the data of table chartb01 is 'aaa', but it runs failed.
### Why are the changes needed?
Improve backward compatibility
```
spark-sql>
> create table tchar01(col char(2)) using parquet;
Time taken: 0.767 seconds
spark-sql>
> insert into tchar01 select 'aaa';
ERROR | Executor task launch worker for task 0.0 in stage 0.0 (TID 0) | Aborting task | org.apache.spark.util.Utils.logError(Logging.scala:94)
java.lang.RuntimeException: Exceeds char/varchar type length limitation: 2
at org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils.trimTrailingSpaces(CharVarcharCodegenUtils.java:31)
at org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils.charTypeWriteSideCheck(CharVarcharCodegenUtils.java:44)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.project_doConsume_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:755)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$executeTask$1(FileFormatWriter.scala:279)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1500)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.executeTask(FileFormatWriter.scala:288)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$write$15(FileFormatWriter.scala:212)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1466)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
```
### Does this PR introduce _any_ user-facing change?
No (the legacy config is false by default).
### How was this patch tested?
Added unit tests.
Closes#32501 from fhygh/master.
Authored-by: fhygh <283452027@qq.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Spark doesn't support aggregate functions with mixed outer and local references. This PR applies this check earlier to fail with a clear error message instead of some weird ones, and simplifies the related code in `SubExprUtils.getOuterReferences`. This PR also refines the error message a bit.
### Why are the changes needed?
better error message
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
updated tests
Closes#32503 from cloud-fan/try.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Introduction: this PR is a part of SPARK-10816 (`EventTime based sessionization (session window)`). Please refer #31937 to see the overall view of the code change. (Note that code diff could be diverged a bit.)
### What changes were proposed in this pull request?
This PR introduces UpdatingSessionsIterator, which analyzes neighbor elements and adjust session information on elements.
UpdatingSessionsIterator calculates and updates the session window for each element in the given iterator, which makes elements in the same session window having same session spec. Downstream can apply aggregation to finally merge these elements bound to the same session window.
UpdatingSessionsIterator works on the precondition that given iterator is sorted by "group keys + start time of session window", and the iterator still retains the characteristic of the sort.
UpdatingSessionsIterator copies the elements to safely update on each element, as well as buffers elements which are bound to the same session window. Due to such overheads, MergingSessionsIterator which will be introduced via SPARK-34889 should be used whenever possible.
This PR also introduces UpdatingSessionsExec which is the physical node on leveraging UpdatingSessionsIterator to sort the input rows and updates session information on input rows.
### Why are the changes needed?
This part is a one of required on implementing SPARK-10816.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test suite added.
Closes#31986 from HeartSaVioR/SPARK-34888-SPARK-10816-PR-31570-part-1.
Lead-authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
Co-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Move hash map lookup operation out of `InvokeLike.invoke` since it doesn't depend on the input.
### Why are the changes needed?
We shouldn't need to look up the hash map for every input row evaluated by `InvokeLike.invoke` since it doesn't depend on input. This could speed up the performance a bit.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#32532 from sunchao/SPARK-35384-follow-up.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add a common functions `getWorkspaceFilePath` (which prefixed with spark home) to `SparkFunctionSuite`, and applies these the function to where they're extracted from.
### Why are the changes needed?
Spark sql has test suites to read resources when running tests. The way of getting the path of resources is commonly used in different suites. We can extract them into a function to ease the code maintenance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass existing tests.
Closes#32315 from Ngone51/extract-common-file-path.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Generally, we would expect that x = y => hash( x ) = hash( y ). However +-0 hash to different values for floating point types.
