### What changes were proposed in this pull request?
Add allow list to capture sql config for view.
### Why are the changes needed?
Spark use origin text sql to store view then capture and store sql config into view metadata.
Capture config will skip some config with some prefix, e.g. `spark.sql.optimizer.` but unfortunately `spark.sql.optimizer.disableHints` is start with `spark.sql.optimizer.`.
We need a allow list to help capture the config.
### Does this PR introduce _any_ user-facing change?
Yes bug fix.
### How was this patch tested?
Add test.
Closes#31732 from ulysses-you/SPARK-34613.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
We have equality in `SqlBase.g4` for `RLIKE: 'RLIKE' | 'REGEXP';`
We seemed to miss adding` REGEXP` as a SQL function just like` RLIKE`
### Why are the changes needed?
symmetry and beauty
This is also a builtin function in Hive, we can reduce the migration pain for those users
### Does this PR introduce _any_ user-facing change?
yes new regexp function as an alias as rlike
### How was this patch tested?
new tests
Closes#31488 from yaooqinn/SPARK-34376.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR intends to fix a bug of `objects.NewInstance` if a user runs Spark on jdk8u and a given `cls` in `NewInstance` is a deeply-nested inner class, e.g.,.
```
object OuterLevelWithVeryVeryVeryLongClassName1 {
object OuterLevelWithVeryVeryVeryLongClassName2 {
object OuterLevelWithVeryVeryVeryLongClassName3 {
object OuterLevelWithVeryVeryVeryLongClassName4 {
object OuterLevelWithVeryVeryVeryLongClassName5 {
object OuterLevelWithVeryVeryVeryLongClassName6 {
object OuterLevelWithVeryVeryVeryLongClassName7 {
object OuterLevelWithVeryVeryVeryLongClassName8 {
object OuterLevelWithVeryVeryVeryLongClassName9 {
object OuterLevelWithVeryVeryVeryLongClassName10 {
object OuterLevelWithVeryVeryVeryLongClassName11 {
object OuterLevelWithVeryVeryVeryLongClassName12 {
object OuterLevelWithVeryVeryVeryLongClassName13 {
object OuterLevelWithVeryVeryVeryLongClassName14 {
object OuterLevelWithVeryVeryVeryLongClassName15 {
object OuterLevelWithVeryVeryVeryLongClassName16 {
object OuterLevelWithVeryVeryVeryLongClassName17 {
object OuterLevelWithVeryVeryVeryLongClassName18 {
object OuterLevelWithVeryVeryVeryLongClassName19 {
object OuterLevelWithVeryVeryVeryLongClassName20 {
case class MalformedNameExample2(x: Int)
}}}}}}}}}}}}}}}}}}}}
```
The root cause that Kris (rednaxelafx) investigated is as follows (Kudos to Kris);
The reason why the test case above is so convoluted is in the way Scala generates the class name for nested classes. In general, Scala generates a class name for a nested class by inserting the dollar-sign ( `$` ) in between each level of class nesting. The problem is that this format can concatenate into a very long string that goes beyond certain limits, so Scala will change the class name format beyond certain length threshold.
For the example above, we can see that the first two levels of class nesting have class names that look like this:
```
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassName1$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassName1$OuterLevelWithVeryVeryVeryLongClassName2$
```
If we leave out the fact that Scala uses a dollar-sign ( `$` ) suffix for the class name of the companion object, `OuterLevelWithVeryVeryVeryLongClassName1`'s full name is a prefix (substring) of `OuterLevelWithVeryVeryVeryLongClassName2`.
But if we keep going deeper into the levels of nesting, you'll find names that look like:
```
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$2a1321b953c615695d7442b2adb1$$$$ryVeryLongClassName8$OuterLevelWithVeryVeryVeryLongClassName9$OuterLevelWithVeryVeryVeryLongClassName10$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$2a1321b953c615695d7442b2adb1$$$$ryVeryLongClassName8$OuterLevelWithVeryVeryVeryLongClassName9$OuterLevelWithVeryVeryVeryLongClassName10$OuterLevelWithVeryVeryVeryLongClassName11$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$85f068777e7ecf112afcbe997d461b$$$$VeryLongClassName11$OuterLevelWithVeryVeryVeryLongClassName12$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$85f068777e7ecf112afcbe997d461b$$$$VeryLongClassName11$OuterLevelWithVeryVeryVeryLongClassName12$OuterLevelWithVeryVeryVeryLongClassName13$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$85f068777e7ecf112afcbe997d461b$$$$VeryLongClassName11$OuterLevelWithVeryVeryVeryLongClassName12$OuterLevelWithVeryVeryVeryLongClassName13$OuterLevelWithVeryVeryVeryLongClassName14$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$5f7ad51804cb1be53938ea804699fa$$$$VeryLongClassName14$OuterLevelWithVeryVeryVeryLongClassName15$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$5f7ad51804cb1be53938ea804699fa$$$$VeryLongClassName14$OuterLevelWithVeryVeryVeryLongClassName15$OuterLevelWithVeryVeryVeryLongClassName16$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$5f7ad51804cb1be53938ea804699fa$$$$VeryLongClassName14$OuterLevelWithVeryVeryVeryLongClassName15$OuterLevelWithVeryVeryVeryLongClassName16$OuterLevelWithVeryVeryVeryLongClassName17$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$69b54f16b1965a31e88968df1a58d8$$$$VeryLongClassName17$OuterLevelWithVeryVeryVeryLongClassName18$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$69b54f16b1965a31e88968df1a58d8$$$$VeryLongClassName17$OuterLevelWithVeryVeryVeryLongClassName18$OuterLevelWithVeryVeryVeryLongClassName19$
org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite$OuterLevelWithVeryVeryVeryLongClassNam$$$$69b54f16b1965a31e88968df1a58d8$$$$VeryLongClassName17$OuterLevelWithVeryVeryVeryLongClassName18$OuterLevelWithVeryVeryVeryLongClassName19$OuterLevelWithVeryVeryVeryLongClassName20$
```
with a hash code in the middle and various levels of nesting omitted.
The `java.lang.Class.isMemberClass` method is implemented in JDK8u as:
http://hg.openjdk.java.net/jdk8u/jdk8u/jdk/file/tip/src/share/classes/java/lang/Class.java#l1425
```
/**
* Returns {code true} if and only if the underlying class
* is a member class.
*
* return {code true} if and only if this class is a member class.
* since 1.5
*/
public boolean isMemberClass() {
return getSimpleBinaryName() != null && !isLocalOrAnonymousClass();
}
/**
* Returns the "simple binary name" of the underlying class, i.e.,
* the binary name without the leading enclosing class name.
* Returns {code null} if the underlying class is a top level
* class.
