### What changes were proposed in this pull request?
This PR proposes to add a new scalastyle rule to enforce not importing `scala.collection.Seq` and `scala.collection.IndexedSeq` which conflicts with `scala.Seq` and `scala.IndexedSeq`.
The problem occurs as Scala 2.13 changed the alias of `scala.Seq` and `scala.IndexedSeq`. Before Scala 2.13, they were `scala.collection.Seq` and `scala.collection.IndexedSeq`. After Scala 2.13, they become `scala.collection.immutable.Seq` and `scala.collection.immutable.IndexedSeq`.
Please refer below doc for more details.
https://docs.scala-lang.org/overviews/core/collections-migration-213.html
### Why are the changes needed?
We have seen Seq/IndexedSeq issues on cross-compilation of Scala 2.12 / 2.13. While I'm not sure this can prevent all cases, this will prevent the simple case of breaking cross compilation.
### Does this PR introduce _any_ user-facing change?
No change on end user. Contributors will be restricted but shouldn't block them doing the right thing.
### How was this patch tested?
Ran scalastyle against current master (before #33084)
```
> dev/scalastyle
Scalastyle checks failed at following occurrences:
[error] /Users/Jungtaek.Lim/WorkArea/ScalaProjects/spark-apache/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/RocksDBFileManager.scala:28:0:
[error] Don't import scala.collection.Seq and scala.collection.IndexedSeq as it may bring some problems with cross-build between Scala 2.12 and 2.13.
[error]
[error] Please refer below page to see the details of changes around Seq.
[error] https://docs.scala-lang.org/overviews/core/collections-migration-213.html
[error]
[error] If you really need to use scala.collection.Seq or scala.collection.IndexedSeq, please use the fully-qualified name instead.
[error]
[error] /Users/Jungtaek.Lim/WorkArea/ScalaProjects/spark-apache/core/src/main/scala/org/apache/spark/util/Utils.scala:37:0:
[error] Don't import scala.collection.Seq and scala.collection.IndexedSeq as it may bring some problems with cross-build between Scala 2.12 and 2.13.
[error]
[error] Please refer below page to see the details of changes around Seq.
[error] https://docs.scala-lang.org/overviews/core/collections-migration-213.html
[error]
[error] If you really need to use scala.collection.Seq or scala.collection.IndexedSeq, please use the fully-qualified name instead.
[error]
[error] Total time: 15 s, completed Jun 25, 2021 9:01:32 PM
```
Closes#33085 from HeartSaVioR/SPARK-35894.
Authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Supprot the following operations:
- TimestampWithoutTZ + Calendar interval
- TimestampWithoutTZ + Year-Month interval
- TimestampWithoutTZ + Daytime interval
### Why are the changes needed?
Support basic '+' operator for timestamp without time zone type.
### Does this PR introduce _any_ user-facing change?
No, the timestamp without time zone type is not release yet.
### How was this patch tested?
Unit tests
Closes#33076 from gengliangwang/addForNewTS.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
The implementation for the load operation of RocksDBFileManager.
### Why are the changes needed?
Provide the functionality of loading all necessary files for specific checkpoint versions from DFS to the given local directory.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New UT added.
Closes#32767 from xuanyuanking/SPARK-35628.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/29137 , which has some issues when running EXPLAIN FORMATTED
```
AdaptiveSparkPlan (13)
+- == Final Plan ==
* HashAggregate (12)
+- CustomShuffleReader (11)
+- ShuffleQueryStage (10)
+- Exchange (9)
+- * HashAggregate (8)
+- * Project (7)
+- * BroadcastHashJoin Inner BuildRight (6)
:- * LocalTableScan (1)
+- BroadcastQueryStage (5)
+- BroadcastExchange (4)
+- * Project (3)
+- * LocalTableScan (2)
+- == Initial Plan ==
HashAggregate (unknown)
+- Exchange (unknown)
+- HashAggregate (unknown)
+- Project (unknown)
+- BroadcastHashJoin Inner BuildRight (unknown)
:- Project (unknown)
: +- LocalTableScan (unknown)
+- BroadcastExchange (unknown)
+- Project (3)
+- LocalTableScan (2)
```
Some nodes do not have an ID and show `unknown`. This PR fixes the issue.
### Why are the changes needed?
bug fix
### Does this PR introduce _any_ user-facing change?
EXPLAIN FORMATTED with AQE displays correctly.
### How was this patch tested?
new tests
Closes#33067 from cloud-fan/explain.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to migrate the following `ALTER TABLE ... RENAME COLUMN` command to use `UnresolvedTable` as a `child` to resolve the table identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
### Why are the changes needed?
This is a part of effort to make the relation lookup behavior consistent: [SPARK-29900](https://issues.apache.org/jira/browse/SPARK-29900).
### Does this PR introduce _any_ user-facing change?
After this PR, the above `ALTER TABLE ... RENAME COLUMN` commands will have a consistent resolution behavior.
### How was this patch tested?
Updated existing tests.
Closes#33066 from imback82/alter_rename.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR change the behavior of unit-to-unit interval syntax to prohibit the case that the same units are specified for FROM and TO.
### Why are the changes needed?
For ANSI compliance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#33057 from sarutak/prohibit-unit-pattern.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR changes the unionByName with null filling logic to append new nested struct fields from the right side of the union to the schema versus sorting fields alphabetically. It removes the need to use UpdateField expressions, and just directly projects new nested structs from each side of the union with the correct schema. This changes the union'd schema from being alphabetically sorted previously to now "left dominant", where the fields from the left side of the union are included and then the missing ones from the right are added in the same order found originally.
### Why are the changes needed?
Certain nested structs would cause unionByName with null filling to error out due to part of the logic for rewriting the expression tree to sort the structs.
### Does this PR introduce _any_ user-facing change?
Yes, nested struct fields will be in a different order after unionByName with null filling than before, though shouldn't cause much effective difference.
### How was this patch tested?
Updated existing tests based on the new StructField ordering and added a new test for the case that was broken originally.
Closes#33040 from Kimahriman/union-by-name-struct-order.
Authored-by: Adam Binford <adamq43@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to migrate the following `ALTER TABLE ... DROP COLUMNS` command to use `UnresolvedTable` as a `child` to resolve the table identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
### Why are the changes needed?
This is a part of effort to make the relation lookup behavior consistent: [SPARK-29900](https://issues.apache.org/jira/browse/SPARK-29900).
### Does this PR introduce _any_ user-facing change?
After this PR, the above `ALTER TABLE ... DROP COLUMNS` commands will have a consistent resolution behavior.
### How was this patch tested?
Updated existing tests.
Closes#32854 from imback82/alter_alternative.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Current Literal.create(data, dataType) for Period to YearMonthIntervalType and Duration to DayTimeIntervalType is not correct.
if data type is Period/Duration, it will create converter of default YearMonthIntervalType/DayTimeIntervalType, then the result is not correct, this pr fix this bug.
### Why are the changes needed?
Fix bug when use Literal.create()
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#33056 from AngersZhuuuu/SPARK-35871.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Revert 8a1995f936
### Why are the changes needed?
The merged test doesn't check different interval fields, actually. Need to apply this https://github.com/apache/spark/pull/33056 first of all.
### Does this PR introduce _any_ user-facing change?
No. This is tests.
### How was this patch tested?
By existing GAs.
Closes#33060 from MaxGekk/revert-Peng-Lei-SPARK-35728.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Revert 3904c0edba
### Why are the changes needed?
The merged test doesn't check different interval fields, actually. Need to apply this https://github.com/apache/spark/pull/33056 first of all.
### Does this PR introduce _any_ user-facing change?
No. This is tests.
### How was this patch tested?
By existing GAs.
Closes#33059 from MaxGekk/revert-Peng-Lei-SPARK-35778.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Check multiply/divide of year-month intervals of any fields by numeric.
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Expanded existed test cases.
Closes#33051 from Peng-Lei/SPARK-35778.
Authored-by: PengLei <18066542445@189.cn>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
1. The testcase is just cover the DayTimeIntervalType() */ numeric
2. Add testcase for following intervals */ numeric:
INTERVAL DAY
INTERVAL DAY TO HOUR
INTERVAL DAY TO MINUTE
INTERVAL HOUR
INTERVAL HOUR TO MINUTE
INTERVAL HOUR TO SECOND
INTERVAL MINUTE
INTERVAL MINUTE TO SECOND
INTERVAL SECOND
### Why are the changes needed?
Add testcase coverage.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existed testcase
Closes#33014 from Peng-Lei/SPARK-35728.
Authored-by: PengLei <18066542445@189.cn>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
* Add a new repartition operator `RebalanceRepartition`.
* Support a new hint `REBALANCE`
After this patch, user can run this query:
```sql
SELECT /*+ REBALANCE(c) */ * FROM t
```
### Why are the changes needed?
Add a new hint to distingush if we can optimize it safely.
This new hint can let AQE optimize with `CustomShuffleReaderExec` safely. Currently, AQE can only coalesce shuffle partitions but can not expand shuffle partitions due to the semantics of output partitioning.
Let's say we have a query:
```sql
SELECT /*+ REPARTITION(col) */ * FROM t
```
AQE can not expand the shuffle partitions even if `col` is skewed because expanding shuffle partitions will break the hashed output paritioning of `RepartitionByExpression`. But if the query is use`REPARTITION_BY_AQE`, AQE can optimize it without considering the semantics of output partitioning.
### Does this PR introduce _any_ user-facing change?
Yes, a new hint.
### How was this patch tested?
Add test.
Closes#32932 from ulysses-you/SPARK-35786.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
… sum of the digits is greater than 38
### What changes were proposed in this pull request?
Since Spark 3.1.1, NULL is returned when casting a string with many decimal places to a decimal type. If the sum of the digits before and after the decimal point is less than 39, a value is returned. From 39 digits, however, NULL is returned.
This worked until Spark 3.0.X.
Code to reproduce:
A string with 2 decimal places in front of the decimal point and 37 decimal places after the decimal point returns null
```
val data = Seq(
"28.9259999999999983799625624669715762138",
"28.925999999999998379962562466971576213",
"2.9259999999999983799625624669715762138"
)
val df = data.toDF("num")
df.withColumn("numConverted", col("num").cast("decimal(38, 5)")).show()
```
before this pull request, the result is
+----------------------+---------------+
| num |numConverted|
+----------------------+---------------+
|28.92599999999999...| null|
|28.92599999999999...| 28.92600|
|2.925999999999998...| 2.92600|
+----------------------+---------------+
the correct result should be
+----------------------+---------------+
| num |numConverted|
+----------------------+---------------+
|28.92599999999999...| 28.92600|
|28.92599999999999...| 28.92600|
|2.925999999999998...| 2.92600|
+----------------------+---------------+
The problem occur since https://issues.apache.org/jira/browse/SPARK-32706, it because the fast fail is checking precision length, which should only check the whole number part length of the input value, not the precision length
### Why are the changes needed?
correctness
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
test added
Closes#33011 from dgd-contributor/SPARK-35841_castStringToDecimalTypeError.
Authored-by: dgd-contributor <dgd_contributor@viettel.com.vn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Simplify the condition code which is introduced by [SPARK-35282](https://issues.apache.org/jira/browse/SPARK-35282).
### Why are the changes needed?
Reduce the code size and make code more readable.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass CI
Closes#33046 from ulysses-you/simplify-shj.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Support take into account year-month interval field in cast
##### Rule cast to target YearMonthIntervalType
| string | demo | strict target type | months |
|---|---|---|---|
| [+\|-]y-m | 1-1 | YearMonthIntervalType(YEAR. MONTH) | 13 |
| [+\|-]y| 1 | YearMonthIntervalType(YEAR. YEAR) | 12 |
| [+\|-]m | 1 | YearMonthIntervalType(MONTH. MONTH) | 1 |
| INTERVAL [+\|-]'[+\|-]y-m' YEAR TO MONTH | interval '1-1' year to month | YearMonthIntervalType(YEAR. MONTH) | 13 |
| INTERVAL [+\|-]'[+\|-]m' MONTH | interval '1' month | YearMonthIntervalType(MONTH. MONTH) | 1 |
| INTERVAL [+\|-]'[+\|-]y' YEAR | interval '1' year | YearMonthIntervalType(YEAR.YEAR) | 12 |
### Why are the changes needed?
Support take into account year-month interval field in cast
### Does this PR introduce _any_ user-facing change?
user can use `cast(str, YearMonthInterval(YEAR, YEAR))` etc
### How was this patch tested?
Added UT
Closes#32940 from AngersZhuuuu/SPARK-35768.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Check all day-time interval types in UDF.
### Why are the changes needed?
New checks should improve test coverage.
### Does this PR introduce _any_ user-facing change?
Yes but `DayTimeIntervalType` has not been released yet.
### How was this patch tested?
Existed UT.
Closes#33047 from AngersZhuuuu/SPARK-35730.
Lead-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Co-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
In a secure Yarn cluster, even though HBase or Kafka, or Hive services are not used in the user application, yarn client unnecessarily trying to generate Delegations token from these services. This will add additional delays while submitting spark application in a yarn cluster
Also during HBase delegation token generation step in the application submit stage, HBaseDelegationTokenProvider prints a full Exception Stack trace and it causes a noisy warning.
Apart from printing exception stack trace, Application submission taking more time as it retries connection to HBase master multiple times before it gives up. So, if HBase is not used in the user Applications, it is better to suggest User disable HBase Delegation Token generation.
This PR aims to avoid printing full Exception Stack by just printing just Exception name and also add a suggestion message to disable `Delegation Token generation` if service is not used in the Spark Application.
eg: `If HBase is not used, set spark.security.credentials.hbase.enabled to false`
### Why are the changes needed?
To avoid printing full Exception stack trace in WARN log
#### Before the fix
----------------
```
spark-shell --master yarn
.......
.......
21/06/12 14:29:41 WARN security.HBaseDelegationTokenProvider: Failed to get token from service hbase
java.lang.reflect.InvocationTargetException
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.spark.deploy.security.HBaseDelegationTokenProvider.obtainDelegationTokensWithHBaseConn(HBaseDelegationT
okenProvider.scala:93)
at org.apache.spark.deploy.security.HBaseDelegationTokenProvider.obtainDelegationTokens(HBaseDelegationTokenProvider.
scala:60)
at org.apache.spark.deploy.security.HadoopDelegationTokenManager$$anonfun$6.apply(HadoopDelegationTokenManager.scala:
166)
at org.apache.spark.deploy.security.HadoopDelegationTokenManager$$anonfun$6.apply(HadoopDelegationTokenManager.scala:
164)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:891)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
at scala.collection.MapLike$DefaultValuesIterable.foreach(MapLike.scala:206)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)
at org.apache.spark.deploy.security.HadoopDelegationTokenManager.obtainDelegationTokens(HadoopDelegationTokenManager.
scala:164)
```
#### After the fix
------------
```
spark-shell --master yarn
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
21/06/13 02:10:02 WARN security.HBaseDelegationTokenProvider: Failed to get token from service hbase due to java.lang.reflect.InvocationTargetException Retrying to fetch HBase security token with hbase connection parameter.
21/06/13 02:10:40 WARN security.HBaseDelegationTokenProvider: Failed to get token from service hbase java.lang.reflect.InvocationTargetException. If HBase is not used, set spark.security.credentials.hbase.enabled to false
21/06/13 02:10:47 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Attempted to request executors before the AM has registered!
```
### Does this PR introduce _any_ user-facing change?
Yes, in the log, it avoids printing full Exception stack trace.
Instread prints this.
**WARN security.HBaseDelegationTokenProvider: Failed to get token from service hbase java.lang.reflect.InvocationTargetException. If HBase is not used, set spark.security.credentials.hbase.enabled to false**
### How was this patch tested?
Tested manually as it can be verified only in a secure cluster
Closes#32894 from vinodkc/br_fix_Hbase_DT_Exception_stack_printing.