```
scala> spark.sql("select hash(cast('0.0' as double)), hash(cast('-0.0' as double))").show
+-------------------------+--------------------------+
|hash(CAST(0.0 AS DOUBLE))|hash(CAST(-0.0 AS DOUBLE))|
+-------------------------+--------------------------+
| -1670924195| -853646085|
+-------------------------+--------------------------+
scala> spark.sql("select cast('0.0' as double) == cast('-0.0' as double)").show
+--------------------------------------------+
|(CAST(0.0 AS DOUBLE) = CAST(-0.0 AS DOUBLE))|
+--------------------------------------------+
| true|
+--------------------------------------------+
```
Here is an extract from IEEE 754:
> The two zeros are distinguishable arithmetically only by either division-byzero ( producing appropriately signed infinities ) or else by the CopySign function recommended by IEEE 754 /854. Infinities, SNaNs, NaNs and Subnormal numbers necessitate four more special cases
From this, I deduce that the hash function must produce the same result for 0 and -0.
### Why are the changes needed?
It is a correctness issue
### Does this PR introduce _any_ user-facing change?
This changes only affect to the hash function applied to -0 value in float and double types
### How was this patch tested?
Unit testing and manual testing
Closes#32496 from planga82/feature/spark35207_hashnegativezero.
Authored-by: Pablo Langa <soypab@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch replaces `sys.err` usages with explicit exception types.
### Why are the changes needed?
Motivated by the previous comment https://github.com/apache/spark/pull/32519#discussion_r630787080, it sounds better to replace `sys.err` usages with explicit exception type.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#32535 from viirya/replace-sys-err.
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 group exception messages in `sql/core/src/main/scala/org/apache/spark/sql/streaming`.
### Why are the changes needed?
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.
Closes#32464 from beliefer/SPARK-35062.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
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 a new config to make cache plan disable configs configurable.
### Why are the changes needed?
The disable configs of cache plan if to avoid the perfermance regression, but not all the query will slow than before due to AQE or bucket scan enabled. It's useful to make a new config so that user can decide if some configs should be disabled during cache plan.
### Does this PR introduce _any_ user-facing change?
Yes, a new config.
### How was this patch tested?
Add test.
Closes#32482 from ulysses-you/SPARK-35332.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add New SQL functions:
* TRY_ADD
* TRY_DIVIDE
These expressions are identical to the following expression under ANSI mode except that it returns null if error occurs:
* ADD
* DIVIDE
Note: it is easy to add other expressions like `TRY_SUBTRACT`/`TRY_MULTIPLY` but let's control the number of these new expressions and just add `TRY_ADD` and `TRY_DIVIDE` for now.
### Why are the changes needed?
1. Users can manage to finish queries without interruptions in ANSI mode.
2. Users can get NULLs instead of unreasonable results if overflow occurs when ANSI mode is off.
For example, the behavior of the following SQL operations is unreasonable:
```
2147483647 + 2 => -2147483647
```
With the new safe version SQL functions:
```
TRY_ADD(2147483647, 2) => null
```
Note: **We should only add new expressions to important operators, instead of adding new safe expressions for all the expressions that can throw errors.**
### Does this PR introduce _any_ user-facing change?
Yes, new SQL functions: TRY_ADD/TRY_DIVIDE
### How was this patch tested?
Unit test
Closes#32292 from gengliangwang/try_add.
Authored-by: Gengliang Wang <ltnwgl@gmail.com>
Signed-off-by: Gengliang Wang <ltnwgl@gmail.com>
### What changes were proposed in this pull request?
In Spark, we have an extension in the MERGE syntax: INSERT/UPDATE *. This is not from ANSI standard or any other mainstream databases, so we need to define the behaviors by our own.
The behavior today is very weird: assume the source table has `n1` columns, target table has `n2` columns. We generate the assignments by taking the first `min(n1, n2)` columns from source & target tables and pairing them by ordinal.
This PR proposes a more reasonable behavior: take all the columns from target table as keys, and find the corresponding columns from source table by name as values.
### Why are the changes needed?
Fix the MEREG INSERT/UPDATE * to be more user-friendly and easy to do schema evolution.
### Does this PR introduce _any_ user-facing change?
Yes, but MERGE is only supported by very few data sources.