*/
private String getSimpleBinaryName() {
Class<?> enclosingClass = getEnclosingClass();
if (enclosingClass == null) // top level class
return null;
// Otherwise, strip the enclosing class' name
try {
return getName().substring(enclosingClass.getName().length());
} catch (IndexOutOfBoundsException ex) {
throw new InternalError("Malformed class name", ex);
}
}
```
and the problematic code is `getName().substring(enclosingClass.getName().length())` -- if a class's enclosing class's full name is *longer* than the nested class's full name, this logic would end up going out of bounds.
The bug has been fixed in JDK9 by https://bugs.java.com/bugdatabase/view_bug.do?bug_id=8057919 , but still exists in the latest JDK8u release. So from the Spark side we'd need to do something to avoid hitting this problem.
### Why are the changes needed?
Bugfix on jdk8u.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added tests.
Closes#31733 from maropu/SPARK-34607.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Make the "duration" column in standalone mode master UI sorted by numeric duration, hence the column can be sorted by the correct order.
Before changes:
After changes:
### Why are the changes needed?
Fix a UI bug to make the sorting consistent across different pages.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Ran several apps with different durations and verified the duration column on the master page can be sorted correctly.
Closes#31743 from baohe-zhang/SPARK-32924.
Authored-by: Baohe Zhang <baohe.zhang@verizonmedia.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose to extend Spark SQL API to accept [`java.time.Duration`](https://docs.oracle.com/javase/8/docs/api/java/time/Duration.html) as an external type of recently added new Catalyst type - `DayTimeIntervalType` (see #31614). The Java class `java.time.Duration` has similar semantic to ANSI SQL day-time interval type, and it is the most suitable to be an external type for `DayTimeIntervalType`. In more details:
1. Added `DurationConverter` which converts `java.time.Duration` instances to/from internal representation of the Catalyst type `DayTimeIntervalType` (to `Long` type). The `DurationConverter` object uses new methods of `IntervalUtils`:
- `durationToMicros()` converts the input duration to the total length in microseconds. If this duration is too large to fit `Long`, the method throws the exception `ArithmeticException`. **Note:** _the input duration has nanosecond precision, the method casts the nanos part to microseconds by dividing by 1000._
- `microsToDuration()` obtains a `java.time.Duration` representing a number of microseconds.
2. Support new type `DayTimeIntervalType` in `RowEncoder` via the methods `createDeserializerForDuration()` and `createSerializerForJavaDuration()`.
3. Extended the Literal API to construct literals from `java.time.Duration` instances.
### Why are the changes needed?
1. To allow users parallelization of `java.time.Duration` collections, and construct day-time interval columns. Also to collect such columns back to the driver side.
2. This will allow to write tests in other sub-tasks of SPARK-27790.
### Does this PR introduce _any_ user-facing change?
The PR extends existing functionality. So, users can parallelize instances of the `java.time.Duration` class and collect them back:
```Scala
scala> val ds = Seq(java.time.Duration.ofDays(10)).toDS
ds: org.apache.spark.sql.Dataset[java.time.Duration] = [value: daytimeinterval]
scala> ds.collect
res0: Array[java.time.Duration] = Array(PT240H)
```
### How was this patch tested?
- Added a few tests to `CatalystTypeConvertersSuite` to check conversion from/to `java.time.Duration`.
- Checking row encoding by new tests in `RowEncoderSuite`.
- Making literals of `DayTimeIntervalType` are tested in `LiteralExpressionSuite`
- Check collecting by `DatasetSuite` and `JavaDatasetSuite`.
Closes#31729 from MaxGekk/java-time-duration.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Set the active SparkSession to `sparkSessionForStream` and diable AQE & CBO before initializing the `StreamExecution.logicalPlan`.
### Why are the changes needed?
The active session should be `sparkSessionForStream`. Otherwise, settings like
6b34745cb9/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala (L332-L335)
wouldn't take effect if callers access them from the active SQLConf, e.g., the rule of `InsertAdaptiveSparkPlan`. Besides, unlike `InsertAdaptiveSparkPlan` (which skips streaming plan), `CostBasedJoinReorder` seems to have the chance to take effect theoretically.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Tested manually. Before the fix, `InsertAdaptiveSparkPlan` would try to apply AQE on the plan(wouldn't take effect though). After this fix, the rule returns directly.
Closes#31600 from Ngone51/active-session-for-stream.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In ANSI mode, casting String to Boolean should throw an exception on parse error, instead of returning null
### Why are the changes needed?
For better ANSI compliance
### Does this PR introduce _any_ user-facing change?
Yes, in ANSI mode there will be an exception on parse failure of casting String value to Boolean type.
### How was this patch tested?
Unit tests.
Closes#31734 from gengliangwang/ansiCastToBoolean.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
`ResolveInsertInto.staticDeleteExpression` should use `UnresolvedAttribute.quoted` to create the delete expression so that we will treat the entire `attr.name` as a column name.
### Why are the changes needed?
When users use `dot` in a partition column name, queries like ```INSERT OVERWRITE $t1 PARTITION (`a.b` = 'a') (`c.d`) VALUES('b')``` is not working.
### Does this PR introduce _any_ user-facing change?
Without this test, the above query will throw
```
[info] org.apache.spark.sql.AnalysisException: cannot resolve '`a.b`' given input columns: [a.b, c.d];
[info] 'OverwriteByExpression RelationV2[a.b#17, c.d#18] default.tbl, ('a.b <=> cast(a as string)), false
[info] +- Project [a.b#19, ansi_cast(col1#16 as string) AS c.d#20]
[info] +- Project [cast(a as string) AS a.b#19, col1#16]
[info] +- LocalRelation [col1#16]
```
With the fix, the query will run correctly.
### How was this patch tested?
The new added test.
Closes#31713 from zsxwing/SPARK-34599.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to generate "stable" output attributes per the logical node of the DESCRIBE NAMESPACE command.
### Why are the changes needed?
This fixes the issue demonstrated by the example:
```
sql(s"CREATE NAMESPACE ns")
val description = sql(s"DESCRIBE NAMESPACE ns")
description.drop("name")
```
```
[info] org.apache.spark.sql.AnalysisException: Resolved attribute(s) name#74 missing from name#25,value#26 in operator !Project [name#74]. Attribute(s) with the same name appear in the operation: name. Please check if the right attribute(s) are used.;
[info] !Project [name#74]
[info] +- LocalRelation [name#25, value#26]
```
### Does this PR introduce _any_ user-facing change?
After this change user `drop()/add()` works well.
### How was this patch tested?
Added UT
Closes#31705 from AngersZhuuuu/SPARK-34577.
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 followup of https://github.com/apache/spark/pull/27597 and simply apply the fix in the v2 table insertion code path.