Authored-by: Vinod KC <vinod.kc.in@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Check all year-month interval types in UDF.
### Why are the changes needed?
New checks should improve test coverage.
### Does this PR introduce _any_ user-facing change?
Yes but `YearMonthIntervalType` has not been released yet.
### How was this patch tested?
Existed UT.
Closes#32985 from AngersZhuuuu/SPARK-35777.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
We use `DateAdd` to impl `DateType` `+`/`-` `INTERVAL DAY`
### Why are the changes needed?
To improve the impl of `DateType` `+`/`-` `INTERVAL DAY`
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Add ut test
Closes#33033 from Peng-Lei/SPARK-35852.
Authored-by: PengLei <18066542445@189.cn>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Extend the `TransposeWindow` rule to transpose `Window` nodes, that have `Project` between them.
### Why are the changes needed?
The analyzer will turn a `dataset.withColumn("colName", expressionWithWindowFunction)` method call to a `Project - Window - Project` chain in the logical plan. When this method is called multiple times in a row, then the projects can block the `Window` nodes from being transposed by the current `TransposeWindow` rule.
TPCDS q47 and q57 are also improved by this.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
UT
Closes#31980 from tanelk/SPARK-34807_transpose_window.
Lead-authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Co-authored-by: Tanel Kiis <tanel.kiis@gmail.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
### What changes were proposed in this pull request?
Check all day-time interval types in arrow.
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added UT.
Closes#33039 from AngersZhuuuu/SPARK-35731.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR adds test to check `sum` and `avg` works with all the `DayTimeIntervalType`.
This PR also moves a dataframe commonly used by tests `SPARK-34837: Support ANSI SQL intervals by the aggregate function avg` and `SPARK-34716: Support ANSI SQL intervals by the aggregate function sum` to `SQLTestData.scala`, and a little bit modifies it.
### Why are the changes needed?
To ensure the results of aggregations are what is expected.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#33042 from sarutak/check-interval-agg-dt.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.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 MergingSessionsIterator, which enables to merge elements belong to the same session directly.
MergingSessionsIterator is a variant of SortAggregateIterator which merges the session windows based on the fact input rows are sorted by "group keys + the start time of session window". When merging windows, MergingSessionsIterator also applies aggregations on merged window, which eliminates the necessity on buffering inputs (which requires copying rows) and update the session spec for each input.
MergingSessionsIterator is quite performant compared to UpdatingSessionsIterator brought by SPARK-34888. Note that MergingSessionsIterator can only apply to the cases aggregation can be applied altogether, so there're still rooms for UpdatingSessionIterator to be used.
This issue also introduces MergingSessionsExec which is the physical node on leveraging MergingSessionsIterator to sort the input rows and aggregate rows according to the session windows.
### 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#31987 from HeartSaVioR/SPARK-34889-SPARK-10816-PR-31570-part-2.
Lead-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Co-authored-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Check all day-time interval types in HiveInspectors tests.
### Why are the changes needed?
New tests should improve test coverage for day-time interval types.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added UT.
Closes#33036 from AngersZhuuuu/SPARK-35733.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Move all the bookkeeping states while scanning a Parquet column chunk into a single class `ParquetReadState`.
### Why are the changes needed?
As suggested [here](https://github.com/apache/spark/pull/32753#discussion_r655580942). To support column index in the vectorized reader path, we'll going to introduce more states to track. These are spread across different classes which make the code harder to maintain. Therefore, this proposes to move them into a single class so they can be managed better.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing UTs.
Closes#33006 from sunchao/SPARK-35846.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Make the ANSI flag part of expression `Cast`'s parameter list, instead of fetching it from the sessional SQLConf.
### Why are the changes needed?
For Views, it is important to show consistent results even the ANSI configuration is different in the running session. This is why many expressions like 'Add'/'Divide' making the ANSI flag part of its case class parameter list.
We should make it consistent for the expression `Cast`
### Does this PR introduce _any_ user-facing change?
Yes, the `Cast` inside a View always behaves the same, independent of the ANSI model SQL configuration in the current session.
### How was this patch tested?
Existing UT
Closes#33027 from gengliangwang/ansiFlagInCast.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Support UpCast between different field of YearMonthIntervalType/DayTimeIntervalType
### Why are the changes needed?
Since in our encoder we handle Period/Duration as default YearMonthIntervalType/DayTimeIntervalType, when we use udf to handle this type, it will upcast all type of YearMonthIntervalType/DayTimeIntervalType to default YearMonthIntervalType/DayTimeIntervalType, so we need to support this.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added Ut
Closes#33035 from AngersZhuuuu/SPARK-35860.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Add tests to check that all year-month interval types are supported in (de-)serialization from/to Arrow format.
### Why are the changes needed?
New tests should improve test coverage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
added ut
Closes#32993 from AngersZhuuuu/SPARK-35776.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR unifies reuse map data structures in non-AQE and AQE rules to a simple `Map[<canonicalized plan>, <plan>]` based on the discussion here: https://github.com/apache/spark/pull/28885#discussion_r655073897
### Why are the changes needed?
The proposed `Map[<canonicalized plan>, <plan>]` is simpler than the currently used `Map[<schema>, ArrayBuffer[<plan>]]` in `ReuseMap`/`ReuseExchangeAndSubquery` (non-AQE) and consistent with the `ReuseAdaptiveSubquery` (AQE) subquery reuse rule.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing UTs.
Closes#33021 from peter-toth/SPARK-35855-unify-reuse-map-data-structures.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The current OuterReference resolution is a bit weird: when the outer plan has more than one child, it resolves OuterReference from the output of each child, one by one, left to right.
This is incorrect in the case of join, as the column name can be ambiguous if both left and right sides output this column.
This PR fixes this bug by resolving OuterReference with `outerPlan.resolveChildren`, instead of something like `outerPlan.children.foreach(_.resolve(...))`
### Why are the changes needed?
bug fix
### Does this PR introduce _any_ user-facing change?
The problem only occurs in join, and join condition doesn't support correlated subquery yet. So this PR only improves the error message. Before this PR, people see
```
java.lang.UnsupportedOperationException
Cannot generate code for expression: outer(t1a#291)
```
### How was this patch tested?
a new test
Closes#33004 from cloud-fan/outer-ref.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Check all year-month interval types in HiveInspectors tests.
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT.
Closes#32970 from AngersZhuuuu/SPARK-35772.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR fixes an issue that `IntervalUtils.toDayTimeIntervalString` doesn't consider the case that a day-time interval type is casted as another day-time interval type.
if data of `interval day to second` is casted as `interval hour to second`, the value of the day is multiplied by 24 and added to the value of hour. For example, `INTERVAL '1 2' DAY TO HOUR` will be `INTERVAL '26' HOUR` if it's casted.
If this behavior is intended, it should be stringified as `INTERVAL '26' HOUR` but currently, it will be `INTERVAL '2' HOUR`
### Why are the changes needed?
t's a bug if the behavior of cast is intended.
### Does this PR introduce _any_ user-facing change?
No, because this feature is not released yet.
### How was this patch tested?
Modified the tests added in SPARK-35734 (#32891)
Closes#33031 from sarutak/fix-toDayTimeIntervalString.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
There are a few test cases that are supposed to be in CastSuiteBase instead of CastSuite:
- SPARK-35112: Cast string to day-time interval
- SPARK-35111: Cast string to year-month interval
- SPARK-35820: Support cast DayTimeIntervalType in different fields
- SPARK-35819: Support cast YearMonthIntervalType in different fields
This PR is to move them to CastSuiteBase. Also, it adds comments for the scope of CastSuiteBase/CastSuite/AnsiCastSuiteBase.
### Why are the changes needed?
Increase test coverage so that we can test the casting under ANSI mode.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UT
Closes#33022 from gengliangwang/moveTest.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/32862 , to simplify the code with AQE helper.
### Why are the changes needed?
code cleanup
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests
Closes#33026 from cloud-fan/follow.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
We introduced `SparkSession.withActive` a while ago, and we use it when we need to run some code with a certain SparkSession as the active session.
Somehow we missed `SparkPlan.makeCopy`, which sets active session directly. This PR proposes to call `SparkSession.withActive` there.
### Why are the changes needed?
make sure we don't change the active session unexpectedly.
### Does this PR introduce _any_ user-facing change?
No. `makeCopy` is an internal function and I can't find a real case that this can change the active session. Mostly in an upper level, there is already a `SparkSession.withActive`, like `QueryExecution.executePhase`
### How was this patch tested?
existing tests
Closes#33029 from cloud-fan/minor1.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/33001 , to provide a more direct fix.
The regression in 3.1 was caused by the fact that we changed the parser and allow the parser to return CHAR/VARCHAR type. We should have replaced CHAR/VARCHAR with STRING before the data type flows into the query engine, however, `OrcUtils` is missed.
When reading ORC files, at the task side we will read the real schema from ORC file metadata, then apply filter pushdown. For some reason, the implementation turns ORC schema to Spark schema before filter pushdown, and this step does not strip CHAR/VARCHAR. Note, for Parquet we use the Parquet schema directly in filter pushdown, and do not this have problem.
This PR proposes to replace the CHAR/VARCHAR with STRING when turning ORC schema to Spark schema.
### Why are the changes needed?
a more directly bug fix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests
Closes#33030 from cloud-fan/help.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Proposed changes in this pull request:
1. Introducing the `TestGroupState` interface which is inherited from `GroupState` so that testing related getters can be exposed in a controlled manner
2. Changing `GroupStateImpl` to inherit from `TestGroupState` interface, instead of directly from `GroupState`
3. Implementing `TestGroupState` object with `create()` method to forward inputs to the private `GroupStateImpl` constructor
4. User input validations have been added into `GroupStateImpl`'s `createForStreaming()` method to prevent users from creating invalid GroupState objects.
5. Replacing existing `GroupStateImpl` usages in sql pkg internal unit tests with the newly added `TestGroupState` to give user best practice about `TestGroupState` usage.
With the changes in this PR, the class hierarchy is changed from `GroupStateImpl` -> `GroupState` to `GroupStateImpl` -> `TestGroupState` -> `GroupState` (-> means inherits from)
### Why are the changes needed?
The internal `GroupStateImpl` implementation for the `GroupState` interface has no public constructors accessible outside of the sql pkg. However, the user-provided state transition function for `[map|flatMap]GroupsWithState` requires a `GroupState` object as the prevState input.
Currently, users are calling the Structured Streaming engine in their unit tests in order to instantiate such `GroupState` instances, which makes UTs cumbersome.
The proposed `TestGroupState` interface is to give users controlled access to the `GroupStateImpl` internal implementation to largely improve testability of Structured Streaming state transition functions.
**Usage Example**
```
import org.apache.spark.sql.streaming.TestGroupState
test(“Structured Streaming state update function”) {
var prevState = TestGroupState.create[UserStatus](
optionalState = Optional.empty[UserStatus],
timeoutConf = EventTimeTimeout,
batchProcessingTimeMs = 1L,
eventTimeWatermarkMs = Optional.of(1L),
hasTimedOut = false)
val userId: String = ...
val actions: Iterator[UserAction] = ...
assert(!prevState.hasUpdated)
updateState(userId, actions, prevState)
assert(prevState.hasUpdated)
}
```
### Does this PR introduce _any_ user-facing change?
Yes, the `TestGroupState` interface and its corresponding `create()` factory function in its companion object are introduced in this pull request for users to use in unit tests.
### How was this patch tested?
- New unit tests are added
- Existing GroupState unit tests are updated
Closes#32938 from lizhangdatabricks/improve-group-state-testability.
Authored-by: Li Zhang <li.zhang@databricks.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
### What changes were proposed in this pull request?
When SQL function `to_timestamp_ntz` has invalid format pattern input, throw a runtime exception with hints for the valid patterns, instead of throwing an upgrade exception with suggestions to use legacy formatters.
### Why are the changes needed?
As discussed in https://github.com/apache/spark/pull/32995/files#r655148980, there is an error message saying
"You may get a different result due to the upgrading of Spark 3.0: Fail to recognize 'yyyy-MM-dd GGGGG' pattern in the DateTimeFormatter. 1) You can set spark.sql.legacy.timeParserPolicy to LEGACY to restore the behavior before Spark 3.0"
This is not true for function to_timestamp_ntz, which only uses the Iso8601TimestampFormatter and added since Spark 3.2. We should improve it.
### Does this PR introduce _any_ user-facing change?
No, the new SQL function is not released yet.
### How was this patch tested?
Unit test
Closes#33019 from gengliangwang/improveError.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
What changes were proposed in this pull request?
1. Change the return value type from DayTimeIntervalType(DAY, SECOND) to DayTimeIntervalType(DAY, DAY) of SubtractDates.
Why are the changes needed?
https://issues.apache.org/jira/browse/SPARK-35727
Does this PR introduce any user-facing change?
no
How was this patch tested?
existed ut test
Closes#32999 from Peng-Lei/SPARK-35727.
Lead-authored-by: Lei Peng <peng.8lei@gmail.com>
Co-authored-by: PengLei <18066542445@189.cn>
Co-authored-by: Peng-Lei <peng.8lei@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Similar to SPARK-35532, the main change of this pr is add `scala-2.13` profile to external/kafka-0-10-sql/pom.xml, external/avro/pom.xml and sql/hive-thriftserver/pom.xml, the `scala-2.13` profile include dependency on `scala-parallel-collections_2.13`, then all(34) spark modules can maven test independently.
### Why are the changes needed?
Ensure alll(34) spark modules can be maven test independently in Scala 2.13
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Pass the GitHub Action Scala 2.13 job
- Manual test：
1. Execute
```
dev/change-scala-version.sh 2.13
mvn clean install -DskipTests -Phadoop-3.2 -Phive-2.3 -Phadoop-cloud -Pmesos -Pyarn -Pkinesis-asl -Phive-thriftserver -Pspark-ganglia-lgpl -Pkubernetes -Phive -Pscala-2.13
```
2. maven test `external/kafka-0-10-sql` module
```
mvn test -Phadoop-3.2 -Phive-2.3 -Phadoop-cloud -Pmesos -Pyarn -Pkinesis-asl -Phive-thriftserver -Pspark-ganglia-lgpl -Pkubernetes -Phive -Pscala-2.13 -pl external/kafka-0-10-sql
```
**before**
```
Discovery starting.
Discovery completed in 857 milliseconds.
Run starting. Expected test count is: 464
...
KafkaRelationSuiteV2:
- explicit earliest to latest offsets
- default starting and ending offsets
- explicit offsets
- default starting and ending offsets with headers
- timestamp provided for starting and ending
- timestamp provided for starting, offset provided for ending
- timestamp provided for ending, offset provided for starting
- timestamp provided for starting, ending not provided
- timestamp provided for ending, starting not provided
- global timestamp provided for starting and ending
- no matched offset for timestamp - startingOffsets
- preferences on offset related options
- no matched offset for timestamp - endingOffsets
*** RUN ABORTED ***
java.lang.NoClassDefFoundError: scala/collection/parallel/TaskSupport
at org.apache.spark.SparkContext.$anonfun$union$1(SparkContext.scala:1411)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:788)
at org.apache.spark.SparkContext.union(SparkContext.scala:1405)
at org.apache.spark.sql.execution.UnionExec.doExecute(basicPhysicalOperators.scala:697)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:182)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:220)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:217)
...
Cause: java.lang.ClassNotFoundException: scala.collection.parallel.TaskSupport
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
at org.apache.spark.SparkContext.$anonfun$union$1(SparkContext.scala:1411)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:788)
at org.apache.spark.SparkContext.union(SparkContext.scala:1405)
at org.apache.spark.sql.execution.UnionExec.doExecute(basicPhysicalOperators.scala:697)
...