### How was this patch tested?
new tests
Closes#32192 from cloud-fan/merge.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change `map` in `InvokeLike.invoke` to a while loop to improve performance, following Spark [style guide](https://github.com/databricks/scala-style-guide#traversal-and-zipwithindex).
### Why are the changes needed?
`InvokeLike.invoke`, which is used in non-codegen path for `Invoke` and `StaticInvoke`, currently uses `map` to evaluate arguments:
```scala
val args = arguments.map(e => e.eval(input).asInstanceOf[Object])
if (needNullCheck && args.exists(_ == null)) {
// return null if one of arguments is null
null
} else {
...
```
which is pretty expensive if the method itself is trivial. We can change it to a plain while loop.
<img width="871" alt="Screen Shot 2021-05-12 at 12 19 59 AM" src="https://user-images.githubusercontent.com/506679/118055719-7f985a00-b33d-11eb-943b-cf85eab35f44.png">
Benchmark results show this can improve as much as 3x from `V2FunctionBenchmark`:
Before
```
OpenJDK 64-Bit Server VM 1.8.0_292-b10 on Linux 5.4.0-1046-azure
Intel(R) Xeon(R) CPU E5-2673 v3 2.40GHz
scalar function (long + long) -> long, result_nullable = false codegen = false: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
--------------------------------------------------------------------------------------------------------------------------------------------------------------
native_long_add 36506 36656 251 13.7 73.0 1.0X
java_long_add_default 47151 47540 370 10.6 94.3 0.8X
java_long_add_magic 178691 182457 1327 2.8 357.4 0.2X
java_long_add_static_magic 177151 178258 1151 2.8 354.3 0.2X
```
After
```
OpenJDK 64-Bit Server VM 1.8.0_292-b10 on Linux 5.4.0-1046-azure
Intel(R) Xeon(R) CPU E5-2673 v3 2.40GHz
scalar function (long + long) -> long, result_nullable = false codegen = false: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
--------------------------------------------------------------------------------------------------------------------------------------------------------------
native_long_add 29897 30342 568 16.7 59.8 1.0X
java_long_add_default 40628 41075 664 12.3 81.3 0.7X
java_long_add_magic 54553 54755 182 9.2 109.1 0.5X
java_long_add_static_magic 55410 55532 127 9.0 110.8 0.5X
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#32527 from sunchao/SPARK-35384.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Switch to plain `while` loop following Spark [style guide](https://github.com/databricks/scala-style-guide#traversal-and-zipwithindex).
### Why are the changes needed?
`while` loop may yield better performance comparing to `foreach`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#32522 from sunchao/SPARK-35361-follow-up.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
A simple follow-up of #32474 to throw exception instead of sys.error.
### Why are the changes needed?
An exception only fails the query, instead of sys.error.
### Does this PR introduce _any_ user-facing change?
Yes, if `Invoke` or `StaticInvoke` cannot find the method, instead of original `sys.error` now we only throw an exception.
### How was this patch tested?
Existing tests.
Closes#32519 from viirya/SPARK-35347-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?
This PR proposes to bump up the janino version from 3.0.16 to v3.1.4.
The major changes of this upgrade are as follows:
- Fixed issue #131: Janino 3.1.2 is 10x slower than 3.0.11: The Compiler's IClassLoader was initialized way too eagerly, thus lots of classes were loaded from the class path, which is very slow.
- Improved the encoding of stack map frames according to JVMS11 4.7.4: Previously, only "full_frame"s were generated.
- Fixed issue #107: Janino requires "org.codehaus.commons.compiler.io", but commons-compiler does not export this package
- Fixed the promotion of the array access index expression (see JLS7 15.13 Array Access Expressions).
For all the changes, please see the change log: http://janino-compiler.github.io/janino/changelog.html
NOTE1: I've checked that there is no obvious performance regression. For all the data, see a link: https://docs.google.com/spreadsheets/d/1srxT9CioGQg1fLKM3Uo8z1sTzgCsMj4pg6JzpdcG6VU/edit?usp=sharing
NOTE2: We upgraded janino to 3.1.2 (#27860) once before, but the commit had been reverted in #29495 because of the correctness issue. Recently, #32374 had checked if Spark could land on v3.1.3 or not, but a new bug was found there. These known issues has been fixed in v3.1.4 by following PRs:
- janino-compiler/janino#145
- janino-compiler/janino#146
### Why are the changes needed?
janino v3.0.X is no longer maintained.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
GA passed.