### Why are the changes needed?
bug fix
### Does this PR introduce _any_ user-facing change?
yes, now v2 table insertion with static partitions also follow StoreAssignmentPolicy.
### How was this patch tested?
moved the test from https://github.com/apache/spark/pull/27597 to the general test suite `SQLInsertTestSuite`, which covers DS v2, file source, and hive tables.
Closes#31726 from cloud-fan/insert.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Fixes a Python UDF `plus_one` used in `GroupedAggPandasUDFTests` to always return float (double) values.
### Why are the changes needed?
The Python UDF `plus_one` used in `GroupedAggPandasUDFTests` is always returning `v + 1` regardless of its type. The return type of the UDF is 'double', so if the input is int, the result will be `null`.
```py
>>> df = spark.range(10).toDF('id') \
... .withColumn("vs", array([lit(i * 1.0) + col('id') for i in range(20, 30)])) \
... .withColumn("v", explode(col('vs'))) \
... .drop('vs') \
... .withColumn('w', lit(1.0))
>>> udf('double')
... def plus_one(v):
... assert isinstance(v, (int, float))
... return v + 1
...
>>> pandas_udf('double', PandasUDFType.GROUPED_AGG)
... def sum_udf(v):
... return v.sum()
...
>>> df.groupby(plus_one(df.id)).agg(sum_udf(df.v)).show()
+------------+----------+
|plus_one(id)|sum_udf(v)|
+------------+----------+
| null| 2900.0|
+------------+----------+
```
This is meaningless and should be:
```py
>>> udf('double')
... def plus_one(v):
... assert isinstance(v, (int, float))
... return float(v + 1)
...
>>> df.groupby(plus_one(df.id)).agg(sum_udf(df.v)).sort('plus_one(id)').show()
+------------+----------+
|plus_one(id)|sum_udf(v)|
+------------+----------+
| 1.0| 245.0|
| 2.0| 255.0|
| 3.0| 265.0|
| 4.0| 275.0|
| 5.0| 285.0|
| 6.0| 295.0|
| 7.0| 305.0|
| 8.0| 315.0|
| 9.0| 325.0|
| 10.0| 335.0|
+------------+----------+
```
### Does this PR introduce _any_ user-facing change?
No, test-only.
### How was this patch tested?
Fixed the test.
Closes#31730 from ueshin/issues/SPARK-34610/test_pandas_udf_grouped_agg.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add the `ResetSystemProperties` trait to `SQLQuerySuite` so that system property changes made by any of the tests will not affect other suites/tests. Specifically, the system property changes made by `SPARK-33084: Add jar support Ivy URI in SQL -- jar contains udf class` are targeted here (which sets and then clears `ivy.home`).
### Why are the changes needed?
PR #29966 added a new test case that adjusts the `ivy.home` system property to force Ivy to resolve an artifact from a custom location. At the end of the test, the value is cleared. Clearing the value meant that, if a custom value of `ivy.home` was configured externally, it would not apply for tests run after this test case.
### Does this PR introduce _any_ user-facing change?
No, this is only in tests.
### How was this patch tested?
Existing unit tests continue to pass, whether or not `spark.jars.ivySettings` is configured (which adjusts the behavior of Ivy w.r.t. handling of `ivy.home` and `ivy.default.ivy.user.dir` properties).
Closes#31694 from xkrogen/xkrogen-SPARK-33084-ivyhome-sysprop-followon.
Authored-by: Erik Krogen <xkrogen@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
`TaskContextTestsWithWorkerReuse.test_task_context_correct_with_python_worker_reuse` can be flaky and fails sometimes:
```
======================================================================
ERROR [1.798s]: test_task_context_correct_with_python_worker_reuse (pyspark.tests.test_taskcontext.TaskContextTestsWithWorkerReuse)
...
test_task_context_correct_with_python_worker_reuse
self.assertTrue(pid in worker_pids)
AssertionError: False is not true
----------------------------------------------------------------------
```
I suspect that the Python worker was killed for whatever reason and new attempt created a new Python worker.
This PR fixes the flakiness simply by retrying the test case.
### Why are the changes needed?
To make the tests more robust.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
Manually tested it by controlling the conditions manually in the test codes.
Closes#31723 from HyukjinKwon/SPARK-34604.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes a new feature that allows developers to debug test code using JDWP with sbt an Maven.
More specifically, this PR introduces the following profile options.
* `jdwp-test-debug`: An profile which controls enable/disable JDWP debug
* `test.jdwp.address`: An option which corresponds to `address` option in JDWP
* `test.jdwp.suspend`: An option which corresponds to `suspend` option in JDWP
* `test.jdwp.server`: An option which corresponds to `server` option in JDWP
* `test.debug.suite`: An option which controls whether debug ScalaStyle suites (Maven only)
For `sbt`, this feature can be used like `build/sbt -Pjdwp-test-debug -Dtest.jdwp.address=localhost:9876 -Dtest.jdwp.suspend=y -Dtest.jdwp.server=y` and can be used for both JUnit tests and ScalaTest tests.
For `Maven`, this feature can be used like as follows:
(For JUnit tests) `build/mvn -Pjdwp-test-debug -Dtest.jdwp.address=localhost:9876 -Dtest.jdwp.suspend=y -Dtest.jdwp.server=y`
(For ScalaTest suites) `build/mvn -Pjdwp-test-debug -Dtest.debug.suite=true -Dtest.jdwp.address=localhost:9876 -Dtest.jdwp.suspend=y -Dtest.jdwp.server=y` (It might be useful to specify specific sub-modules like `-pl sql/core,sql/catalyst`).
### Why are the changes needed?
It's useful to debug test code.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I confirmed the following things.
* `jdwp-tes-debug` can switch JDWP enabled/disabled
* `test.jdwp.address` can change address and port.
* `test.jdwp.suspend` can change the behavior that the target debugee suspends or not.
* `test.jdwp.server` can change the behavior that the JDWP debugger run as a server or client.
* ScalaTest suites can be debugged with Maven with setting `test.debug.suite` to `true`.
Closes#31706 from sarutak/sbt-jdwp.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Use a non-recursive implementation for the function buildBalancedPredicate
### Why are the changes needed?
For better performance.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing unit tests.
Also, a quick benchmark:
```
test("buildBalancedPredicate") {
val expressions = (1 to 1000).map(_ => Literal(true))
val start = System.currentTimeMillis()
buildBalancedPredicate(expressions, And)
println(System.currentTimeMillis() - start)
}
```
Before: 47ms
After: 4ms
Closes#31724 from gengliangwang/nonrecursive.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
Add metadataOutput as a fallback to resolution.
Builds off https://github.com/apache/spark/pull/31654.
### Why are the changes needed?
The metadata columns could not be resolved via `df.col("metadataColName")` from the DataFrame API.