```
**After**
```
Run completed in 33 minutes, 51 seconds.
Total number of tests run: 464
Suites: completed 31, aborted 0
Tests: succeeded 464, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```
3. maven test `external/avro` module
```
mvn test -Phadoop-3.2 -Phive-2.3 -Phadoop-cloud -Pmesos -Pyarn -Pkinesis-asl -Phive-thriftserver -Pspark-ganglia-lgpl -Pkubernetes -Phive -Pscala-2.13 -pl external/avro
```
**before**
```
Discovery starting.
Discovery completed in 2 seconds, 765 milliseconds.
Run starting. Expected test count is: 255
AvroReadSchemaSuite:
- append column at the end
- hide column at the end
- append column into middle
- hide column in the middle
- add a nested column at the end of the leaf struct column
- add a nested column in the middle of the leaf struct column
- add a nested column at the end of the middle struct column
- add a nested column in the middle of the middle struct column
- hide a nested column at the end of the leaf struct column
- hide a nested column in the middle of the leaf struct column
- hide a nested column at the end of the middle struct column
- hide a nested column in the middle of the middle struct column
*** RUN ABORTED ***
java.lang.NoClassDefFoundError: scala/collection/parallel/TaskSupport
at org.apache.spark.SparkContext.$anonfun$union$1(SparkContext.scala:1411)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:788)
at org.apache.spark.SparkContext.union(SparkContext.scala:1405)
at org.apache.spark.sql.execution.UnionExec.doExecute(basicPhysicalOperators.scala:697)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:182)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:220)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:217)
...
Cause: java.lang.ClassNotFoundException: scala.collection.parallel.TaskSupport
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
at org.apache.spark.SparkContext.$anonfun$union$1(SparkContext.scala:1411)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:788)
at org.apache.spark.SparkContext.union(SparkContext.scala:1405)
at org.apache.spark.sql.execution.UnionExec.doExecute(basicPhysicalOperators.scala:697)
...
```
**After**
```
Run completed in 1 minute, 42 seconds.
Total number of tests run: 255
Suites: completed 12, aborted 0
Tests: succeeded 255, failed 0, canceled 0, ignored 2, pending 0
All tests passed.
```
4. maven test `sql/hive-thriftserver` module
```
mvn test -Phadoop-3.2 -Phive-2.3 -Phadoop-cloud -Pmesos -Pyarn -Pkinesis-asl -Phive-thriftserver -Pspark-ganglia-lgpl -Pkubernetes -Phive -Pscala-2.13 -pl sql/hive-thriftserver
```
**before**
```
- union.sql *** FAILED ***
"1 a
1 a
2 b
2 b" did not contain "Exception" Exception did not match for query #2
SELECT *
FROM (SELECT * FROM t1
UNION ALL
SELECT * FROM t1), expected: 1 a
1 a
2 b
2 b, but got: java.sql.SQLException
org.apache.hive.service.cli.HiveSQLException: Error running query: java.lang.NoClassDefFoundError: scala/collection/parallel/TaskSupport
at org.apache.spark.sql.hive.thriftserver.HiveThriftServerErrors$.runningQueryError(HiveThriftServerErrors.scala:38)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecuteStatementOperation$$execute(SparkExecuteStatementOperation.scala:324)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$2$$anon$3.$anonfun$run$2(SparkExecuteStatementOperation.scala:229)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
at org.apache.spark.sql.hive.thriftserver.SparkOperation.withLocalProperties(SparkOperation.scala:79)
at org.apache.spark.sql.hive.thriftserver.SparkOperation.withLocalProperties$(SparkOperation.scala:63)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.withLocalProperties(SparkExecuteStatementOperation.scala:43)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$2$$anon$3.run(SparkExecuteStatementOperation.scala:229)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$2$$anon$3.run(SparkExecuteStatementOperation.scala:224)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1878)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$2.run(SparkExecuteStatementOperation.scala:238)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.NoClassDefFoundError: scala/collection/parallel/TaskSupport
at org.apache.spark.SparkContext.$anonfun$union$1(SparkContext.scala:1411)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:788)
at org.apache.spark.SparkContext.union(SparkContext.scala:1405)
at org.apache.spark.sql.execution.UnionExec.doExecute(basicPhysicalOperators.scala:697)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:182)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:220)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:217)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:178)
at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:323)
at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:389)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3719)
at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2987)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3710)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:774)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3708)
at org.apache.spark.sql.Dataset.collect(Dataset.scala:2987)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecuteStatementOperation$$execute(SparkExecuteStatementOperation.scala:299)
... 16 more
Caused by: java.lang.ClassNotFoundException: scala.collection.parallel.TaskSupport
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
... 40 more (ThriftServerQueryTestSuite.scala:209)
```
**After**
```
Run completed in 29 minutes, 17 seconds.
Total number of tests run: 535
Suites: completed 20, aborted 0
Tests: succeeded 535, failed 0, canceled 0, ignored 17, pending 0
All tests passed.
```
Closes#32994 from LuciferYang/SPARK-35838.
Authored-by: YangJie <yangjie01@baidu.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
When we call periodToMonths and durationToMicros with certain type field, we should pass endField parameter.
### Why are the changes needed?
When we call periodToMonths and durationToMicros with certain type field, we should pass endField parameter.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existed UT
Closes#32984 from AngersZhuuuu/SPARK-35726-35769.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
#32513 added the case class `CommandResult` so as we can eagerly execute command locally. But we forgot to update
`isLocal` of `Dataset`.
### Why are the changes needed?
`Dataset.isLocal` should consider `CommandResult`.
### Does this PR introduce _any_ user-facing change?
Yes. If the SQL plan is `CommandResult`, `Dataset.isLocal` must return true.
### How was this patch tested?
No test.
Closes#32963 from beliefer/SPARK-35378-followup2.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Fix how we measure the metric `allUpdatesTimeMs` in `FlatMapGroupsWithStateExec` similar to other streaming stateful operators.
### Why are the changes needed?
Metric `allUpdatesTimeMs` meant to capture the start to end walltime of the operator `FlatMapGroupsWithStateExec`, but currently it just [captures](79362c4efc/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FlatMapGroupsWithStateExec.scala (L121)) the iterator creation time.
Fix it to measure similar to how other stateful operators measure. Example one [here](79362c4efc/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/statefulOperators.scala (L406)). This measurement is not perfect due to the nature of the lazy iterator and also includes the time the consumer operator spent in processing the current operator output, but it should give a good signal when comparing the metric in one microbatch to the metric in another microbatch.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UTs for regression. Due to the nature of metric type (time), it is hard to write a UT, but have manually verified.
Closes#32952 from vkorukanti/SPARK-35799.
Authored-by: Venki Korukanti <venki.korukanti@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
The char/varchar type should be mapped to orc's string type too, see https://orc.apache.org/docs/types.html
### Why are the changes needed?
fix a regression
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new tests
Closes#33001 from yaooqinn/SPARK-35700.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Implement new SQL function: `to_timestamp_ntz`.
The syntax is similar to the built-in function `to_timestamp`:
```
to_timestamp_ntz ( <date_expr> )
to_timestamp_ntz ( <timestamp_expr> )
to_timestamp_ntz ( <string_expr> [ , <format> ] )
```
The naming is from snowflake: https://docs.snowflake.com/en/sql-reference/functions/to_timestamp.html
### Why are the changes needed?
Adds a new SQL function to create a literal/column of timestamp without time zone.
It's convenient for both end-users and developers.
### Does this PR introduce _any_ user-facing change?
Yes, a new SQL function `to_timestamp_ntz`.
### How was this patch tested?
Unit tests
Closes#32995 from gengliangwang/toTimestampNtz.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR adds test to check `sum` and `avg` works with all the `YearMonthInterval` types.
### Why are the changes needed?
To ensure the results of aggregations are what is expected.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#32988 from sarutak/check-interval-agg-ym.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Extend the `CollapseWindow` rule to collapse `Window` nodes, that have `Project` between them.
### Why are the changes needed?
The analyzer will turn a `dataset.withColumn("colName", expressionWithWindowFunction)` method call to a `Project - Window - Project` chain in the logical plan. When this method is called multiple times in a row, then the projects can block the `Window` nodes from being collapsed by the current `CollapseWindow` rule.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
UT
Closes#31677 from tanelk/SPARK-34565_collapse_windows.
Lead-authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Co-authored-by: Tanel Kiis <tanel.kiis@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to add 2 new methods that accept one field and produce either `YearMonthIntervalType` or `DayTimeIntervalType`.
### Why are the changes needed?
To improve code maintenance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By existing test suites.
Closes#32997 from MaxGekk/ansi-interval-types-single-field.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Support Cast between different field DayTimeIntervalType
### Why are the changes needed?
Make user convenient to get different field DayTimeIntervalType
### Does this PR introduce _any_ user-facing change?
User can call cast DayTimeIntervalType(DAY, SECOND) to DayTimeIntervalType(DAY, MINUTE) etc
### How was this patch tested?
Added UT
Closes#32975 from AngersZhuuuu/SPARK-35820.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Add a test.
### Why are the changes needed?
The SubqueryExpression refactor PR https://github.com/apache/spark/pull/32687 actually fixes the bug of `SubqueryExpression.references`. So this follow-up PR adds a regression unit test for it.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added a new test.
Closes#32990 from Ngone51/spark-35545-followup.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR fixes error message shown when changing a column type to year-month/day-time interval type is attempted.
### Why are the changes needed?
It's for consistent behavior.
Updating column types to interval types are prohibited for V2 source tables.
So, if we attempt to update the type of a column to the conventional interval type, an error message like `Error in query: Cannot update <table> field <column> to interval type;`.
But, for year-month/day-time interval types, another error message like `Error in query: Cannot update <table> field <column>:<type> cannot be cast to interval year;`.
You can reproduce with the following procedure.
```
$ bin/spark-sql
spark-sql> SET spark.sql.catalog.mycatalog=<a catalog implementation class>;
spark-sql> CREATE TABLE mycatalog.t1(c1 int) USING <V2 datasource implementation class>;
spark-sql> ALTER TABLE mycatalog.t1 ALTER COLUMN c1 TYPE interval year to month;
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Modified an existing test.
Closes#32978 from sarutak/err-msg-interval.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR fixes an issue that `IntervalUtils.toYearMonthIntervalString` doesn't consider the case that year-month interval type is casted as month interval type.
If a year-month interval data is casted as month interval, the value of the year is multiplied by `12` and added to the value of month. For example, `INTERVAL '1-2' YEAR TO MONTH` will be `INTERVAL '14' MONTH` if it's casted.
If this behavior is intended, it's stringified to be `'INTERVAL 14' MONTH` but currently, it will be `INTERVAL '2' MONTH`
### Why are the changes needed?
It's a bug if the behavior of cast is intended.
### Does this PR introduce _any_ user-facing change?
No, because this feature is not released yet.
### How was this patch tested?
Modified the tests added in SPARK-35771 (#32924).
Closes#32982 from sarutak/fix-toYearMonthIntervalString.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Support Cast between different field YearMonthIntervalType
### Why are the changes needed?
Make user convenient to get different field YearMonthIntervalType
### Does this PR introduce _any_ user-facing change?
User can call cast YearMonthIntervalType(YEAR, MONTH) to YearMonthIntervalType(YEAR, YEAR) etc
### How was this patch tested?
Added UT
Closes#32974 from AngersZhuuuu/SPARK-35819.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Support truncate java.time.Duration by fields of day-time interval type.
### Why are the changes needed?
To respect fields of the target day-time interval types.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#32950 from AngersZhuuuu/SPARK-35726.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This patch proposes to add an internal config for ignoring metadata of `FileStreamSink` when reading the output path.
### Why are the changes needed?
`FileStreamSink` produces a metadata directory which logs output files per micro-batch. When we read from the output path, Spark will look at the metadata and ignore other files not in the log.
Normally it works well. But for some use-cases, we may need to ignore the metadata when reading the output path. For example, when we change the streaming query and must to run it with new checkpoint directory, we cannot use previous metadata. If we create a new metadata too, when we read the output path later in Spark, Spark only reads the files listed in the new metadata. The files written before we use new checkpoint and metadata are ignored by Spark.
Although seems we can output to different output directory every time, but it is bad idea as we will produce many directories unnecessarily.
We need a config for ignoring the metadata of `FileStreamSink` when reading the output path.
### Does this PR introduce _any_ user-facing change?
Added a config for ignoring metadata of FileStreamSink when reading the output.
### How was this patch tested?
Unit tests.
Closes#32702 from viirya/ignore-metadata.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This PR improves `Distinct` statistics estimation by rewrite it to `Aggregate`.
### Why are the changes needed?
1. The current implementation will lack column statistics.
2. Some rules before the `ReplaceDistinctWithAggregate` may use it. For example: https://github.com/apache/spark/pull/31113/files#diff-11264d807efa58054cca2d220aae8fba644ee0f0f2a4722c46d52828394846efR1808
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32291 from wangyum/SPARK-35185.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
### What changes were proposed in this pull request?
Change `AQEPropagateEmptyRelation` from `transformUp` to `transformUpWithPruning
### Why are the changes needed?
To avoid unnecessary iteration during AQE optimizer.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass CI.
Closes#32742 from ulysses-you/aqe-transformUpWithPruning.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Support truncate java.time.Period by fields of year-month interval type
### Why are the changes needed?
To follow the SQL standard and respect the field restriction of the target year-month type.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#32945 from AngersZhuuuu/SPARK-35769.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR extends the following tests to use day-time intervals.
* StreamingOuterJoinSuite.right outer with watermark range condition
* StreamingOuterJoinSuite.left outer with watermark range condition
### Why are the changes needed?
Currently, there are no tests to use day-time intervals.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New assertions.
Closes#32953 from sarutak/stream-join-interval.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Extend the Cast expression and support StringType in casting to TimestampWithoutTZType.
Closes#32898
### Why are the changes needed?
To conform the ANSI SQL standard which requires to support such casting.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32936 from gengliangwang/castStringToTswtz.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR is a follow-up for SPARK-34382. It refines the lateral join syntax to only allow the LATERAL keyword to be in front of subqueries, instead of all `relationPriamry`. For example, `SELECT * FROM t1, LATERAL t2` should not be allowed.
### Why are the changes needed?
To be consistent with Postgres.
### Does this PR introduce _any_ user-facing change?
Yes. After this PR, the LATERAL keyword can only be in front of subqueries.
```scala
sql("SELECT * FROM t1, LATERAL t2")
org.apache.spark.sql.catalyst.parser.ParseException:
LATERAL can only be used with subquery(line 1, pos 26)
== SQL ==
select * from t1, lateral t2
--------------------------^^^
```
### How was this patch tested?
New unit tests.
Closes#32937 from allisonwang-db/spark-35789-lateral-join-parser.
Authored-by: allisonwang-db <allison.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/32513 added the case class `CommandResult` in package `org.apache.spark.sql.expression`. It is not suitable, so this PR move `CommandResult` from `org.apache.spark.sql.expression` to `org.apache.spark.sql.catalyst.plans.logical`.
### Why are the changes needed?
Make `CommandResult` in suitable package.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
No need.
Closes#32942 from beliefer/SPARK-35378-followup.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
There might be `SparkPlan` nodes where canonicalization on executor side can cause issues. This is a follow-up fix to conversation https://github.com/apache/spark/pull/32885/files#r651019687.
### Why are the changes needed?
To avoid potential NPEs when canonicalization happens on executors.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing UTs.
Closes#32947 from peter-toth/SPARK-35798-fix-sparkplan.sqlcontext-usage.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`RelationConversions` is actually an optimization rule while it's executed in the analysis phase.
For view, it's designed to only capture semantic configs, so we should ignore the optimization
configs that will be used in the analysis phase.