Closes#32455 from maropu/janino_v3.1.4.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
According to discuss https://github.com/apache/spark/pull/25854#discussion_r629451135
### Why are the changes needed?
Clean code
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existed UT
Closes#32499 from AngersZhuuuu/SPARK-29145-fix.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In `ApplyFunctionExpression`, move `zipWithIndex` out of the loop for each input row.
### Why are the changes needed?
When the `ScalarFunction` is trivial, `zipWithIndex` could incur significant costs, as shown below:
<img width="899" alt="Screen Shot 2021-05-11 at 10 03 42 AM" src="https://user-images.githubusercontent.com/506679/117866421-fb19de80-b24b-11eb-8c94-d5e8c8b1eda9.png">
By removing it out of the loop, I'm seeing sometimes 2x speedup from `V2FunctionBenchmark`. For instance:
Before:
```
scalar function (long + long) -> long, result_nullable = false codegen = false: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
native_long_add 32437 32896 434 15.4 64.9 1.0X
java_long_add_default 85675 97045 NaN 5.8 171.3 0.4X
```
After:
```
scalar function (long + long) -> long, result_nullable = false codegen = false: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
native_long_add 30182 30387 279 16.6 60.4 1.0X
java_long_add_default 42862 43009 209 11.7 85.7 0.7X
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests
Closes#32507 from sunchao/SPARK-35361.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Added the following TreePattern enums:
- BOOL_AGG
- COUNT_IF
- CURRENT_LIKE
- RUNTIME_REPLACEABLE
Added tree traversal pruning to the following rules:
- ReplaceExpressions
- RewriteNonCorrelatedExists
- ComputeCurrentTime
- GetCurrentDatabaseAndCatalog
### Why are the changes needed?
Reduce the number of tree traversals and hence improve the query compilation latency.
Performance improvement (org.apache.spark.sql.TPCDSQuerySuite):
Rule name | Total Time (baseline) | Total Time (experiment) | experiment/baseline
ReplaceExpressions | 27546369 | 19753804 | 0.72
RewriteNonCorrelatedExists | 17304883 | 2086194 | 0.12
ComputeCurrentTime | 35751301 | 19984477 | 0.56
GetCurrentDatabaseAndCatalog | 37230787 | 18874013 | 0.51
### How was this patch tested?
Existing tests.
Closes#32461 from sigmod/finish_analysis.
Authored-by: Yingyi Bu <yingyi.bu@databricks.com>
Signed-off-by: Gengliang Wang <ltnwgl@gmail.com>
### What changes were proposed in this pull request?
Sequence expression output a message looks confused.
This PR will fix the issue.
### Why are the changes needed?
Improve the error message for Sequence expression
### Does this PR introduce _any_ user-facing change?
Yes. this PR updates the error message of Sequence expression.
### How was this patch tested?
Tests updated.
Closes#32492 from beliefer/SPARK-35088-followup.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Change the definition of `findTightestCommonType` from
```
def findTightestCommonType(t1: DataType, t2: DataType): Option[DataType]
```
to
```
val findTightestCommonType: (DataType, DataType) => Option[DataType]
```
### Why are the changes needed?
For backward compatibility.