### Does this PR introduce _any_ user-facing change?
Yes, the metadata columns can now be resolved as described above.
### How was this patch tested?
Scala unit test.
Closes#31668 from karenfeng/spark-34555.
Authored-by: Karen Feng <karen.feng@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Hive support type constructed value as partition spec value, spark should support too.
### Why are the changes needed?
Support TypeConstructed partition spec value keep same with hive
### Does this PR introduce _any_ user-facing change?
Yes, user can use TypeConstruct value as partition spec value such as
```
CREATE TABLE t1(name STRING) PARTITIONED BY (part DATE)
INSERT INTO t1 PARTITION(part = date'2019-01-02') VALUES('a')
CREATE TABLE t2(name STRING) PARTITIONED BY (part TIMESTAMP)
INSERT INTO t2 PARTITION(part = timestamp'2019-01-02 11:11:11') VALUES('a')
CREATE TABLE t4(name STRING) PARTITIONED BY (part BINARY)
INSERT INTO t4 PARTITION(part = X'537061726B2053514C') VALUES('a')
```
### How was this patch tested?
Added UT
Closes#30421 from AngersZhuuuu/SPARK-33474.
Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
spark.sql.adaptive.coalescePartitions.initialPartitionNum 200 -> (none)
spark.sql.adaptive.skewJoin.skewedPartitionFactor is 10 -> 5
### Why are the changes needed?
the wrong doc misguide people
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
passing doc
Closes#31717 from yaooqinn/minordoc0.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Enhance boolean simplification rule by handling following scenarios:
(((a && b) && a && (a && c))) => a && b && c)
(((a || b) || a || (a || c))) => a || b || c
### Why are the changes needed?
Minor improvement
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UTs
Closes#31318 from Swinky/booleansimplification.
Authored-by: Swinky <mannswinky@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to generate "stable" output attributes per the logical node of the DESCRIBE COLUMN command.
### Why are the changes needed?
This fixes the issue demonstrated by the example:
```
val tbl = "testcat.ns1.ns2.tbl"
sql(s"CREATE TABLE $tbl (c0 INT) USING _")
val description = sql(s"DESCRIBE TABLE $tbl c0")
description.drop("info_name")
```
```
[info] org.apache.spark.sql.AnalysisException: Resolved attribute(s) info_name#74 missing from info_name#25,info_value#26 in operator !Project [info_name#74]. Attribute(s) with the same name appear in the operation: info_name. Please check if the right attribute(s) are used.;
[info] !Project [info_name#74]
[info] +- LocalRelation [info_name#25, info_value#26]
```
### Does this PR introduce _any_ user-facing change?
After this change user `drop()/add()` works well.
### How was this patch tested?
Added UT
Closes#31696 from AngersZhuuuu/SPARK-34576.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to extend Catalyst's type system by two new types that conform to the SQL standard (see SQL:2016, section 4.6.3):
- `DayTimeIntervalType` represents the day-time interval type,
- `YearMonthIntervalType` for SQL year-month interval type.
This PR only adds the two new DataType implementations, and there will be more PRs as sub-tasks of SPARK-27790 to completely support the new ANSI interval types.
### Why are the changes needed?
Spark as it is today supports an INTERVAL datatype. However this type is of very limited use. Existing interval values cannot be compared with any other interval values, or persisted to storage. Spark users request to either implement new or expand existing built-in functions which produce some sort of measures for elapsed time, such as `DATEDIFF()`. Rather than work around the edges to fill the potholes of the existing INTERVAL data type, I would like to propose to deliver a proper ANSI compliant INTERVAL type that can be introduced with minimal incompatibility, is comparable and thus sortable, and can be persisted in tables.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
1. By checking coding style via:
```
$ ./dev/scalastyle
$ ./dev/lint-java
```
2. Run the test for the default sizes:
```
$ build/sbt "test:testOnly *DataTypeSuite"
```
Closes#31614 from MaxGekk/day-time-interval-type.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`BroadcastNestedLoopJoinExec` does not preserve `outputPartitioning` and `outputOrdering` right now. But it can preserve the streamed side partitioning and ordering when possible. This can help avoid shuffle and sort in later stage, if there's join and aggregation in the query. See example queries in added unit test in `JoinSuite.scala`.
In addition, fix a bunch of minor places in `BroadcastNestedLoopJoinExec.scala` for better style and readability.
### Why are the changes needed?
Avoid shuffle and sort for certain complicated query shape. Better query performance can be achieved.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit test in `JoinSuite.scala`.
Closes#31708 from c21/nested-join.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Fix a problems which can lead to data correctness after part blocks retry in `OneForOneBlockFetcher` when use `FetchShuffleBlocks` .
### Why are the changes needed?
This is a data correctness bug, It's is no problems when use old protocol to send `OpenBlocks` before fetch chunks in `OneForOneBlockFetcher`;
In latest branch, `OpenBlocks` has been replaced to `FetchShuffleBlocks`. Howerver, `FetchShuffleBlocks` read shuffle blocks order is not the same as `blockIds` in `OneForOneBlockFetcher`; the `blockIds` is used to match blockId with shuffle data with index, now it is out of order;
It will lead to read wrong block chunk when some blocks fetch failed in `OneForOneBlockFetcher`, it will retry the rest of the blocks in `blockIds` based on the `blockIds`'s order.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Closes#31643 from seayoun/yuhaiyang_fix_use_FetchShuffleBlocks_order.
Lead-authored-by: yuhaiyang <yuhaiyang@yuhaiyangs-MacBook-Pro.local>
Co-authored-by: yuhaiyang <yuhaiyang@172.19.25.126>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Use `Utils.getSimpleName` to avoid hitting `Malformed class name` error in `NewInstance.doGenCode`.
### 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.
In this particular case, creating an `ExpressionEncoder` on such a nested Scala class would create a `NewInstance` expression under the hood, which will trigger the problem during codegen.
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.
There are two other occurrences of `java.lang.Class.getSimpleName` in the same file, but they're safe because they're only guaranteed to be only used on Java classes, which don't have this problem, e.g.:
```scala
// Make a copy of the data if it's unsafe-backed
def makeCopyIfInstanceOf(clazz: Class[_ <: Any], value: String) =
s"$value instanceof ${clazz.getSimpleName}? ${value}.copy() : $value"
val genFunctionValue: String = lambdaFunction.dataType match {
case StructType(_) => makeCopyIfInstanceOf(classOf[UnsafeRow], genFunction.value)
case ArrayType(_, _) => makeCopyIfInstanceOf(classOf[UnsafeArrayData], genFunction.value)
case MapType(_, _, _) => makeCopyIfInstanceOf(classOf[UnsafeMapData], genFunction.value)
case _ => genFunction.value
}
```
The Unsafe-* family of types are all Java types, so they're okay.