This PR also fixes the issue that view resolution will always use the default value for uncaptured config
### Why are the changes needed?
Bugfix
### Does this PR introduce _any_ user-facing change?
Yes, after this PR view resolution will respect the values set in the current session for the below configs
```
"spark.sql.hive.convertMetastoreParquet"
"spark.sql.hive.convertMetastoreOrc"
"spark.sql.hive.convertInsertingPartitionedTable"
"spark.sql.hive.convertMetastoreCtas"
```
### How was this patch tested?
By running new UT:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *HiveSQLViewSuite"
```
Closes#32941 from linhongliu-db/SPARK-35792-ignore-convert-configs.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Support Parse DayTimeIntervalType from JSON
### Why are the changes needed?
this will allow to store day-second intervals as table columns into Hive external catalog.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#32930 from AngersZhuuuu/SPARK-35732.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/32513
It's hard to keep the command execution name for `DataFrameWriter`, as the command logical plan is a bit messy (DS v1, file source and hive and different command logical plans) and sometimes it's hard to distinguish "insert" and "save".
However, `DataFrameWriterV2` only produce v2 commands which are pretty clean. It's easy to keep the command execution name for them.
### Why are the changes needed?
less breaking changes.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#32919 from cloud-fan/follow.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Providing a new function make_dt_interval to construct DayTimeIntervalType value
### Why are the changes needed?
As the JIRA described, we should provide a function to construct DayTimeIntervalType value
### Does this PR introduce _any_ user-facing change?
Yes, a new make_dt_interval function provided
### How was this patch tested?
Updated UTs, manual testing
Closes#32601 from copperybean/work.
Authored-by: copperybean <copperybean.zhang@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Parse YearMonthIntervalType from JSON.
### Why are the changes needed?
This will allow to store year-month intervals as table columns into Hive external catalog.
### Does this PR introduce _any_ user-facing change?
People can store year-month interval types as json string.
### How was this patch tested?
Added UT.
Closes#32929 from AngersZhuuuu/SPARK-35770.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
NULL-aware ANTI join (https://issues.apache.org/jira/browse/SPARK-32290) detects NULL join keys during building the map for `HashedRelation`, and will immediately return `HashedRelationWithAllNullKeys` without taking care of the map built already. Before returning `HashedRelationWithAllNullKeys`, the map needs to be freed properly to save memory and keep memory accounting correctly.
### Why are the changes needed?
Save memory and keep memory accounting correctly for the join query.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests introduced in https://github.com/apache/spark/pull/29104 .
Closes#32939 from c21/free-null-aware.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Set the list of read columns in the task configuration to reduce reading of ORC data.
### Why are the changes needed?
Now, the ORC reader will read all columns of the ORC table when the task configuration does not set the list of read columns . Therefore, we should set the list of read columns in the task configuration to reduce reading of ORC data.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
exist unittests
Closes#32923 from weixiuli/SPARK-35783.
Authored-by: weixiuli <weixiuli@jd.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Remove the usage of the enumerating subclasses of `StateStoreCustomMetric` dependency.
To achieve it, add couple of utility methods to `StateStoreCustomMetric`
* `withNewDesc(desc : String)` to `StateStoreCustomMetric` for cloning the instance with a new `desc` (currently used in `SymmetricHashJoinStateManager`)
* `createSQLMetric(sparkContext: sparkContext): SQLMetric` for creating a corresponding `SQLMetric` to show the metric in UI and accumulate at the query level (currently used in `statefulOperator. stateStoreCustomMetrics`)
### Why are the changes needed?
Code in [SymmetricHashJoinStateManager](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/SymmetricHashJoinStateManager.scala#L321) and [StateStoreWriter](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/statefulOperators.scala#L129) rely on the subclass implementations of [StateStoreCustomMetric](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/StateStore.scala#L187).
If a new subclass of `StateStoreCustomMetric` is added, it requires code changes to `SymmetricHashJoinStateManager` and `StateStoreWriter`, and we may miss the update if there is no existing test coverage.
To prevent these issues add a couple of utility methods to `StateStoreCustomMetric` as mentioned above.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UT and a new UT
Closes#32914 from vkorukanti/SPARK-35763.
Authored-by: Venki Korukanti <venki.korukanti@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This upgrade default Hadoop version from 3.2.1 to 3.3.1. The changes here are simply update the version number and dependency file.
### Why are the changes needed?
Hadoop 3.3.1 just came out, which comes with many client-side improvements such as for S3A/ABFS (20% faster when accessing S3). These are important for users who want to use Spark in a cloud environment.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Existing unit tests in Spark
- Manually tested using my S3 bucket for event log dir:
```
bin/spark-shell \
-c spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID \
-c spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY \
-c spark.eventLog.enabled=true
-c spark.eventLog.dir=s3a://<my-bucket>
```
- Manually tested against docker-based YARN dev cluster, by running `SparkPi`.
Closes#30135 from sunchao/SPARK-29250.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
It will not generate `tmpOutputFile`, `tmpErrOutputFile` and `sessionDirs` since [SPARK-35286](https://issues.apache.org/jira/browse/SPARK-35286). So we can remove `HiveClientImpl.closeState` to avoid these exceptions:
```
java.lang.NoSuchMethodError: org.apache.hadoop.hive.ql.session.SessionState.getTmpErrOutputFile()Ljava/io/File
```
### Why are the changes needed?
1. Avoid incompatible exceptions.
2. Remove useless code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Pass the GitHub Action
- Manual test：
Execute
```
mvn clean install -DskipTests -pl sql/hive -am
mvn test -pl sql/hive -DwildcardSuites=org.apache.spark.sql.hive.client.VersionsSuite -Dtest=none
```
**Before**
```
Run completed in 17 minutes, 18 seconds.
Total number of tests run: 867
Suites: completed 2, aborted 0
Tests: succeeded 867, failed 0, canceled 0, ignored 1, pending 0
All tests passed.
15:04:02.407 WARN org.apache.hadoop.hive.metastore.ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 2.3.0
15:04:02.408 WARN org.apache.hadoop.hive.metastore.ObjectStore: setMetaStoreSchemaVersion called but recording version is disabled: version = 2.3.0, comment = Set by MetaStore yangjie010.2.30.21
15:04:02.441 WARN org.apache.hadoop.hive.metastore.ObjectStore: Failed to get database default, returning NoSuchObjectException
15:04:03.140 ERROR org.apache.spark.util.Utils: Uncaught exception in thread shutdown-hook-0
java.lang.NoSuchMethodError: org.apache.hadoop.hive.ql.session.SessionState.getTmpErrOutputFile()Ljava/io/File;
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$closeState$1(HiveClientImpl.scala:168)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$withHiveState$1(HiveClientImpl.scala:312)
at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:243)
at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:242)
at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:292)
at org.apache.spark.sql.hive.client.HiveClientImpl.closeState(HiveClientImpl.scala:158)
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$new$1(HiveClientImpl.scala:175)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:214)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$2(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1994)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$1(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at scala.util.Try$.apply(Try.scala:213)
at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
15:04:03.141 WARN org.apache.hadoop.util.ShutdownHookManager: ShutdownHook '$anon$2' failed, java.util.concurrent.ExecutionException: java.lang.NoSuchMethodError: org.apache.hadoop.hive.ql.session.SessionState.getTmpErrOutputFile()Ljava/io/File;
java.util.concurrent.ExecutionException: java.lang.NoSuchMethodError: org.apache.hadoop.hive.ql.session.SessionState.getTmpErrOutputFile()Ljava/io/File;
at java.util.concurrent.FutureTask.report(FutureTask.java:122)
at java.util.concurrent.FutureTask.get(FutureTask.java:206)
at org.apache.hadoop.util.ShutdownHookManager.executeShutdown(ShutdownHookManager.java:124)
at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:95)
Caused by: java.lang.NoSuchMethodError: org.apache.hadoop.hive.ql.session.SessionState.getTmpErrOutputFile()Ljava/io/File;
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$closeState$1(HiveClientImpl.scala:168)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$withHiveState$1(HiveClientImpl.scala:312)
at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:243)
at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:242)
at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:292)
at org.apache.spark.sql.hive.client.HiveClientImpl.closeState(HiveClientImpl.scala:158)
at org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$new$1(HiveClientImpl.scala:175)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:214)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$2(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1994)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$1(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at scala.util.Try$.apply(Try.scala:213)
at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
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)
```
**After**
```
Run completed in 11 minutes, 41 seconds.
Total number of tests run: 867
Suites: completed 2, aborted 0
Tests: succeeded 867, failed 0, canceled 0, ignored 1, pending 0
All tests passed.
```
Closes#32693 from LuciferYang/SPARK-35556.
Lead-authored-by: YangJie <yangjie01@baidu.com>
Co-authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
### What changes were proposed in this pull request?
Currently, `ResolveAggregateFunctions` is a complicated rule that recursively calls the entire analyzer to resolve aggregate functions in parent nodes of aggregate. It's kind of necessary as we need to do many things to identify the aggregate function and push it down to the aggregate node: resolve columns as if they are in the aggregate node, resolve functions, apply type coercion, etc. However, this is overly complicated and it's hard to fully understand how the resolution is done there. It also leads to hacks such as the [char/varchar hack](https://github.com/apache/spark/blob/v3.1.2/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L2396-L2401), [subquery hack](https://github.com/apache/spark/blob/v3.1.2/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L2274-L2277), [grouping function hack](https://github.com/apache/spark/blob/v3.1.2/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L2465-L2467), etc.
This PR simplifies the `ResolveAggregateFunctions` rule and clarifies the resolution logic. To resolve aggregate functions/grouping columns in HAVING, ORDER BY and `df.where`, we expand the aggregate node below to output these required aggregate functions/grouping columns. In details, when resolving an expression from the parent of an aggregate node:
1. try to resolve columns with `agg.child` and wrap the result with `TempResolvedColumn`.
2. try to resolve subqueries with `agg.child`
3. if the expression is not resolved, return it and wait for other rules to resolve it, such as resolve functions, type coercions, etc.
4. if the expression is resolved, we transform it and push aggregate functions/grouping columns into the aggregate node below.
4.1 the expression may already present in `agg.aggregateExpressions`, we can simply replace the expression with attr ref.
4.2 if a `TempResolvedColumn` is neither inside an aggregate function, or wrap a grouping column, turn it back to an `UnresolvedAttribute`
5. after the main resolution batch, remove all `TempResolvedColumn` and turn them back to `UnresolvedAttribute`.
### Why are the changes needed?
Code cleanup
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing test
Closes#32470 from cloud-fan/agg2.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to format year-month interval to strings using the start and end fields of `YearMonthIntervalType`.
### Why are the changes needed?
Currently, they are ignored, and any `YearMonthIntervalType` is formatted as `INTERVAL YEAR TO MONTH`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#32924 from sarutak/year-month-interval-format.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR extends the parser rules to be able to parse the following types:
* INTERVAL YEAR
* INTERVAL YEAR TO MONTH
* INTERVAL MONTH
### Why are the changes needed?
For ANSI compliance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New assertion.
Closes#32922 from sarutak/parse-any-year-month.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Cache commonly occurring duplicate Some objects in SQLMetrics by using a Guava cache and reusing the existing Guava String Interner to avoid duplicate strings in JSONProtocol. Also with AccumulatorV2 we have seen lot of Some(-1L) and Some(0L) occurrences in a heap dump that is naively interned by having reusing a already constructed Some(-1L) and Some(0L)
To give some context on the impact and the garbage got accumulated, below are the details of the complex spark job which we troubleshooted and figured out the bottlenecks. **tl;dr - In short, major issues were the accumulation of duplicate objects mainly from SQLMetrics.**
Greater than 25% of the 40G driver heap filled with (a very large number of) **duplicate**, immutable objects.
1. Very large number of **duplicate** immutable objects.
- Type of metric is represented by `'scala.Some("sql")'` - which is created for each metric.
- Fixing this reduced memory usage from 4GB to a few bytes.
2. `scala.Some(0)` and `scala.Some(-1)` are very common metric values (typically to indicate absence of metric)
- Individually the values are all immutable, but spark sql was creating a new instance each time.
- Intern'ing these resulted in saving ~4.5GB for a 40G heap.
3. Using string interpolation for metric names.
- Interpolation results in creation of a new string object.
- We end up with a very large number of metric names - though the number of unique strings is miniscule.
- ~7.5 GB in the 40 GB heap : which went down to a few KB's when fixed.
### Why are the changes needed?
To reduce overall driver memory footprint which eventually reduces the Full GC pauses.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Since these are memory related optimizations, unit tests are not added. These changes are added in our internal platform which made it possible for one of the complex spark job continuously failing to succeed along with other set of optimizations.
Closes#32754 from venkata91/SPARK-35613.
Authored-by: Venkata krishnan Sowrirajan <vsowrirajan@linkedin.com>
Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
### What changes were proposed in this pull request?
This PR improves `Repartition` and `RepartitionByExpr` statistics estimation using child statistics.
### Why are the changes needed?
The current implementation will missing column stat. For example:
```sql
CREATE TABLE t1 USING parquet AS SELECT id % 10 AS key FROM range(100);
ANALYZE TABLE t1 COMPUTE STATISTICS FOR ALL COLUMNS;
set spark.sql.cbo.enabled=true;
EXPLAIN COST SELECT key FROM (SELECT key FROM t1 DISTRIBUTE BY key) t GROUP BY key;
```
Before this PR:
```
== Optimized Logical Plan ==
Aggregate [key#2950L], [key#2950L], Statistics(sizeInBytes=1600.0 B)
+- RepartitionByExpression [key#2950L], Statistics(sizeInBytes=1600.0 B, rowCount=100)
+- Relation default.t1[key#2950L] parquet, Statistics(sizeInBytes=1600.0 B, rowCount=100)
```
After this PR:
```
== Optimized Logical Plan ==
Aggregate [key#2950L], [key#2950L], Statistics(sizeInBytes=160.0 B, rowCount=10)
+- RepartitionByExpression [key#2950L], Statistics(sizeInBytes=1600.0 B, rowCount=100)
+- Relation default.t1[key#2950L] parquet, Statistics(sizeInBytes=1600.0 B, rowCount=100)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32309 from wangyum/SPARK-35203.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/31964
We should only quote the column name when nested column predicate pushdown is enabled, otherwise the data source side may not have the logic to parse the quoted column name and fail. This is not a problem before #31964 , as we don't quote the column name if there is no dot in the name. But #31964 changed it.
### Why are the changes needed?
fix a query failure
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new test
Closes#32807 from cloud-fan/bug.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This is to fix the maximal allowed number of rows check in `BroadcastExchangeExec`. After https://github.com/apache/spark/pull/27828, the max number of rows is calculated based on max capacity of `BytesToBytesMap` (previous value before the PR is 512000000). This calculation is not accurate as only `UnsafeHashedRelation` is using `BytesToBytesMap`. `LongHashedRelation` (used for broadcast join on key with long data type) has limit of [512000000](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala#L584), and `BroadcastNestedLoopJoinExec` is not depending on `HashedRelation` at all.
The change is to only specialize the max rows limit when needed. Keep other broadcast case with the previous limit - 512000000.
### Why are the changes needed?
Fix code logic and avoid unexpected behavior.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#32911 from c21/broadcast.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Remove commons-httpclient as a direct dependency for Hadoop-3.2 profile.
Hadoop-2.7 profile distribution still has it, hadoop-client has a compile dependency on commons-httpclient, thus we cannot remove it for Hadoop-2.7 profile.