When running a MongoDB connector (built with Spark 3.1.1) with the latest master, there is such an error
```
java.lang.NoSuchMethodError: org.apache.spark.sql.catalyst.analysis.TypeCoercion$.findTightestCommonType()Lscala/Function2
```
from https://github.com/mongodb/mongo-spark/blob/master/src/main/scala/com/mongodb/spark/sql/MongoInferSchema.scala#L150
In the previous release, the function was
```
static public scala.Function2<org.apache.spark.sql.types.DataType, org.apache.spark.sql.types.DataType, scala.Option<org.apache.spark.sql.types.DataType>> findTightestCommonType ()
```
After https://github.com/apache/spark/pull/31349, the function becomes:
```
static public scala.Option<org.apache.spark.sql.types.DataType> findTightestCommonType (org.apache.spark.sql.types.DataType t1, org.apache.spark.sql.types.DataType t2)
```
This PR is to reduce the unnecessary API change.
### Does this PR introduce _any_ user-facing change?
Yes, the definition of `TypeCoercion.findTightestCommonType` is consistent with previous release again.
### How was this patch tested?
Existing unit tests
Closes#32493 from gengliangwang/typecoercion.
Authored-by: Gengliang Wang <ltnwgl@gmail.com>
Signed-off-by: Gengliang Wang <ltnwgl@gmail.com>
### What changes were proposed in this pull request?
RepairTableCommand respects `spark.sql.addPartitionInBatch.size` too
### Why are the changes needed?
Make RepairTableCommand add partition batch size configurable.
### Does this PR introduce _any_ user-facing change?
User can use `spark.sql.addPartitionInBatch.size` to change batch size when repair table.
### How was this patch tested?
Not need
Closes#32489 from AngersZhuuuu/SPARK-35360.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Rename pattern strings and regexps of year-month and day-time intervals.
### Why are the changes needed?
To improve code maintainability.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By existing test suites.
Closes#32444 from AngersZhuuuu/SPARK-35111-followup.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
when `numSlices` is avaiable, `logical.Range` should compute a exact `maxRowsPerPartition`
### Why are the changes needed?
`maxRowsPerPartition` is used in optimizer, we should provide an exact value if possible
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#32350 from zhengruifeng/range_maxRowsPerPartition.
Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This patch proposes to use `MethodUtils` for looking up methods `Invoke` and `StaticInvoke` expressions.
### Why are the changes needed?
Currently we wrote our logic in `Invoke` and `StaticInvoke` expressions for looking up methods. It is tricky to consider all the cases and there is already existing utility package for this purpose. We should reuse the utility package.
### Does this PR introduce _any_ user-facing change?
No, internal change only.
### How was this patch tested?
Existing tests.
Closes#32474 from viirya/invoke-util.
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 makes the below case work well.
```sql
select a b from values(1) t(a) distribute by a;
```
```logtalk
== Parsed Logical Plan ==
'RepartitionByExpression ['a]
+- 'Project ['a AS b#42]
+- 'SubqueryAlias t
+- 'UnresolvedInlineTable [a], [List(1)]
== Analyzed Logical Plan ==
org.apache.spark.sql.AnalysisException: cannot resolve 'a' given input columns: [b]; line 1 pos 62;
'RepartitionByExpression ['a]
+- Project [a#48 AS b#42]
+- SubqueryAlias t
+- LocalRelation [a#48]
```
### Why are the changes needed?
bugfix
### Does this PR introduce _any_ user-facing change?
yes, the original attributes can be used in `distribute by` / `cluster by` and hints like `/*+ REPARTITION(3, c) */`
### How was this patch tested?
new tests
Closes#32465 from yaooqinn/SPARK-35331.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Retain column metadata during the process of nested column pruning, when constructing `StructField`.
To test the above change, this also added the logic of column projection in `InMemoryTable`. Without the fix `DSV2CharVarcharDDLTestSuite` will fail.
### Why are the changes needed?
The column metadata is used in a few places such as re-constructing CHAR/VARCHAR information such as in [SPARK-33901](https://issues.apache.org/jira/browse/SPARK-33901). Therefore, we should retain the info during nested column pruning.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#32354 from sunchao/SPARK-35232.
Authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This allows `ScalarFunction` implemented in Java to optionally specify the magic method `invoke` to be static, which can be used if the UDF is stateless. Comparing to the non-static method, it can potentially give better performance due to elimination of dynamic dispatch, etc.