### Does this PR introduce _any_ user-facing change?
Fixes a bug that throws an error when using `ExpressionEncoder` on some nested Scala types, otherwise no changes.
### How was this patch tested?
Added a test case to `org.apache.spark.sql.catalyst.encoders.ExpressionEncoderSuite`. It'll fail on JDK8u before the fix, and pass after the fix.
Closes#31709 from rednaxelafx/spark-34596-master.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Call `toSeq` to fix Scala 2.13 build error.
### Why are the changes needed?
It is needed to fix 2.13 build error.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#31716 from viirya/SPARK-34548-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch proposes to remove unnecessary children from Union under Distince and Deduplicate
### Why are the changes needed?
If there are any duplicate child of `Union` under `Distinct` and `Deduplicate`, it can be removed to simplify query plan.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test
Closes#31656 from viirya/SPARK-34548.
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 modifies the POD allocator to use the scheduler backend to get the known executors and remove those from the pending and newly created list.
This is different from the normal `ExecutorAllocationManager` requested killing of executors where the `spark.dynamicAllocation.executorIdleTimeout` is used.
In this case POD allocator kills the executors which should be only responsible for terminating not satisfied POD allocations (new requests where no POD state is received yet and PODs in pending state).
### Why are the changes needed?
Because there is race between executor POD allocator and cluster scheduler backend.
Running several experiment during downscaling we experienced a lot of killed fresh executors wich has already running task on them.
The pattern in the log was the following (see executor 312 and TID 2079):
```
21/02/01 15:12:03 INFO ExecutorMonitor: New executor 312 has registered (new total is 138)
...
21/02/01 15:12:03 INFO TaskSetManager: Starting task 247.0 in stage 4.0 (TID 2079, 100.100.18.138, executor 312, partition 247, PROCESS_LOCAL, 8777 bytes)
21/02/01 15:12:03 INFO ExecutorPodsAllocator: Deleting 3 excess pod requests (408,312,307).
...
21/02/01 15:12:04 ERROR TaskSchedulerImpl: Lost executor 312 on 100.100.18.138: The executor with id 312 was deleted by a user or the framework.
21/02/01 15:12:04 INFO TaskSetManager: Task 2079 failed because while it was being computed, its executor exited for a reason unrelated to the task. Not counting this failure towards the maximum number of failures for the task.
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
#### Manually
With this change there was no executor lost with running task on it.
##### With unit test
A new test is added and existing test is modified to check these cases.
Closes#31513 from attilapiros/SPARK-34361.
Authored-by: “attilapiros” <piros.attila.zsolt@gmail.com>
Signed-off-by: Holden Karau <hkarau@apple.com>
### What changes were proposed in this pull request?
Currently, the warehouse path gets fully qualified in the caller side for creating a database, table, partition, etc. An unqualified path is populated into Spark and Hadoop confs, which leads to inconsistent API behaviors. We should make it qualified ahead.
When the value is a relative path `spark.sql.warehouse.dir=lakehouse`, some behaviors become inconsistent, for example.
If the default database is absent at runtime, the app fails with
```java
Caused by: java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: file:./lakehouse
at org.apache.hadoop.fs.Path.initialize(Path.java:263)
at org.apache.hadoop.fs.Path.<init>(Path.java:254)
at org.apache.hadoop.hive.metastore.Warehouse.getDnsPath(Warehouse.java:133)
at org.apache.hadoop.hive.metastore.Warehouse.getDnsPath(Warehouse.java:137)
at org.apache.hadoop.hive.metastore.Warehouse.getWhRoot(Warehouse.java:150)
at org.apache.hadoop.hive.metastore.Warehouse.getDefaultDatabasePath(Warehouse.java:163)
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB_core(HiveMetaStore.java:636)
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:655)
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:431)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.hive.metastore.RetryingHMSHandler.invokeInternal(RetryingHMSHandler.java:148)
at org.apache.hadoop.hive.metastore.RetryingHMSHandler.invoke(RetryingHMSHandler.java:107)
at org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:79)
... 73 more
```
If the default database is present at runtime, the app can work with it, and if we create a database, it gets fully qualified, for example
```sql
spark-sql> create database test;
Time taken: 0.052 seconds
spark-sql> desc database test;
Database Name test
Comment
Location file:/Users/kentyao/Downloads/spark/spark-3.2.0-SNAPSHOT-bin-20210226/lakehouse/test.db
Owner kentyao
Time taken: 0.023 seconds, Fetched 4 row(s)
```
Another thing is that the log becomes nubilous, for example.
```logtalk
21/02/27 13:54:17 INFO SharedState: Setting hive.metastore.warehouse.dir ('null') to the value of spark.sql.warehouse.dir ('datalake').
21/02/27 13:54:17 INFO SharedState: Warehouse path is 'lakehouse'.
```
### Why are the changes needed?
fix bug and ambiguity
### Does this PR introduce _any_ user-facing change?
yes, the path now resolved with proper order - `warehouse->database->table->partition`
### How was this patch tested?
w/ ut added
Closes#31671 from yaooqinn/SPARK-34558.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup of #31560,
In #31560, we added `JavaSimpleWritableDataSource ` and left some little problems like unused interface `SessionConfigSupport` 、 inconsistent schema between `JavaSimpleWritableDataSource ` and `SimpleWritableDataSource`.
This PR fixes the remaining problems in #31560.
### Why are the changes needed?
1. `SessionConfigSupport` in `JavaSimpleWritableDataSource ` and `SimpleWritableDataSource` is never used, so we don't need to implement it.
2. change the schema of `SimpleWritableDataSource`, to match `TestingV2Source`
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
existing testsuites
Closes#31621 from kevincmchen/SPARK-34498.
Authored-by: kevincmchen <kevincmchen@tencent.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Today, child expressions may be resolved based on "real" or metadata output attributes. We should prefer the real attribute during resolution if one exists.
### Why are the changes needed?
Today, attempting to resolve an expression when there is a "real" output attribute and a metadata attribute with the same name results in resolution failure. This is likely unexpected, as the user may not know about the metadata attribute.
### Does this PR introduce _any_ user-facing change?
Yes. Previously, the user would see an error message when resolving a column with the same name as a "real" output attribute and a metadata attribute as below:
```
org.apache.spark.sql.AnalysisException: Reference 'index' is ambiguous, could be: testcat.ns1.ns2.tableTwo.index, testcat.ns1.ns2.tableOne.index.; line 1 pos 71
at org.apache.spark.sql.catalyst.expressions.package$AttributeSeq.resolve(package.scala:363)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveChildren(LogicalPlan.scala:107)
```
Now, resolution succeeds and provides the "real" output attribute.
### How was this patch tested?
Added a unit test.