```
[INFO] +- org.apache.hadoop:hadoop-client:jar:2.7.4:compile
[INFO] | +- org.apache.hadoop:hadoop-common:jar:2.7.4:compile
[INFO] | | +- commons-cli:commons-cli:jar:1.2:compile
[INFO] | | +- xmlenc:xmlenc:jar:0.52:compile
[INFO] | | +- commons-httpclient:commons-httpclient:jar:3.1:compile
```
### Why are the changes needed?
Spark is pulling in commons-httpclient as a dependency directly. commons-httpclient went EOL years ago and there are most likely CVEs not being reported against it, thus we should remove it.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Existing unittests
- Checked the dependency tree before and after introducing the changes
Before:
```
./build/mvn dependency:tree -Phadoop-3.2 | grep -i "commons-httpclient"
Using `mvn` from path: /usr/bin/mvn
[INFO] +- commons-httpclient:commons-httpclient:jar:3.1:compile
[INFO] | +- commons-httpclient:commons-httpclient:jar:3.1:provided
```
After
```
./build/mvn dependency:tree | grep -i "commons-httpclient"
Using `mvn` from path: /Users/sumeet.gajjar/cloudera/upstream-spark/build/apache-maven-3.6.3/bin/mvn
```
P.S. Reopening this since [spark upgraded](463daabd5a) its `hive.version` to `2.3.9` which does not have a dependency on `commons-httpclient`.
Closes#32912 from sumeetgajjar/SPARK-35429.
Authored-by: Sumeet Gajjar <sumeetgajjar93@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Extend `YearMonthIntervalType` to support interval fields. Valid interval field values:
- 0 (YEAR)
- 1 (MONTH)
After the changes, the following year-month interval types are supported:
1. `YearMonthIntervalType(0, 0)` or `YearMonthIntervalType(YEAR, YEAR)`
2. `YearMonthIntervalType(0, 1)` or `YearMonthIntervalType(YEAR, MONTH)`. **It is the default one**.
3. `YearMonthIntervalType(1, 1)` or `YearMonthIntervalType(MONTH, MONTH)`
Closes#32825
### Why are the changes needed?
In the current implementation, Spark supports only `interval year to month` but the SQL standard allows to specify the start and end fields. The changes will allow to follow ANSI SQL standard more precisely.
### Does this PR introduce _any_ user-facing change?
Yes but `YearMonthIntervalType` has not been released yet.
### How was this patch tested?
By existing test suites.
Closes#32909 from MaxGekk/add-fields-to-YearMonthIntervalType.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
`CoalesceExec` needlessly calls `child.execute` twice when it could just call it once and re-use the results. This only happens when `numPartitions == 1`.
### Why are the changes needed?
It is more efficient to execute the child plan once rather than twice.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
There are no functional changes. This is just a performance optimization, so the existing tests should be sufficient to catch any regressions.
Closes#32920 from andygrove/coalesce-exec-executes-twice.
Authored-by: Andy Grove <andygrove73@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add a new function to support construct YearMonthIntervalType from integral fields
### Why are the changes needed?
Add a new function to support construct YearMonthIntervalType from integral fields
### Does this PR introduce _any_ user-facing change?
Yea user can use `make_ym_interval` to construct TearMonthIntervalType from years/months integral fields
### How was this patch tested?
Added UT
Closes#32645 from AngersZhuuuu/SPARK-35129.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Currently, the file CastSuite.scala becomes big: 2000 lines, 2 base classes, 4 test suites.
In my previous work of Timestamp without time zone, I planned to put new test cases in CastSuiteBase, but they were accidentally added in AnsiCastSuiteBase.
This PR is to break the file down into 3 files. It also moves the test cases about timestamp without time zone to the right base class.
### Why are the changes needed?
Make development and review easier.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests
Closes#32918 from gengliangwang/refactorCastSuite.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Extended `RemoveRedundantAggregates` to remove deduplicating aggregations before aggregations that ignore duplicates.
### Why are the changes needed?
Performance imporovement.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Extending existing UT
Closes#32904 from tanelk/SPARK-33122_followup2_distinct_agg.
Authored-by: Tanel Kiis <tanel.kiis@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR group exception messages in `sql/core/src/main/scala/org/apache/spark/sql/execution/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#32880 from beliefer/SPARK-35056.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
In the PR, I propose to override the typeName() method in TimestampWithoutTZType, and assign it a name according to the ANSI SQL standard
### Why are the changes needed?
To improve Spark SQL user experience, and have readable types in error messages.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32915 from gengliangwang/typename.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Currently, there are some expressions that overwrite `semanticEquals`, which makes it not symmetrical. Ideally, expressions should overwrite `canonicalized` instead of `semanticEquals`.
This PR marks `semanticEquals` as final, and implement `canonicalized` for the few expressions that overwrote `semanticEquals` before.
### Why are the changes needed?
To avoid subtle bugs (I haven't found a real bug yet).
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
a new test
Closes#32885 from cloud-fan/attr.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup PR for SPARK-35736(#32893) and SPARK-35737(#32892).
This PR moves a common logic to `object DayTimeIntervalType`.
That logic is like `val strToFieldIndex = DayTimeIntervalType.dayTimeFields.map(i => DayTimeIntervalType.fieldToString(i) -> (i).toMap`, a `Map` which maps each time unit to the corresponding day-time field index.
### Why are the changes needed?
That logic appeared in the change in SPARK-35736 and SPARK-35737 so it can be a common logic and it's better to avoid the similar logic scattered.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#32905 from sarutak/followup-SPARK-35736-35737.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR fixes `StreamingJoinHelper` to be able to handle day-time interval.
### Why are the changes needed?
In the current master, `StreamingJoinHelper.getStateValueWatermark` can't handle conditions which contain day-time interval literals.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New assertions added to `StreamingJoinHlelperSuite`.
Closes#32896 from sarutak/streamingjoinhelper-daytime.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR add a feature which parse day-time interval literals to tightest type.
### Why are the changes needed?
To comply with the ANSI behavior.
For example, `INTERVAL '10 20:30' DAY TO MINUTE` should be parsed as `DayTimeIntervalType(DAY, MINUTE)` but not as `DayTimeIntervalType(DAY, SECOND)`.
### Does this PR introduce _any_ user-facing change?
No because `DayTimeIntervalType` will be introduced in `3.2.0`.
### How was this patch tested?
New tests.
Closes#32892 from sarutak/tight-daytime-interval.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR adda a feature which allow the parser parse any day-time interval types in SQL.
### Why are the changes needed?
To comply with ANSI standard, we additionally need to support the following types.
* INTERVAL DAY
* INTERVAL DAY TO HOUR
* INTERVAL DAY TO MINUTE
* INTERVAL HOUR
* INTERVAL HOUR TO MINUTE
* INTERVAL HOUR TO SECOND
* INTERVAL MINUTE
* INTERVAL MINUTE TO SECOND
* INTERVAL SECOND
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New tests.
Closes#32893 from sarutak/parse-any-day-time.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
1. Extend the Cast expression and support TimestampType in casting to TimestampWithoutTZType.
2. There was a mistake in casting TimestampWithoutTZType as TimestampType in https://github.com/apache/spark/pull/32864. The target value should be `sourceValue - timeZoneOffset` instead of being the same value.
### Why are the changes needed?
To conform the ANSI SQL standard which requires to support such casting.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32878 from gengliangwang/timestampToTimestampWithoutTZ.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Using copy-on-write for `SQLConf.sqlConfEntries` and `SQLConf.staticConfKeys` to reduce contention in concurrent workloads.
### Why are the changes needed?
The global locks used to protect `SQLConf.sqlConfEntries` map and the `SQLConf.staticConfKeys` set can cause significant contention on the `SQLConf` instance in a concurrent setting.
Using copy-on-write versions should reduce the contention given that modifications to the configs are relatively rare.
Closes#32865 from haiyangsun-db/SPARK-35701.
Authored-by: Haiyang Sun <haiyang.sun@databricks.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR add a feature which formats day-time interval to strings using the start and end fields of `DayTimeIntervalType`.
### Why are the changes needed?
Currently, they are ignored, and any `DayTimeIntervalType` is formatted as `INTERVAL DAY TO SECOND.`
### Does this PR introduce _any_ user-facing change?
Yes. The format of day-time intervals is determined the start and end fields.
### How was this patch tested?
New test.
Closes#32891 from sarutak/interval-format.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Instantiate a new Hive client through `Hive.getWithoutRegisterFns(conf, false)` instead of `Hive.get(conf)`, if `Hive` version is >= '2.3.9' (the built-in version).
### Why are the changes needed?
[HIVE-10319](https://issues.apache.org/jira/browse/HIVE-10319) introduced a new API `get_all_functions` which is only supported in Hive 1.3.0/2.0.0 and up. As result, when Spark 3.x talks to a HMS service of version 1.2 or lower, the following error will occur:
```
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: org.apache.thrift.TApplicationException: Invalid method name: 'get_all_functions'
at org.apache.hadoop.hive.ql.metadata.Hive.getAllFunctions(Hive.java:3897)
at org.apache.hadoop.hive.ql.metadata.Hive.reloadFunctions(Hive.java:248)
at org.apache.hadoop.hive.ql.metadata.Hive.registerAllFunctionsOnce(Hive.java:231)
... 96 more
Caused by: org.apache.thrift.TApplicationException: Invalid method name: 'get_all_functions'
at org.apache.thrift.TServiceClient.receiveBase(TServiceClient.java:79)
at org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.recv_get_all_functions(ThriftHiveMetastore.java:3845)
at org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.get_all_functions(ThriftHiveMetastore.java:3833)
```
The `get_all_functions` is called only when `doRegisterAllFns` is set to true:
```java
private Hive(HiveConf c, boolean doRegisterAllFns) throws HiveException {
conf = c;
if (doRegisterAllFns) {
registerAllFunctionsOnce();
}
}
```
what this does is to register all Hive permanent functions defined in HMS in Hive's `FunctionRegistry` class, via iterating through results from `get_all_functions`. To Spark, this seems unnecessary as it loads Hive permanent (not built-in) UDF via directly calling the HMS API, i.e., `get_function`. The `FunctionRegistry` is only used in loading Hive's built-in function that is not supported by Spark. At this time, it only applies to `histogram_numeric`.
[HIVE-21563](https://issues.apache.org/jira/browse/HIVE-21563) introduced a new API `getWithoutRegisterFns` which skips the above registration and is available in Hive 2.3.9. Therefore, Spark should adopt it to avoid the cost.
### Does this PR introduce _any_ user-facing change?
Yes with this fix Spark now should be able to talk to HMS server with Hive 1.2.x and lower.
### How was this patch tested?
Manually started a HMS server of Hive version 1.2.2. Without the PR it failed with the above exception. With the PR the error disappeared and I can successfully perform common operations such as create table, create database, list tables, etc.
Closes#32887 from sunchao/SPARK-35321-new.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
### What changes were proposed in this pull request?
This patch adds log warn when `keyWithIndexToValue` returns null value in `SymmetricHashJoinStateManager`.
### Why are the changes needed?
Once we get null from state store in SymmetricHashJoinStateManager, it is better to add meaningful logging for the case. It is better for debugging.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#32828 from viirya/fix-ss-joinstatemanager-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This PR is used to do add a UT to check if user-defined cached batch are completely released when clearCache called.
### Why are the changes needed?
Add a new UT file RefCountedTestCachedBatchSerializerSuite.scala
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
UT is added, org.apache.spark.sql.execution.columnar.RefCountedTestCachedBatchSerializerSuite
Closes#32717 from xuechendi/support_manual_close_in_InMemoryRelation.
Authored-by: Chendi Xue <chendi.xue@intel.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Extend DayTimeIntervalType to support interval fields. Valid interval field values:
- 0 (DAY)
- 1 (HOUR)
- 2 (MINUTE)
- 3 (SECOND)
After the changes, the following day-time interval types are supported:
1. `DayTimeIntervalType(0, 0)` or `DayTimeIntervalType(DAY, DAY)`
2. `DayTimeIntervalType(0, 1)` or `DayTimeIntervalType(DAY, HOUR)`
3. `DayTimeIntervalType(0, 2)` or `DayTimeIntervalType(DAY, MINUTE)`
4. `DayTimeIntervalType(0, 3)` or `DayTimeIntervalType(DAY, SECOND)`. **It is the default one**. The second fraction precision is microseconds.
5. `DayTimeIntervalType(1, 1)` or `DayTimeIntervalType(HOUR, HOUR)`
6. `DayTimeIntervalType(1, 2)` or `DayTimeIntervalType(HOUR, MINUTE)`
7. `DayTimeIntervalType(1, 3)` or `DayTimeIntervalType(HOUR, SECOND)`
8. `DayTimeIntervalType(2, 2)` or `DayTimeIntervalType(MINUTE, MINUTE)`
9. `DayTimeIntervalType(2, 3)` or `DayTimeIntervalType(MINUTE, SECOND)`
10. `DayTimeIntervalType(3, 3)` or `DayTimeIntervalType(SECOND, SECOND)`
### Why are the changes needed?
In the current implementation, Spark supports only `interval day to second` but the SQL standard allows to specify the start and end fields. The changes will allow to follow ANSI SQL standard more precisely.
### Does this PR introduce _any_ user-facing change?
Yes but `DayTimeIntervalType` has not been released yet.
### How was this patch tested?
By existing test suites.
Closes#32849 from MaxGekk/day-time-interval-type-units.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Collect observed metrics from cached and adaptive execution sub-trees.
### Why are the changes needed?
Currently persisting/caching will hide all observed metrics in that sub-tree from reaching the `QueryExecutionListeners`. Adaptive query execution can also hide the metrics from reaching `QueryExecutionListeners`.
### Does this PR introduce _any_ user-facing change?
Bugfix
### How was this patch tested?
New UTs
Closes#32862 from tanelk/SPARK-35695_collect_metrics_persist.
Lead-authored-by: Tanel Kiis <tanel.kiis@gmail.com>
Co-authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The STRUCT type syntax is defined like this:
STRUCT(fieldNmae: fileType [NOT NULL][COMMENT stringLiteral][,.....])
So the field list is nearly the same as a column list
if we could make ':' optional it would be so much cleaner an less proprietary
### Why are the changes needed?
ease of use
### Does this PR introduce _any_ user-facing change?
Yes, you can use Struct type list is nearly the same as a column list
### How was this patch tested?
unit tests
Closes#32858 from jerqi/master.
Authored-by: RoryQi <1242949407@qq.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
It seems like spark inner join is performing a cartesian join in self joining using `joinWith`
To produce this issues:
```
val df = spark.range(0,3)
df.joinWith(df, df("id") === df("id")).show()
```
Before this pull request, the result is
+---+---+
| _1 | _2 |
+---+---+
| 0 | 0 |
| 0 | 1 |
| 0 | 2 |
| 1 | 0 |
| 1 | 1 |
| 1 | 2 |
| 2 | 0 |
| 2 | 1 |
| 2 | 2 |
+---+---+
The expected result is
+---+---+
| _1 | _2 |
+---+---+
| 0 | 0 |
| 1 | 1 |
| 2 | 2 |
+---+---+
### Why are the changes needed?
correctness
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
add test
Closes#32863 from dgd-contributor/SPARK-35652_join_and_joinWith_in_seft_joining.
Authored-by: dgd-contributor <dgd_contributor@viettel.com.vn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup of #32586. We introduced `ExpressionContainmentOrdering` to sort common expressions according to their parent-child relations. For unrelated expressions, previously the ordering returns -1 which is not correct and can possibly lead to transitivity issue.
### Why are the changes needed?
To fix the possible transitivity issue of `ExpressionContainmentOrdering`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#32870 from viirya/SPARK-35439-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Extend the Cast expression and support DateType in casting to TimestampWithoutTZType.