Also added a benchmark to measure performance of: the default `produceResult`, non-static magic method and static magic method.
### Why are the changes needed?
For UDFs that are stateless (e.g., no need to maintain intermediate state between each function call), it's better to allow users to implement the UDF function as static method which could potentially give better performance.
### Does this PR introduce _any_ user-facing change?
Yes. Spark users can now have the choice to define static magic method for `ScalarFunction` when it is written in Java and when the UDF is stateless.
### How was this patch tested?
Added new UT.
Closes#32407 from sunchao/SPARK-35261.
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 StaticInvoke able to find method with given method name even the parameter types do not exactly match to argument classes.
### Why are the changes needed?
Unlike `Invoke`, `StaticInvoke` only tries to get the method with exact argument classes. If the calling method's parameter types are not exactly matched with the argument classes, `StaticInvoke` cannot find the method.
`StaticInvoke` should be able to find the method under the cases too.
### Does this PR introduce _any_ user-facing change?
Yes. `StaticInvoke` can find a method even the argument classes are not exactly matched.
### How was this patch tested?
Unit test.
Closes#32413 from viirya/static-invoke.
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?
This PR group exception messages in `sql/catalyst/src/main/scala/org/apache/spark/sql/connector/catalog`.
### Why are the changes needed?
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.
Closes#32377 from beliefer/SPARK-35021.
Lead-authored-by: beliefer <beliefer@163.com>
Co-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Added the following TreePattern enums:
- APPEND_COLUMNS
- DESERIALIZE_TO_OBJECT
- LAMBDA_VARIABLE
- MAP_OBJECTS
- SERIALIZE_FROM_OBJECT
- PROJECT
- TYPED_FILTER
Added tree traversal pruning to the following rules dealing with objects:
- EliminateSerialization
- CombineTypedFilters
- EliminateMapObjects
- ObjectSerializerPruning
### Why are the changes needed?
Reduce the number of tree traversals and hence improve the query compilation latency.
### How was this patch tested?
Existing tests.
Closes#32451 from sigmod/object.
Authored-by: Yingyi Bu <yingyi.bu@databricks.com>
Signed-off-by: Gengliang Wang <ltnwgl@gmail.com>
### What changes were proposed in this pull request?
If `targetObject` is not nullable, we don't need the object null check in `Invoke`.
### Why are the changes needed?
small perf improvement
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests
Closes#32466 from cloud-fan/invoke.
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 group exception messages in `sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util`.
### Why are the changes needed?
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.
Closes#32367 from beliefer/SPARK-35020.
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 is a follow up to https://github.com/apache/spark/pull/32032#discussion_r620928086. Basically, `children`/`innerChildren` should be mutually exclusive for `AlterViewAsCommand` and `CreateViewCommand`, which extend `AnalysisOnlyCommand`. Otherwise, there could be an issue in the `EXPLAIN` command. Currently, this is not an issue, because these commands will be analyzed (children will always be empty) when the `EXPLAIN` command is run.
### Why are the changes needed?
To be future-proof where these commands are directly used.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added new tsts
Closes#32447 from imback82/SPARK-34701-followup.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Hide internal view properties for describe table command, because those
properties are generated by spark and should be transparent to the end-user.
### Why are the changes needed?
Avoid internal properties confusing the users.
### Does this PR introduce _any_ user-facing change?
Yes
Before this change, the user will see below output for `describe formatted test_view`
```
....
Table Properties [view.catalogAndNamespace.numParts=2, view.catalogAndNamespace.part.0=spark_catalog, view.catalogAndNamespace.part.1=default, view.query.out.col.0=c, view.query.out.col.1=v, view.query.out.numCols=2, view.referredTempFunctionsNames=[], view.referredTempViewNames=[]]
...
```
After this change, the internal properties will be hidden for `describe formatted test_view`
```
...
Table Properties []
...
```
### How was this patch tested?
existing UT
Closes#32441 from linhongliu-db/hide-properties.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