Closes#31654 from karenfeng/fallback-resolve-metadata.
Authored-by: Karen Feng <karen.feng@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
**What changes were proposed in this pull request?**
This PR fixes dataframe.na.fillMap() for column having a dot in the name as mentioned in [SPARK-34417](https://issues.apache.org/jira/browse/SPARK-34417).
Use resolved attributes of a column for replacing null values.
**Why are the changes needed?**
dataframe.na.fillMap() does not work for column having a dot in the name
**Does this PR introduce any user-facing change?**
None
**How was this patch tested?**
Added unit test for the same
Closes#31545 from amandeep-sharma/master.
Lead-authored-by: Amandeep Sharma <happyaman91@gmail.com>
Co-authored-by: Amandeep Sharma <amandeep.sharma@oracle.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
As a follow-up to SPARK-34456, this PR removes `BatchWriteHelper` completely.
### Why are the changes needed?
These changes remove no longer used code.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#31699 from aokolnychyi/spark-34585.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. This PR aims to ignore ORC encryption tests when ORC shim is loaded by old Hadoop library by some other tests. The test coverage is preserved by Jenkins SBT runs and GitHub Action jobs. This PR only aims to recover Maven Jenkins jobs.
2. In addition, this PR simplifies SBT testing by refactor the test config to `SparkBuild.scala/pom.xml` and remove `DedicatedJVMTest`. This will remove one GitHub Action job which was recently added for `DedicatedJVMTest` tag.
### Why are the changes needed?
Currently, Maven test fails when it runs in a batch mode because `HadoopShimsPre2_3$NullKeyProvider` is loaded.
**MVN COMMAND**
```
$ mvn test -pl sql/core --am -Dtest=none -DwildcardSuites=org.apache.spark.sql.execution.datasources.orc.OrcV1QuerySuite,org.apache.spark.sql.execution.datasources.orc.OrcEncryptionSuite
```
**BEFORE**
```
- Write and read an encrypted table *** FAILED ***
...
Cause: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1) (localhost executor driver): java.lang.IllegalArgumentException: Unknown key pii
at org.apache.orc.impl.HadoopShimsPre2_3$NullKeyProvider.getCurrentKeyVersion(HadoopShimsPre2_3.java:71)
at org.apache.orc.impl.WriterImpl.getKey(WriterImpl.java:871)
```
**AFTER**
```
OrcV1QuerySuite
...
OrcEncryptionSuite:
- Write and read an encrypted file !!! CANCELED !!!
[] was empty org.apache.orc.impl.HadoopShimsPre2_3$NullKeyProvider1b705f65 doesn't has the test keys. ORC shim is created with old Hadoop libraries (OrcEncryptionSuite.scala:39)
- Write and read an encrypted table !!! CANCELED !!!
[] was empty org.apache.orc.impl.HadoopShimsPre2_3$NullKeyProvider22adeee1 doesn't has the test keys. ORC shim is created with old Hadoop libraries (OrcEncryptionSuite.scala:67)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the Jenkins Maven tests.
For SBT command,
- the test suite required a dedicated JVM (Before)
- the test suite doesn't require a dedicated JVM (After)
```
$ build/sbt "sql/testOnly *.OrcV1QuerySuite *.OrcEncryptionSuite"
...
[info] OrcV1QuerySuite
...
[info] - SPARK-20728 Make ORCFileFormat configurable between sql/hive and sql/core (26 milliseconds)
[info] OrcEncryptionSuite:
[info] - Write and read an encrypted file (431 milliseconds)
[info] - Write and read an encrypted table (359 milliseconds)
[info] All tests passed.
[info] Passed: Total 35, Failed 0, Errors 0, Passed 35
```
Closes#31697 from dongjoon-hyun/SPARK-34578-TEST.
Lead-authored-by: Dongjoon Hyun <dhyun@apple.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently in `SpecificParquetRecordReaderBase` we use deprecated APIs in a few places from Parquet, such as `readFooter`, `ParquetInputSplit`, `new ParquetFileReader`, `filterRowGroups`, etc. This replaces these with the newer APIs. In specific this:
- Replaces `ParquetInputSplit` with `FileSplit`. We never use specific things in the former such as `rowGroupOffsets` so the swap is pretty simple.
- Removes `readFooter` calls by using `ParquetFileReader.open`
- Replace deprecated `ParquetFileReader` ctor with the newer API which takes `ParquetReadOptions`.
- Removes the unnecessary handling of case when `rowGroupOffsets` is not null. It seems this never happens.
### Why are the changes needed?
The aforementioned APIs were deprecated and is going to be removed at some point in future. This is to ensure better supportability.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
This is a cleanup and relies on existing tests on the relevant code paths.
Closes#31667 from sunchao/SPARK-32703.
Lead-authored-by: Chao Sun <sunchao@apache.org>
Co-authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### Why is this change being proposed?
This patch adds support for a new "product" aggregation function in `sql.functions` which multiplies-together all values in an aggregation group.
This is likely to be useful in statistical applications which involve combining probabilities, or financial applications that involve combining cumulative interest rates, but is also a versatile mathematical operation of similar status to `sum` or `stddev`. Other users [have noted](https://stackoverflow.com/questions/52991640/cumulative-product-in-spark) the absence of such a function in current releases of Spark.
This function is both much more concise than an expression of the form `exp(sum(log(...)))`, and avoids awkward edge-cases associated with some values being zero or negative, as well as being less computationally costly.
### Does this PR introduce _any_ user-facing change?
No - only adds new function.
### How was this patch tested?
Built-in tests have been added for the new `catalyst.expressions.aggregate.Product` class and its invocation via the (scala) `sql.functions.product` function. The latter, and the PySpark wrapper have also been manually tested in spark-shell and pyspark sessions. The SparkR wrapper is currently untested, and may need separate validation (I'm not an "R" user myself).
An illustration of the new functionality, within PySpark is as follows:
```
import pyspark.sql.functions as pf, pyspark.sql.window as pw
df = sqlContext.range(1, 17).toDF("x")
win = pw.Window.partitionBy(pf.lit(1)).orderBy(pf.col("x"))
df.withColumn("factorial", pf.product("x").over(win)).show(20, False)
+---+---------------+
|x |factorial |
+---+---------------+
|1 |1.0 |
|2 |2.0 |
|3 |6.0 |
|4 |24.0 |
|5 |120.0 |
|6 |720.0 |
|7 |5040.0 |
|8 |40320.0 |
|9 |362880.0 |
|10 |3628800.0 |
|11 |3.99168E7 |
|12 |4.790016E8 |
|13 |6.2270208E9 |
|14 |8.71782912E10 |
|15 |1.307674368E12 |
|16 |2.0922789888E13|
+---+---------------+
```
Closes#30745 from rwpenney/feature/agg-product.