### Why are the changes needed?
To conform the ANSI SQL standard which requires to support such casting.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32873 from gengliangwang/dateToTswtz.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Remove duplicated code in the form of `readXXX` in `VectorizedRleValuesReader`. For instance:
```java
public void readIntegers(
int total,
WritableColumnVector c,
int rowId,
int level,
VectorizedValuesReader data) throws IOException {
int left = total;
while (left > 0) {
if (this.currentCount == 0) this.readNextGroup();
int n = Math.min(left, this.currentCount);
switch (mode) {
case RLE:
if (currentValue == level) {
data.readIntegers(n, c, rowId);
} else {
c.putNulls(rowId, n);
}
break;
case PACKED:
for (int i = 0; i < n; ++i) {
if (currentBuffer[currentBufferIdx++] == level) {
c.putInt(rowId + i, data.readInteger());
} else {
c.putNull(rowId + i);
}
}
break;
}
rowId += n;
left -= n;
currentCount -= n;
}
}
```
and replace with:
```java
public void readBatch(
int total,
int offset,
WritableColumnVector values,
int maxDefinitionLevel,
VectorizedValuesReader valueReader,
ParquetVectorUpdater updater) throws IOException {
int left = total;
while (left > 0) {
if (this.currentCount == 0) this.readNextGroup();
int n = Math.min(left, this.currentCount);
switch (mode) {
case RLE:
if (currentValue == maxDefinitionLevel) {
updater.updateBatch(n, offset, values, valueReader);
} else {
values.putNulls(offset, n);
}
break;
case PACKED:
for (int i = 0; i < n; ++i) {
if (currentBuffer[currentBufferIdx++] == maxDefinitionLevel) {
updater.update(offset + i, values, valueReader);
} else {
values.putNull(offset + i);
}
}
break;
}
offset += n;
left -= n;
currentCount -= n;
}
}
```
where the `ParquetVectorUpdater` is type specific, and has different implementations under `updateBatch` and `update`. Together, this also changes code paths handling timestamp types to use the batch read API for decoding definition levels.
2. Similar to the above, this removes code duplication in `VectorizedColumnReader.decodeDictionaryIds`. Now different implementations are under `ParquetVectorUpdater.decodeSingleDictionaryId`.
### Why are the changes needed?
`VectorizedRleValuesReader` and `VectorizedColumnReader` are becoming increasingly harder to maintain, as any change touches the above logic **will need to be replicated in 20+ places**. The issue becomes even more serious when we are going to implement column index (for instance, see how the change [here](https://github.com/apache/spark/pull/32753/files#diff-a01e174e178366aadf07f64ee690d47d343b2ca416a4a2b2ea735887c22d5934R191) has to be replicated multiple times) and complex type support (in progress) for the vectorized path.
In addition, currently dictionary decoding (see `VectorizedColumnReader.decodeDictionaryIds`) and non-dictionary decoding are handled separately, and therefore the same (very complicated) branching logic based on input Spark & Parquet types have to be replicated in two places, which is another burden for code maintenance.
The original intention is for performance. However these days JIT compilers tend to be very effective on this and will inline virtual calls aggressively to eliminate the method invocation costs (see [this](https://shipilev.net/blog/2015/black-magic-method-dispatch/) and [this](http://insightfullogic.com/blog/2014/may/12/fast-and-megamorphic-what-influences-method-invoca/)). I've also done benchmarks using a modified `DataSourceReadBenchmark` and `DateTimeRebaseBenchmark` and the result is almost exact the same before and after the change. The results can be found [here](https://gist.github.com/sunchao/674afbf942ccc2370bdcfa33efb4471c), and [here's](https://github.com/sunchao/spark/tree/parquet-refactor) the source code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#32777 from sunchao/SPARK-35640.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
This pr upgrades built-in Hive to 2.3.9. Hive 2.3.9 changes:
- [HIVE-17155] - findConfFile() in HiveConf.java has some issues with the conf path
- [HIVE-24797] - Disable validate default values when parsing Avro schemas
- [HIVE-24608] - Switch back to get_table in HMS client for Hive 2.3.x
- [HIVE-21200] - Vectorization: date column throwing java.lang.UnsupportedOperationException for parquet
- [HIVE-21563] - Improve Table#getEmptyTable performance by disabling registerAllFunctionsOnce
- [HIVE-19228] - Remove commons-httpclient 3.x usage
### Why are the changes needed?
Fix regression caused by AVRO-2035.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32750 from wangyum/SPARK-34512.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Extend the Cast expression and support TimestampWithoutTZType in casting to DateType.
### Why are the changes needed?
To conform the ANSI SQL standard which requires to support such casting.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32869 from gengliangwang/castToDate.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR fixes an issue that `Dataset.observe` doesn't work if `CollectMetricsExec` in a task handles multiple partitions.
If `coalesce` follows `observe` and the number of partitions shrinks after `coalesce`, `CollectMetricsExec` can handle multiple partitions in a task.
### Why are the changes needed?
The current implementation of `CollectMetricsExec` doesn't consider the case it can handle multiple partitions.
Because new `updater` is created for each partition even though those partitions belong to the same task, `collector.setState(updater)` raise an assertion error.
This is a simple reproducible example.
```
$ bin/spark-shell --master "local[1]"
scala> spark.range(1, 4, 1, 3).observe("my_event", count($"id").as("count_val")).coalesce(2).collect
```
```
java.lang.AssertionError: assertion failed
at scala.Predef$.assert(Predef.scala:208)
at org.apache.spark.sql.execution.AggregatingAccumulator.setState(AggregatingAccumulator.scala:204)
at org.apache.spark.sql.execution.CollectMetricsExec.$anonfun$doExecute$2(CollectMetricsExec.scala:72)
at org.apache.spark.sql.execution.CollectMetricsExec.$anonfun$doExecute$2$adapted(CollectMetricsExec.scala:71)
at org.apache.spark.TaskContext$$anon$1.onTaskCompletion(TaskContext.scala:125)
at org.apache.spark.TaskContextImpl.$anonfun$markTaskCompleted$1(TaskContextImpl.scala:124)
at org.apache.spark.TaskContextImpl.$anonfun$markTaskCompleted$1$adapted(TaskContextImpl.scala:124)
at org.apache.spark.TaskContextImpl.$anonfun$invokeListeners$1(TaskContextImpl.scala:137)
at org.apache.spark.TaskContextImpl.$anonfun$invokeListeners$1$adapted(TaskContextImpl.scala:135)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#32786 from sarutak/fix-collectmetricsexec.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Extend the Cast expression and support TimestampWithoutTZType in casting to TimestampType.
### Why are the changes needed?
To conform the ANSI SQL standard which requires to support such casting.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32864 from gengliangwang/castToTimestamp.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
#31637 removed the usage of `CheckAnalysis.checkAlterTablePartition` but didn't remove the function.
### Why are the changes needed?
To removed an unused function.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#32855 from imback82/SPARK-34524-followup.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Use `UnresolvedHint.resolved = child.resolved` instead `UnresolvedHint.resolved = false`, then the plan contains `UnresolvedHint` child can be optimized by rule in batch `Resolution`.
For instance, before this pr, the following plan can't be optimized by `ResolveReferences`.
```
!'Project [*]
+- SubqueryAlias __auto_generated_subquery_name
+- UnresolvedHint use_hash
+- Project [42 AS 42#10]
+- OneRowRelation
```
### Why are the changes needed?
fix hint in subquery bug
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#32841 from cfmcgrady/SPARK-35673.
Authored-by: Fu Chen <cfmcgrady@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### Why are the changes needed?
With Long.minValue cast to an instant, secs will be floored in function microsToInstant and cause overflow when multiply with Micros_per_second
```
def microsToInstant(micros: Long): Instant = {
val secs = Math.floorDiv(micros, MICROS_PER_SECOND)
// Unfolded Math.floorMod(us, MICROS_PER_SECOND) to reuse the result of
// the above calculation of `secs` via `floorDiv`.
val mos = micros - secs * MICROS_PER_SECOND <- it will overflow here
Instant.ofEpochSecond(secs, mos * NANOS_PER_MICROS)
}
```
But the overflow is acceptable because it won't produce any change to the result
However, when convert the instant back to micro value, it will raise Overflow Error
```
def instantToMicros(instant: Instant): Long = {
val us = Math.multiplyExact(instant.getEpochSecond, MICROS_PER_SECOND) <- It overflow here
val result = Math.addExact(us, NANOSECONDS.toMicros(instant.getNano))
result
}
```
Code to reproduce this error
```
instantToMicros(microToInstant(Long.MinValue))
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Test added
Closes#32839 from dgd-contributor/SPARK-35679_instantToMicro.
Authored-by: dgd-contributor <dgd_contributor@viettel.com.vn>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Add `PartitioningCollection` in EnsureRequirements during remove shuffle.
### Why are the changes needed?
Currently `EnsureRequirements` only check if child has semantic equal `HashPartitioning` and remove
redundant shuffle. We can enhance this case using `PartitioningCollection`.
### Does this PR introduce _any_ user-facing change?
Yes, plan might be changed.
### How was this patch tested?
Add test.
Closes#32815 from ulysses-you/shuffle-node.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Kent Yao <yao@apache.org>
### What changes were proposed in this pull request?
Add an `internalRegisterFunction` for the built-in function registry. So that
we can skip the unnecessary function normalization.
### Why are the changes needed?
small refactor
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing ut
Closes#32842 from linhongliu-db/function-refactor.
Lead-authored-by: Linhong Liu <linhong.liu@databricks.com>
Co-authored-by: Linhong Liu <67896261+linhongliu-db@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR changes an occurrence of `Seq` to `collections.Seq` in `NestedColumnAliasing`.
### Why are the changes needed?
In the current master, `NestedColumnAliasing` doesn't work with Scala 2.13 and the relevant tests fail.
The following are examples.
* `NestedColumnAliasingSuite`
* Subclasses of `SchemaPruningSuite`
* `ColumnPruningSuite`
```
NestedColumnAliasingSuite:
[info] - Pushing a single nested field projection *** FAILED *** (14 milliseconds)
[info] scala.MatchError: (none#211451,ArrayBuffer(name#211451.middle)) (of class scala.Tuple2)
[info] at org.apache.spark.sql.catalyst.optimizer.NestedColumnAliasing$.$anonfun$getAttributeToExtractValues$5(NestedColumnAliasing.scala:258)
[info] at scala.collection.StrictOptimizedMapOps.flatMap(StrictOptimizedMapOps.scala:31)
[info] at scala.collection.StrictOptimizedMapOps.flatMap$(StrictOptimizedMapOps.scala:30)
[info] at scala.collection.immutable.HashMap.flatMap(HashMap.scala:39)
[info] at org.apache.spark.sql.catalyst.optimizer.NestedColumnAliasing$.getAttributeToExtractValues(NestedColumnAliasing.scala:258)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Ran tests mentioned above and all passed with Scala 2.13.
Closes#32848 from sarutak/followup-SPARK-35194-2.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes the examples of `rand` and `randn`.
### Why are the changes needed?
SPARK-23643 (#20793) fixes an issue which is related to the seed and it causes the result of `rand` and `randn`.
Now the results of `SELECT rand(0)` and `SELECT randn((null)` are `0.7604953758285915` and `1.6034991609278433` respectively, and they should be deterministic because the number of partitions are always 1 (the leaf node is `OneRowRelation`).
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Built the doc and confirmed it.
Closes#32844 from sarutak/rand-example.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Extend the Cast expression and support TimestampWithoutTZType in casting to StringType.
### Why are the changes needed?
To conform the ANSI SQL standard which requires to support such casting.
### Does this PR introduce _any_ user-facing change?
No, the new timestamp type is not released yet.
### How was this patch tested?
Unit test
Closes#32846 from gengliangwang/tswtzToString.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR adds support for lateral subqueries. A lateral subquery is a subquery preceded by the `LATERAL` keyword in the FROM clause of a query that can reference columns in the preceding FROM items. For example:
```sql
SELECT * FROM t1, LATERAL (SELECT * FROM t2 WHERE t1.a = t2.c)
```
A new subquery expression`LateralSubquery` is used to represent a lateral subquery. It is similar to `ScalarSubquery` but can return multiple rows and columns. A new logical unary node `LateralJoin` is used to represent a lateral join.
Here is the analyzed plan for the above query:
```scala
Project [a, b, c, d]
+- LateralJoin lateral-subquery [a], Inner
: +- Project [c, d]
: +- Filter (outer(a) = c)
: +- Relation [c, d]
+- Relation [a, b]
```
Similar to a correlated subquery, a lateral subquery can be viewed as a dependent (nested loop) join where the evaluation of the right subtree depends on the current value of the left subtree. The same technique to decorrelate a subquery is used to decorrelate a lateral join:
```scala
Project [a, b, c, d]
+- LateralJoin lateral-subquery [a && a = c], Inner // pull up correlated predicates as join conditions
: +- Project [c, d]
: +- Relation [c, d]
+- Relation [a, b]
```
Then the lateral join can be rewritten into a normal join:
```scala
Join Inner (a = c)
:- Relation [a, b]
+- Relation [c, d]
```
#### Follow-ups:
1. Similar to rewriting correlated scalar subqueries, rewriting lateral joins is also subject to the COUNT bug (See SPARK-15370 for more details). This is **not** handled in the current PR as it requires a sizeable amount of refactoring. It will be addressed in a subsequent PR (SPARK-35551).
2. Currently Spark does use outer query references to resolve star expressions in subqueries. This is not lateral subquery specific and can be handled in a separate PR (SPARK-35618)
### Why are the changes needed?
To support an ANSI SQL feature.
### Does this PR introduce _any_ user-facing change?
Yes. It allows users to use lateral subqueries in the FROM clause of a query.
### How was this patch tested?
- Parser test: `PlanParserSuite.scala`
- Analyzer test: `ResolveSubquerySuite.scala`
- Optimizer test: `PullupCorrelatedPredicatesSuite.scala`
- SQL test: `join-lateral.sql`, `postgreSQL/join.sql`
Closes#32303 from allisonwang-db/spark-34382-lateral.
Lead-authored-by: allisonwang-db <66282705+allisonwang-db@users.noreply.github.com>
Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `TimestampWithoutTZType` to `DataTypeTestUtils.ordered`/`atomicTypes`, and implement values generation of those types in `LiteralGenerator`/`RandomDataGenerator`. In this way, the types will be tested automatically in:
1. ArithmeticExpressionSuite:
- "function least"
- "function greatest"
2. PredicateSuite
- "BinaryComparison consistency check"
- "AND, OR, EqualTo, EqualNullSafe consistency check"
3. ConditionalExpressionSuite
- "if"
4. RandomDataGeneratorSuite
- "Basic types"
5. CastSuite
- "null cast"
- "up-cast"
- "SPARK-27671: cast from nested null type in struct"
6. OrderingSuite
- "GenerateOrdering with TimestampWithoutTZType"
7. PredicateSuite
- "IN with different types"
8. UnsafeRowSuite
- "calling get(ordinal, datatype) on null columns"
9. SortSuite
- "sorting on TimestampWithoutTZType ..."
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the affected test suites.
Closes#32843 from gengliangwang/atomicTest.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The changed [unit test](https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingJoinSuite.scala#L566) was introduce in https://github.com/apache/spark/pull/21587, to fix the planner side of thing for stream-stream join. Ideally check the query result should catch the bug, but it would be better to add plan check to make the purpose of unit test more clearly and catch future bug from planner change.
### Why are the changes needed?
Improve unit test.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Changed test itself.
Closes#32836 from c21/ss-test.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Handle type coercion when resolving V2 function. In particular:
- prior to evaluating function arguments, insert cast whenever the argument type doesn't match the expected input type.
- use `BoundFunction.inputTypes()` to lookup magic method for scalar function
### Why are the changes needed?
For V2 functions, the actual argument types should not necessarily match those of the input types, and Spark should handle type coercion whenever it is needed.
### Does this PR introduce _any_ user-facing change?
Yes. Now V2 function resolution should be able to handle type coercion properly.