Lead-authored-by: Richard Penney <rwp@rwpenney.uk>
Co-authored-by: Richard Penney <rwpenney@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the `SQLConf` object, the `sqlConfEntries` map is globally synchronized (it is a Java `Collections.synchronizedMap`): any operation, including a get, will need to acquire the lock.
An example of this is calling the `DatatType.sameType` method. This will trigger a check on `SQLConf.get.caseSensitiveAnalysis`. So every time we compare two datatypes with sameType, we hit a lock.
To avoid having multiple tasks locking on this, a better approach would be to use a map that does not lock on read (like a `ConcurrentHashMap`). This map implementation does not lock on read, and on write it only locks the map partially. The only lock that happens is on write on the same map key.
### Why are the changes needed?
Multiple tasks performing any operation that directly or indirectly trigger a query to the `SQLConf.sqlConfEntries` map, will require acquiring a global lock on that map. Something as easy as calling `DataType.sameType(...)` would be locking on the global `sqlConfEntries` lock of the `Collections.synchronizedMap`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No functionality change. Existing unit tests run normally.
Closes#31689 from gabrielenizzoli/SPARK-34573.
Authored-by: Gabriele Nizzoli <1545350+gabrielenizzoli@users.noreply.github.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR is a follow-up of https://github.com/apache/spark/pull/31618 to document the available codecs for event log compression.
### Why are the changes needed?
Documentation.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual.
Closes#31695 from dongjoon-hyun/SPARK-34503-DOC.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose to generate unique attributes in the logical nodes of the `SHOW TABLES` command.
Also, this PR fixes similar issues in other logical nodes:
- ShowTableExtended
- ShowViews
- ShowTableProperties
- ShowFunctions
- ShowColumns
- ShowPartitions
- ShowNamespaces
### Why are the changes needed?
This fixes the issue which is demonstrated by the example below:
```scala
scala> val show1 = sql("SHOW TABLES IN ns1")
show1: org.apache.spark.sql.DataFrame = [namespace: string, tableName: string ... 1 more field]
scala> val show2 = sql("SHOW TABLES IN ns2")
show2: org.apache.spark.sql.DataFrame = [namespace: string, tableName: string ... 1 more field]
scala> show1.show
+---------+---------+-----------+
|namespace|tableName|isTemporary|
+---------+---------+-----------+
| ns1| tbl1| false|
+---------+---------+-----------+
scala> show2.show
+---------+---------+-----------+
|namespace|tableName|isTemporary|
+---------+---------+-----------+
| ns2| tbl2| false|
+---------+---------+-----------+
scala> show1.join(show2).where(show1("tableName") =!= show2("tableName")).show
org.apache.spark.sql.AnalysisException: Column tableName#17 are ambiguous. It's probably because you joined several Datasets together, and some of these Datasets are the same. This column points to one of the Datasets but Spark is unable to figure out which one. Please alias the Datasets with different names via `Dataset.as` before joining them, and specify the column using qualified name, e.g. `df.as("a").join(df.as("b"), $"a.id" > $"b.id")`. You can also set spark.sql.analyzer.failAmbiguousSelfJoin to false to disable this check.
at org.apache.spark.sql.execution.analysis.DetectAmbiguousSelfJoin$.apply(DetectAmbiguousSelfJoin.scala:157)
```
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, the example above works as expected:
```scala
scala> show1.join(show2).where(show1("tableName") =!= show2("tableName")).show
+---------+---------+-----------+---------+---------+-----------+
|namespace|tableName|isTemporary|namespace|tableName|isTemporary|
+---------+---------+-----------+---------+---------+-----------+
| ns1| tbl1| false| ns2| tbl2| false|
+---------+---------+-----------+---------+---------+-----------+
```
### How was this patch tested?
By running the new test:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *ShowTablesSuite"
```
Closes#31675 from MaxGekk/fix-output-attrs.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Cleanup all Zinc standalone server code, and realated coniguration.
### Why are the changes needed?
- Zinc is the incremental compiler to speed up builds of compilation.
- The scala-maven-plugin is the mave plugin, which is used by Spark, one of the function is to integrate the Zinc to enable the incremental compiler.
- Since Spark v3.0.0 ([SPARK-28759](https://issues.apache.org/jira/browse/SPARK-28759)), the scala-maven-plugin is upgraded to v4.X, that means Zinc v0.3.13 standalone server is useless anymore.
However, we still download, install, start the standalone Zinc server. we should remove all zinc standalone server code, and all related configuration.
See more in [SPARK-34539](https://issues.apache.org/jira/projects/SPARK/issues/SPARK-34539) or the doc [Zinc standalone server is useless after scala-maven-plugin 4.x](https://docs.google.com/document/d/1u4kCHDx7KjVlHGerfmbcKSB0cZo6AD4cBdHSse-SBsM).
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Run any mvn build:
./build/mvn -DskipTests clean package -pl core
You could see the increamental compilation is still working, the stage of "scala-maven-plugin:4.3.0:compile (scala-compile-first)" with incremental compilation info, like:
```
[INFO] --- scala-maven-plugin:4.3.0:testCompile (scala-test-compile-first) spark-core_2.12 ---
[INFO] Using incremental compilation using Mixed compile order
[INFO] Compiler bridge file: /root/.sbt/1.0/zinc/org.scala-sbt/org.scala-sbt-compiler-bridge_2.12-1.3.1-bin_2.12.10__52.0-1.3.1_20191012T045515.jar
[INFO] compiler plugin: BasicArtifact(com.github.ghik,silencer-plugin_2.12.10,1.6.0,null)
[INFO] Compiling 303 Scala sources and 27 Java sources to /root/spark/core/target/scala-2.12/test-classes ...
```
Closes#31647 from Yikun/cleanup-zinc.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
In the PR, I propose to generate "stable" output attributes per the logical node of the `DESCRIBE TABLE` command.
### Why are the changes needed?
This fixes the issue demonstrated by the example:
```scala
val tbl = "testcat.ns1.ns2.tbl"
sql(s"CREATE TABLE $tbl (c0 INT) USING _")
val description = sql(s"DESCRIBE TABLE $tbl")
description.drop("comment")
```
The `drop()` method fails with the error:
```
org.apache.spark.sql.AnalysisException: Resolved attribute(s) col_name#102,data_type#103 missing from col_name#29,data_type#30,comment#31 in operator !Project [col_name#102, data_type#103]. Attribute(s) with the same name appear in the operation: col_name,data_type. Please check if the right attribute(s) are used.;
!Project [col_name#102, data_type#103]
+- LocalRelation [col_name#29, data_type#30, comment#31]
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis.failAnalysis(CheckAnalysis.scala:51)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis.failAnalysis$(CheckAnalysis.scala:50)
```
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, `drop()`/`add()` works as expected:
```scala
description.drop("comment").show()
+---------------+---------+
| col_name|data_type|
+---------------+---------+
| c0| int|
| | |
| # Partitioning| |
|Not partitioned| |
+---------------+---------+
```
### How was this patch tested?
1. Run new test:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *DataSourceV2SQLSuite"
```
2. Run existing test suite:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *CatalogedDDLSuite"
```
Closes#31676 from MaxGekk/describe-table-drop-column.
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 fixes an issue that `bundler exec jekyll` build fails to generate Scala API docs even though after `dev/change-scala-version.sh 2.13` run.
### Why are the changes needed?
The reason of this issue is that `build/sbt` in `copy_api_dirs.rb` runs without `-Pscala-2.13`.
So, it's a bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I tested the following patterns manually.
* `dev/change-scala-version 2.13` and then `bundler exec jekyll build`
* `dev/change-scala-version 2.12` to change back to Scala 2.12 and then `bundler exec jekyll build`
* `dev/change-scala-version 2.13` two times to confirm the idempotency and then `bundler exec jekyll build`
* `dev/change-scala-version 2.12` two times to confirm the idempotency and then `bundler exec jekyll build`
Closes#31690 from sarutak/jekyll-scala-2.13.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR makes partition spec parsing respect case sensitive conf.
### Why are the changes needed?
When parsing the partition spec, Spark will call `org.apache.spark.sql.catalyst.parser.ParserUtils.checkDuplicateKeys` to check if there are duplicate partition column names in the list. But this method is always case sensitive and doesn't detect duplicate partition column names when using different cases.
### Does this PR introduce _any_ user-facing change?
Yep. This prevents users from writing incorrect queries such as `INSERT OVERWRITE t PARTITION (c='2', C='3') VALUES (1)` when they don't enable case sensitive conf.
### How was this patch tested?
The new added test will fail without this change.
Closes#31669 from zsxwing/SPARK-34556.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/31636. There was one place missed in `GangliaSink`, and we should also remove `SecurityManager`.
### Why are the changes needed?
To make `GangliaSink` work.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
It was found in the internal it tests in the company I work for.
Closes#31688 from HyukjinKwon/SPARK-34520-followup.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This adds `hadoop-yarn-server-web-proxy` as dependency for Yarn and Hadoop 3.x profile (it is already a dependency for 2.x). Also excludes some dependencies from the module which are already covered by other Hadoop jars used by Spark.
### Why are the changes needed?
The class `org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter` is used by `ApplicationMaster`:
```scala
private def addAmIpFilter(driver: Option[RpcEndpointRef], proxyBase: String) = {
val amFilter = "org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter"
val params = client.getAmIpFilterParams(yarnConf, proxyBase)
driver match {
case Some(d) =>
d.send(AddWebUIFilter(amFilter, params, proxyBase))
...
```
and will be loaded at runtime. Therefore, without the above jar Spark Yarn app will fail with `ClassNotFoundError`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests. Also tested manually and it worked with the fix, while was failing previously.
Closes#31642 from sunchao/SPARK-33212-followup-2.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
the parameter - `options` is never used. The changes here was part of https://github.com/apache/spark/pull/30642, It got reverted for easier backporting #30642 as a hotfix by dad24543aa, this PR brings it back to master.
### Why are the changes needed?
remove unless dead code
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
Passing CI is enough.
Closes#31683 from yaooqinn/SPARK-34570.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Some UT in SQLQuerySuite is not incorrect, it have wrong table name in `withTable`, this pr to make it correct.
### Why are the changes needed?
Fix UT
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existed UT
Closes#31681 from AngersZhuuuu/SPARK-34569.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
SPARK-33084 added the ability to use ivy coordinates with `SparkContext.addJar`. PR #29966 claims to mimic Hive behavior although I found a few cases where it doesn't
1) The default value of the transitive parameter is false, both in case of parameter not being specified in coordinate or parameter value being invalid. The Hive behavior is that transitive is [true if not specified](cb2ac3dcc6/ql/src/java/org/apache/hadoop/hive/ql/util/DependencyResolver.java (L169)) in the coordinate and [false for invalid values](cb2ac3dcc6/ql/src/java/org/apache/hadoop/hive/ql/util/DependencyResolver.java (L124)). Also, regardless of Hive, I think a default of true for the transitive parameter also matches [ivy's own defaults](https://ant.apache.org/ivy/history/2.5.0/ivyfile/dependency.html#_attributes).
2) The parameter value for transitive parameter is regarded as case-sensitive [based on the understanding](https://github.com/apache/spark/pull/29966#discussion_r547752259) that Hive behavior is case-sensitive. However, this is not correct, Hive [treats the parameter value case-insensitively](cb2ac3dcc6/ql/src/java/org/apache/hadoop/hive/ql/util/DependencyResolver.java (L122)).
I propose that we be compatible with Hive for these behaviors
### Why are the changes needed?
To make `ADD JAR` with ivy coordinates compatible with Hive's transitive behavior
### Does this PR introduce _any_ user-facing change?
The user-facing changes here are within master as the feature introduced in SPARK-33084 has not been released yet
1. Previously an ivy coordinate without `transitive` parameter specified did not resolve transitive dependency, now it does.
2. Previously an `transitive` parameter value was treated case-sensitively. e.g. `transitive=TRUE` would be treated as false as it did not match exactly `true`. Now it will be treated case-insensitively.
### How was this patch tested?
Modified existing unit tests to test new behavior
Add new unit test to cover usage of `exclude` with unspecified `transitive`
Closes#31623 from shardulm94/spark-34506.
Authored-by: Shardul Mahadik <smahadik@linkedin.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
Missing Python example file for [SPARK-34415][ML] Randomization in hyperparameter optimization
(https://github.com/apache/spark/pull/31535)
### What changes were proposed in this pull request?
For some reason (probably me being silly) a examples/src/main/python/ml/model_selection_random_hyperparameters_example.py was not pushed in a previous PR.
This PR restores that file.
### Why are the changes needed?
A single file (examples/src/main/python/ml/model_selection_random_hyperparameters_example.py) that should have been pushed as part of SPARK-34415 but was not. This was causing Lint errors as highlighted by dongjoon-hyun. Consequently, srowen asked for a new PR.
### Does this PR introduce _any_ user-facing change?
No, it merely restores a file that was overlook in SPARK-34415.
### How was this patch tested?
By running:
`bin/spark-submit examples/src/main/python/ml/model_selection_random_hyperparameters_example.py`
Closes#31687 from PhillHenry/SPARK-34415_model_selection_random_hyperparameters_example.
Authored-by: Phillip Henry <PhillHenry@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