### How was this patch tested?
Added a few new tests.
Closes#32764 from sunchao/SPARK-35390.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR enhances `RepartitionByExpression` to make it coalesce partitions efficiently by AQE. Usually used to merge small files.
The basic logic is: Spark first tries to coalesce partitions, if it cannot be coalesced, then use the local shuffle reader to read data to avoid exchange the data over the network.
Usage:
```sql
SELECT /*+ REPARTITION */ * FROM t
```
```scala
df.repartition()
```
For example:
coalesce small output files | local shuffle reader
--- | ---
| 
### Why are the changes needed?
Coalesce partitions efficiently.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32781 from wangyum/SPARK-35650.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Write tests for timestamp without time zone in UDF as input parameters and results.
### Why are the changes needed?
It follows https://github.com/apache/spark/pull/31779 to improve test coverage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test
Closes#32840 from gengliangwang/tswtzUDF.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR group exception messages in `sql/hive/src/main/scala/org/apache/spark/sql/hive/client`.
### 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#32763 from beliefer/SPARK-35058.
Lead-authored-by: beliefer <beliefer@163.com>
Co-authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to extend Spark SQL API to accept `java.time.LocalDateTime` as an external type of recently added new Catalyst type - `TimestampWithoutTZ`. The Java class `java.time.LocalDateTime` has a similar semantic to ANSI SQL timestamp without timezone type, and it is the most suitable to be an external type for `TimestampWithoutTZType`. In more details:
* Added `TimestampWithoutTZConverter` which converts java.time.LocalDateTime instances to/from internal representation of the Catalyst type `TimestampWithoutTZType` (to Long type). The `TimestampWithoutTZConverter` object uses new methods of DateTimeUtils:
* localDateTimeToMicros() converts the input date time to the total length in microseconds.
* microsToLocalDateTime() obtains a java.time.LocalDateTime
* Support new type `TimestampWithoutTZType` in RowEncoder via the methods createDeserializerForLocalDateTime() and createSerializerForLocalDateTime().
* Extended the Literal API to construct literals from `java.time.LocalDateTime` instances.
### Why are the changes needed?
To allow users parallelization of `java.time.LocalDateTime` collections, and construct timestamp without time zone columns. Also to collect such columns back to the driver side.
### Does this PR introduce _any_ user-facing change?
The PR extends existing functionality. So, users can parallelize instances of the java.time.LocalDateTime class and collect them back.
```
scala> val ds = Seq(java.time.LocalDateTime.parse("1970-01-01T00:00:00")).toDS
ds: org.apache.spark.sql.Dataset[java.time.LocalDateTime] = [value: timestampwithouttz]
scala> ds.collect()
res0: Array[java.time.LocalDateTime] = Array(1970-01-01T00:00)
```
### How was this patch tested?
New unit tests
Closes#32814 from gengliangwang/LocalDateTime.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Move `assume` into methods at `PythonUDFSuite`.
### Why are the changes needed?
When we run Spark test with such command:
`./build/mvn -Phadoop-2.7 -Phive -Phive-thriftserver -Pyarn -Pkubernetes clean test`
get this exception:
```
PythonUDFSuite:
org.apache.spark.sql.execution.python.PythonUDFSuite *** ABORTED ***
java.lang.RuntimeException: Unable to load a Suite class that was discovered in the runpath: org.apache.spark.sql.execution.python.PythonUDFSuite
at org.scalatest.tools.DiscoverySuite$.getSuiteInstance(DiscoverySuite.scala:81)
at org.scalatest.tools.DiscoverySuite.$anonfun$nestedSuites$1(DiscoverySuite.scala:38)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
at scala.collection.Iterator.foreach(Iterator.scala:941)
at scala.collection.Iterator.foreach$(Iterator.scala:941)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
at scala.collection.IterableLike.foreach(IterableLike.scala:74)
at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
at scala.collection.AbstractIterable.foreach(Iterable.scala:56)
at scala.collection.TraversableLike.map(TraversableLike.scala:238)
```
The test env has no PYSpark module so it failed.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
manual
Closes#32833 from ulysses-you/SPARK-35687.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The implementation for the save operation of RocksDBFileManager.
### Why are the changes needed?
Save all the files in the given local checkpoint directory as a committed version in DFS.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New UT added.
Closes#32582 from xuanyuanking/SPARK-35436.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Currently, Spark eagerly executes commands on the caller side of `QueryExecution`, which is a bit hacky as `QueryExecution` is not aware of it and leads to confusion.
For example, if you run `sql("show tables").collect()`, you will see two queries with identical query plans in the web UI.
The first query is triggered at `Dataset.logicalPlan`, which eagerly executes the command.
The second query is triggered at `Dataset.collect`, which is the normal query execution.
From the web UI, it's hard to tell that these two queries are caused by eager command execution.
This PR proposes to move the eager command execution to `QueryExecution`, and turn the command plan to `CommandResult` to indicate that command has been executed already. Now `sql("show tables").collect()` still triggers two queries, but the quey plans are not identical. The second query becomes:
In addition to the UI improvements, this PR also has other benefits:
1. Simplifies code as caller side no need to worry about eager command execution. `QueryExecution` takes care of it.
2. It helps https://github.com/apache/spark/pull/32442 , where there can be more plan nodes above commands, and we need to replace commands with something like local relation that produces unsafe rows.
### Why are the changes needed?
Explained above.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing tests
Closes#32513 from beliefer/SPARK-35378.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Refactor LocalDateTimeUDT as YearUDT in UserDefinedTypeSuite
### Why are the changes needed?
As we are going to support java.time.LocalDateTime as an external type of TimestampWithoutTZ type https://github.com/apache/spark/pull/32814, registering java.time.LocalDateTime as UDT will cause test failures: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/139469/testReport/
This PR is to unblock https://github.com/apache/spark/pull/32814.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32824 from gengliangwang/UDTFollowUp.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR fixes an issue that both key and value of state schema cannot accept long length (>65535 bytes) JSON.
To solve the problem explained below, JSON represented schema is divided into chunks whose maximum length is 65535 bytes, and each chunk is written by `DataOutputStream.writeUTF`.
As the solution changes the format of the schema, the version is also changes from `1` to `2` but old version schema is still acceptable to ensures backward compatibility.
### Why are the changes needed?
In the current implementation, writing state schema fails if the length of schema exceeds 65535 bytes and `UTFDataFormatException` is thrown.
It's due to the limitation of `DataOutputStream.writeUTF`.
`writeUTF` writes a length field first and it's 2 bytes width, meaning the maximum content length is limited to `2^16-1`=`65535` bytes.
https://docs.oracle.com/javase/8/docs/api/java/io/DataOutputStream.html#writeUTF-java.lang.String-
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New tests.
Closes#32788 from sarutak/fix-UTFDataFormatException.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Fix Scala doc for removed parameters for `InvokeLike.invoke`.
### Why are the changes needed?
#32532 forgot to update the Scala doc after removing 2 parameters for `InvokeLike.invoke`. This fixes it.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes#32827 from sunchao/SPARK-35384-followup.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch removes the usage of putting null into StateStore.
### Why are the changes needed?
According to `get` method doc in `StateStore` API, it returns non-null row if the key exists. So basically we should avoid write null to `StateStore`. You cannot distinguish if the returned null row is because the key doesn't exist, or the value is actually null. And due to the defined behavior of `get`, it is quite easy to cause NPE error if the caller doesn't expect to get a null if the caller believes the key exists.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added test.
Closes#32796 from viirya/fix-ss-joinstatemanager.
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 patch introduces a new option to specify the minimum number of offsets to read per trigger i.e. minOffsetsPerTrigger and maxTriggerDelay to avoid the infinite wait for the trigger.
This new option will allow skipping trigger/batch when the number of records available in Kafka is low. This is a very useful feature in cases where we have a sudden burst of data at certain intervals in a day and data volume is low for the rest of the day.
'maxTriggerDelay' option will help to avoid cases of infinite delay in scheduling trigger and the trigger will happen irrespective of records available if the maxTriggerDelay time exceeds the last trigger. It would be an optional parameter with a default value of 15 mins. This option will be only applicable if minOffsetsPerTrigger is set.
minOffsetsPerTrigger option would be optional of course, but once specified it would take precedence over maxOffestsPerTrigger which will be honored only after minOffsetsPerTrigger is satisfied.
### Why are the changes needed?
There are many scenarios where there is a sudden burst of data at certain intervals in a day and data volume is low for the rest of the day. Tunning such jobs is difficult as decreasing trigger processing time increasing the number of batches and hence cluster resource usage and adds to small file issues. Increasing trigger processing time adds consumer lag. This patch tries to address this issue.
### How was this patch tested?
This patch was tested by adding test cases as well as manually on a cluster where the job was running for a full one day with a data burst happening once a day.
Here is the picture of databurst and hence consumer lag:
<img width="1198" alt="Screenshot 2021-04-29 at 11 39 35 PM" src="https://user-images.githubusercontent.com/1044003/116997587-9b2ab180-acfa-11eb-91fd-524802ce3316.png">
This is how the job behaved at burst time running every 4.5 mins (which is the specified trigger time):
<img width="1154" alt="Burst Time" src="https://user-images.githubusercontent.com/1044003/116997919-12f8dc00-acfb-11eb-9b0a-98387fc67560.png">
This is job behavior during the non-burst time where it is skipping 2 to 3 triggers and running once every 9 to 13.5 mins
<img width="1154" alt="Non Burst Time" src="https://user-images.githubusercontent.com/1044003/116998244-8b5f9d00-acfb-11eb-8340-33d47149ef81.png">
Here are some more stats from the two-run i.e. one normal run and the other with minOffsetsperTrigger set:
| Run | Data Size | Number of Batch Runs | Number of Files |
| ------------- | ------------- |------------- |------------- |
| Normal Run | 54.2 GB | 320 | 21968 |
| Run with minOffsetsperTrigger | 54.2 GB | 120 | 12104 |
Closes#32653 from satishgopalani/SPARK-35312.
Authored-by: Satish Gopalani <satish.gopalani@pubmatic.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Cleanup unreachable code.
### Why are the changes needed?
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existed test.
Closes#32791 from pan3793/cleanup.
Authored-by: Cheng Pan <379377944@qq.com>
Signed-off-by: Kent Yao <yao@apache.org>
### What changes were proposed in this pull request?
Extend Catalyst's type system by a new type that conforms to the SQL standard (see SQL:2016, section 4.6.2): TimestampWithoutTZType represents the timestamp without time zone type
### Why are the changes needed?
Spark SQL today supports the TIMESTAMP data type. However the semantics provided actually match TIMESTAMP WITH LOCAL TIMEZONE as defined by Oracle. Timestamps embedded in a SQL query or passed through JDBC are presumed to be in session local timezone and cast to UTC before being processed.
These are desirable semantics in many cases, such as when dealing with calendars.
In many (more) other cases, such as when dealing with log files it is desirable that the provided timestamps not be altered.
SQL users expect that they can model either behavior and do so by using TIMESTAMP WITHOUT TIME ZONE for time zone insensitive data and TIMESTAMP WITH LOCAL TIME ZONE for time zone sensitive data.
Most traditional RDBMS map TIMESTAMP to TIMESTAMP WITHOUT TIME ZONE and will be surprised to see TIMESTAMP WITH LOCAL TIME ZONE, a feature that does not exist in the standard.
In this new feature, we will introduce TIMESTAMP WITH LOCAL TIMEZONE to describe the existing timestamp type and add TIMESTAMP WITHOUT TIME ZONE for standard semantic.
Using these two types will provide clarity.
This is a starting PR. See more details in https://issues.apache.org/jira/browse/SPARK-35662
### Does this PR introduce _any_ user-facing change?
Yes, a new data type for Timestamp without time zone type. It is still in development.
### How was this patch tested?
Unit test
Closes#32802 from gengliangwang/TimestampNTZType.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
It's a long-standing bug that we forgot to resolve `UnresolvedAlias` in `CollectMetrics`. It's a bit hard to trigger this bug before 3.2 as most likely people won't create `UnresolvedAlias` when calling `Dataset.observe`. However things have been changed after https://github.com/apache/spark/pull/30974
This PR proposes to handle `CollectMetrics` in the rule `ResolveAliases`.
### Why are the changes needed?
bug fix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
updated test
Closes#32803 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Optimizes the retrieval of approximate quantiles for an array of percentiles.
* Adds an overload for QuantileSummaries.query that accepts an array of percentiles and optimizes the computation to do a single pass over the sketch and avoid redundant computation.
* Modifies the ApproximatePercentiles operator to call into the new method.
All formatting changes are the result of running ./dev/scalafmt
### Why are the changes needed?
The existing implementation does repeated calls per input percentile resulting in redundant computation.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added unit tests for the new method.
Closes#32700 from alkispoly-db/spark_35558_approx_quants_array.
Authored-by: Alkis Polyzotis <alkis.polyzotis@databricks.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Override `getJDBCType` method in `MySQLDialect` so that `FloatType` is mapped to `FLOAT` instead of `REAL`
### Why are the changes needed?
MySQL treats `REAL` as a synonym to `DOUBLE` by default (see https://dev.mysql.com/doc/refman/8.0/en/numeric-types.html). Therefore, when creating a table with a column of `REAL` type, it will be created as `DOUBLE`. However, currently, `MySQLDialect` does not provide an implementation for `getJDBCType`, and will thus ultimately fall back to `JdbcUtils.getCommonJDBCType`, which maps `FloatType` to `REAL`. This change is needed so that we can properly map the `FloatType` to `FLOAT` for MySQL.
### Does this PR introduce _any_ user-facing change?
Prior to this PR, when writing a dataframe with a `FloatType` column to a MySQL table, it will create a `DOUBLE` column. After the PR, it will create a `FLOAT` column.
### How was this patch tested?
Added a test case in `JDBCSuite` that verifies the mapping.
Closes#32605 from mariosmeim-db/SPARK-35446.
Authored-by: Marios Meimaris <marios.meimaris@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
A followup for 345d35ed1a, in this PR we support CURRENT_USER without tailing parentheses in default mode. And for ANSI mode, we can only use CURRENT_USER without tailing parentheses because it is a reserved keyword that cannot be used as a function name
### Why are the changes needed?
1. make it the same as current_date/current_timestamp
2. better ANSI compliance
### Does this PR introduce _any_ user-facing change?
no, just a followup
### How was this patch tested?
new tests
Closes#32770 from yaooqinn/SPARK-21957-F.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR is to fix the `UnsupportedOperationException` described in [PR#32705](https://github.com/apache/spark/pull/32705).
When AQE and DPP are turned on at the same time, because the `BroadcastExchange` included in the DPP filter is not added through `EnsureRequirement` rule, Therefore, when AQE optimizes the DPP filter, there is no way to add `BroadcastExchange` through the `EnsureRequirement` rule in `reOptimize` method, which eventually leads to the loss of `BroadcastExchange` in the final physical plan. This PR adds `BroadcastExchange` node in the `reOptimize` method if the current plan is DPP filter.
### Why are the changes needed?
bug fix
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
adding new ut
Closes#32741 from JkSelf/fixDPP+AQEbug.
Authored-by: Ke Jia <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add database if exists check in `SeesionCatalog`
### Why are the changes needed?
Curently execute `drop database test` will throw unfriendly error msg.
```
Error in query: org.apache.hadoop.hive.metastore.api.NoSuchObjectException: test
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.metastore.api.NoSuchObjectException: test
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:112)
at org.apache.spark.sql.hive.HiveExternalCatalog.dropDatabase(HiveExternalCatalog.scala:200)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogWithListener.dropDatabase(ExternalCatalogWithListener.scala:53)
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.dropDatabase(SessionCatalog.scala:273)
at org.apache.spark.sql.execution.command.DropDatabaseCommand.run(ddl.scala:111)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:75)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:73)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:84)
at org.apache.spark.sql.Dataset.$anonfun$logicalPlan$1(Dataset.scala:228)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3707)
```
### Does this PR introduce _any_ user-facing change?
Yes, more cleaner error msg.
### How was this patch tested?
Add test.
Closes#32768 from ulysses-you/SPARK-35629.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Sets `references` for `NamedLambdaVariable` and `LambdaFunction`.
| Expression | NamedLambdaVariable | LambdaFunction |
| --- | --- | --- |
| References before | None | All function references |
| References after | self.toAttribute | Function references minus arguments' references |
In `NestedColumnAliasing`, this means that `ExtractValue(ExtractValue(attr, lv: NamedLambdaVariable), ...)` now references both `attr` and `lv`, rather than just `attr`. As a result, it will not be included in the nested column references.
### Why are the changes needed?
Before, lambda key was referenced outside of lambda function.
#### Example 1
Before:
```
Project [transform(keys#0, lambdafunction(_extract_v1#0, lambda key#0, false)) AS a#0]
+- 'Join Cross
:- Project [kvs#0[lambda key#0].v1 AS _extract_v1#0]
: +- LocalRelation <empty>, [kvs#0]
+- LocalRelation <empty>, [keys#0]
```
After:
```
Project [transform(keys#418, lambdafunction(kvs#417[lambda key#420].v1, lambda key#420, false)) AS a#419]
+- Join Cross
:- LocalRelation <empty>, [kvs#417]
+- LocalRelation <empty>, [keys#418]
```
#### Example 2
Before:
```
Project [transform(keys#0, lambdafunction(kvs#0[lambda key#0].v1, lambda key#0, false)) AS a#0]
+- GlobalLimit 5
+- LocalLimit 5
+- Project [keys#0, _extract_v1#0 AS _extract_v1#0]
+- GlobalLimit 5
+- LocalLimit 5
+- Project [kvs#0[lambda key#0].v1 AS _extract_v1#0, keys#0]
+- LocalRelation <empty>, [kvs#0, keys#0]
```
After:
```
Project [transform(keys#428, lambdafunction(kvs#427[lambda key#430].v1, lambda key#430, false)) AS a#429]
+- GlobalLimit 5
+- LocalLimit 5
+- Project [keys#428, kvs#427]
+- GlobalLimit 5
+- LocalLimit 5
+- LocalRelation <empty>, [kvs#427, keys#428]
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Scala unit tests for the examples above
Closes#32773 from karenfeng/SPARK-35636.
Authored-by: Karen Feng <karen.feng@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pr add in/inset predicate support for `UnwrapCastInBinaryComparison`.
Current implement doesn't pushdown filters for `In/InSet` which contains `Cast`.
For instance:
```scala
spark.range(50).selectExpr("cast(id as int) as id").write.mode("overwrite").parquet("/tmp/parquet/t1")
spark.read.parquet("/tmp/parquet/t1").where("id in (1L, 2L, 4L)").explain
```
before this pr:
```
== Physical Plan ==
*(1) Filter cast(id#5 as bigint) IN (1,2,4)
+- *(1) ColumnarToRow
+- FileScan parquet [id#5] Batched: true, DataFilters: [cast(id#5 as bigint) IN (1,2,4)], Format: Parquet, Location: InMemoryFileIndex(1 paths)[file:/tmp/parquet/t1], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int>
```
after this pr:
```
== Physical Plan ==
*(1) Filter id#95 IN (1,2,4)
+- *(1) ColumnarToRow
+- FileScan parquet [id#95] Batched: true, DataFilters: [id#95 IN (1,2,4)], Format: Parquet, Location: InMemoryFileIndex(1 paths)[file:/tmp/parquet/t1], PartitionFilters: [], PushedFilters: [In(id, [1,2,4])], ReadSchema: struct<id:int>
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#32488 from cfmcgrady/SPARK-35316.
Authored-by: Fu Chen <cfmcgrady@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR removes `canPlanAsBroadcastHashJoin` check in `EliminateOuterJoin.
### Why are the changes needed?
We can always removes outer join if it only has DISTINCT on streamed side.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32744 from wangyum/SPARK-34808-2.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR group exception messages in `sql/hive/src/main/scala/org/apache/spark/sql/hive/execution`.
### 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#32694 from beliefer/SPARK-35059.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, we do not have a suitable definition of the `user` concept in Spark. We only have a `sparkUser` app widely but do not support identify or retrieve the user information from a session in STS or a runtime query execution.
`current_user()` is very popular and supported by plenty of other modern or old school databases, and also ANSI compliant.
This PR add `current_user()` as a SQL function. And, they are the same. In this PR, we add these functions w/o ambiguity.
1. For a normal single-threaded Spark application, clearly the `sparkUser` is always equivalent to `current_user()` .
2. For a multi-threaded Spark application, e.g. Spark thrift server, we use a `ThreadLocal` variable to store the client-side user(after authenticated) before running the query and retrieve it in the parser.
### Why are the changes needed?
`current_user()` is very popular and supported by plenty of other modern or old school databases, and also ANSI compliant.
### Does this PR introduce _any_ user-facing change?
yes, added `current_user()` as a SQL function
### How was this patch tested?
new tests in thrift server and sql/catalyst
Closes#32718 from yaooqinn/SPARK-21957.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add rule `ConvertToLocalRelation` into AQE Optimizer.
### Why are the changes needed?
Support propagate empty local relation through project and filter like such SQL case:
```
Aggregate
Project
Join
ShuffleStage
ShuffleStage
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#32724 from ulysses-you/SPARK-35585.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The condition check for FULL OUTER sort merge join (https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoinExec.scala#L1368 ) has unnecessary trip when `leftIndex == leftMatches.size` or `rightIndex == rightMatches.size`. Though this does not affect correctness (`scanNextInBuffered()` returns false anyway). But we can avoid it in the first place.
### Why are the changes needed?
Better readability for developers and avoid unnecessary execution.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests, such as `OuterJoinSuite.scala`.
Closes#32736 from c21/join-bug.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Gengliang Wang <ltnwgl@gmail.com>
### What changes were proposed in this pull request?
A test case of AdaptiveQueryExecSuite becomes flaky since there are too many debug logs in RootLogger:
https://github.com/Yikun/spark/runs/2715222392?check_suite_focus=truehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/139125/testReport/
To fix it, I suggest supporting multiple loggers in the testing method withLogAppender. So that the LogAppender gets clean target log outputs.
### Why are the changes needed?
Fix a flaky test case.
Also, reduce unnecessary memory cost in tests.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test
Closes#32725 from gengliangwang/fixFlakyLogAppender.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Move `Set` command related test cases from `SQLQuerySuite` to a new test suite `SetCommandSuite`. There are 7 test cases in total.
### Why are the changes needed?
Code refactoring. `SQLQuerySuite` is becoming big.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests
Closes#32732 from gengliangwang/setsuite.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to support special datetime values introduced by #25708 and by #25716 only in typed literals, and don't recognize them in parsing strings to dates/timestamps. The following string values are supported only in typed timestamp literals:
- `epoch [zoneId]` - `1970-01-01 00:00:00+00 (Unix system time zero)`
- `today [zoneId]` - midnight today.
- `yesterday [zoneId]` - midnight yesterday
- `tomorrow [zoneId]` - midnight tomorrow
- `now` - current query start time.
For example:
```sql
spark-sql> SELECT timestamp 'tomorrow';
2019-09-07 00:00:00
```
Similarly, the following special date values are supported only in typed date literals:
- `epoch [zoneId]` - `1970-01-01`
- `today [zoneId]` - the current date in the time zone specified by `spark.sql.session.timeZone`.
- `yesterday [zoneId]` - the current date -1
- `tomorrow [zoneId]` - the current date + 1
- `now` - the date of running the current query. It has the same notion as `today`.
For example:
```sql
spark-sql> SELECT date 'tomorrow' - date 'yesterday';
2
```
### Why are the changes needed?
In the current implementation, Spark supports the special date/timestamp value in any input strings casted to dates/timestamps that leads to the following problems:
- If executors have different system time, the result is inconsistent, and random. Column values depend on where the conversions were performed.
- The special values play the role of distributed non-deterministic functions though users might think of the values as constants.
### Does this PR introduce _any_ user-facing change?
Yes but the probability should be small.
### How was this patch tested?
By running existing test suites:
```
$ build/sbt "sql/testOnly org.apache.spark.sql.SQLQueryTestSuite -- -z interval.sql"
$ build/sbt "sql/testOnly org.apache.spark.sql.SQLQueryTestSuite -- -z date.sql"
$ build/sbt "sql/testOnly org.apache.spark.sql.SQLQueryTestSuite -- -z timestamp.sql"
$ build/sbt "test:testOnly *DateTimeUtilsSuite"
```
Closes#32714 from MaxGekk/remove-datetime-special-values.
Lead-authored-by: Max Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR adds a unit test to show a bug in the latest janino version which fails to compile valid Java code. Unfortunately, I can't share the exact query that can trigger this bug (includes some custom expressions), but this pattern is not very uncommon and I believe can be triggered by some real queries.
A follow-up is needed before the 3.2 release, to either fix this bug in janino, or revert the janino version upgrade, or work around it in Spark.
### Why are the changes needed?
make it easy for people to debug janino, as I'm not a janino expert.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#32716 from cloud-fan/janino.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently, the results of following SQL queries are not redacted:
```
SET [KEY];
SET;
```
For example:
```
scala> spark.sql("set javax.jdo.option.ConnectionPassword=123456").show()
+--------------------+------+
| key| value|
+--------------------+------+
|javax.jdo.option....|123456|
+--------------------+------+
scala> spark.sql("set javax.jdo.option.ConnectionPassword").show()
+--------------------+------+
| key| value|
+--------------------+------+
|javax.jdo.option....|123456|
+--------------------+------+
scala> spark.sql("set").show()
+--------------------+--------------------+
| key| value|
+--------------------+--------------------+
|javax.jdo.option....| 123456|
```
We should hide the sensitive information and redact the query output.
### Why are the changes needed?
Security.
### Does this PR introduce _any_ user-facing change?
Yes, the sensitive information in the output of Set commands are redacted
### How was this patch tested?
Unit test
Closes#32712 from gengliangwang/redactSet.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Handle Currying Product while serializing TreeNode to JSON. While processing [Product](https://github.com/apache/spark/blob/v3.1.2/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala#L820), we may get an assert error for cases like Currying Product because of the mismatch of sizes between field name and field values.
Fallback to use reflection to get all the values for constructor parameters when we meet such cases.
### Why are the changes needed?
Avoid throwing error while serializing TreeNode to JSON, try to output as much information as possible.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
New UT case added.
Closes#32713 from ivoson/SPARK-35411-followup.
Authored-by: Tengfei Huang <tengfei.h@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr add new rule to removes outer join if it only has distinct on streamed side. For example:
```scala
spark.range(200L).selectExpr("id AS a").createTempView("t1")
spark.range(300L).selectExpr("id AS b").createTempView("t2")
spark.sql("SELECT DISTINCT a FROM t1 LEFT JOIN t2 ON a = b").explain(true)
```
Before this pr:
```
== Optimized Logical Plan ==
Aggregate [a#2L], [a#2L]
+- Project [a#2L]
+- Join LeftOuter, (a#2L = b#6L)
:- Project [id#0L AS a#2L]
: +- Range (0, 200, step=1, splits=Some(2))
+- Project [id#4L AS b#6L]
+- Range (0, 300, step=1, splits=Some(2))
```
After this pr:
```
== Optimized Logical Plan ==
Aggregate [a#2L], [a#2L]
+- Project [id#0L AS a#2L]
+- Range (0, 200, step=1, splits=Some(2))
```
### Why are the changes needed?
Improve query performance. [DB2](https://www.ibm.com/docs/en/db2-for-zos/11?topic=manipulation-how-db2-simplifies-join-operations) support this feature:
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#31908 from wangyum/SPARK-34808.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <yumwang@ebay.com>
### What changes were proposed in this pull request?
This is a minor change to update how `StateStoreRestoreExec` computes its number of output rows. Previously we only count input rows, but the optionally restored rows are not counted in.
### Why are the changes needed?
Currently the number of output rows of `StateStoreRestoreExec` only counts the each input row. But it actually outputs input rows + optional restored rows. We should provide correct number of output rows.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#32703 from viirya/fix-outputrows.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR refactors `SubqueryExpression` class. It removes the children field from SubqueryExpression's constructor and adds `outerAttrs` and `joinCond`.
### Why are the changes needed?
Currently, the children field of a subquery expression is used to store both collected outer references in the subquery plan and join conditions after correlated predicates are pulled up.
For example:
`SELECT (SELECT max(c1) FROM t1 WHERE t1.c1 = t2.c1) FROM t2`
During the analysis phase, outer references in the subquery are stored in the children field: `scalar-subquery [t2.c1]`, but after the optimizer rule `PullupCorrelatedPredicates`, the children field will be used to store the join conditions, which contain both the inner and the outer references: `scalar-subquery [t1.c1 = t2.c1]`. This is why the references of SubqueryExpression excludes the inner plan's output:
29ed1a2de4/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/subquery.scala (L68-L69)
This can be confusing and error-prone. The references for a subquery expression should always be defined as outer attribute references.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#32687 from allisonwang-db/refactor-subquery-expr.
Authored-by: allisonwang-db <66282705+allisonwang-db@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
After SPARK-29291 and SPARK-33352, there are still some compilation warnings about `procedure syntax is deprecated` as follows:
```
[WARNING] [Warn] /spark/core/src/main/scala/org/apache/spark/MapOutputTracker.scala:723: [deprecation | origin= | version=2.13.0] procedure syntax is deprecated: instead, add `: Unit =` to explicitly declare `registerMergeResult`'s return type
[WARNING] [Warn] /spark/core/src/main/scala/org/apache/spark/MapOutputTracker.scala:748: [deprecation | origin= | version=2.13.0] procedure syntax is deprecated: instead, add `: Unit =` to explicitly declare `unregisterMergeResult`'s return type
[WARNING] [Warn] /spark/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala:223: [deprecation | origin= | version=2.13.0] procedure syntax is deprecated: instead, add `: Unit =` to explicitly declare `testSimpleSpillingForAllCodecs`'s return type
[WARNING] [Warn] /spark/mllib-local/src/test/scala/org/apache/spark/ml/linalg/BLASBenchmark.scala:53: [deprecation | origin= | version=2.13.0] procedure syntax is deprecated: instead, add `: Unit =` to explicitly declare `runBLASBenchmark`'s return type
[WARNING] [Warn] /spark/sql/core/src/main/scala/org/apache/spark/sql/execution/command/DataWritingCommand.scala:110: [deprecation | origin= | version=2.13.0] procedure syntax is deprecated: instead, add `: Unit =` to explicitly declare `assertEmptyRootPath`'s return type
[WARNING] [Warn] /spark/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala:602: [deprecation | origin= | version=2.13.0] procedure syntax is deprecated: instead, add `: Unit =` to explicitly declare `executeCTASWithNonEmptyLocation`'s return type
```
So the main change of this pr is cleanup these compilation warnings.
### Why are the changes needed?
Eliminate compilation warnings in Scala 2.13 and this change should be compatible with Scala 2.12
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#32669 from LuciferYang/re-clean-procedure-syntax.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Added the following TreePattern enums:
- EXCHANGE
- IN_SUBQUERY_EXEC
- UPDATE_FIELDS
Migrated `transformAllExpressions` call sites to use `transformAllExpressionsWithPruning`
### Why are the changes needed?
Reduce the number of tree traversals and hence improve the query compilation latency.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Perf diff:
Rule name | Total Time (baseline) | Total Time (experiment) | experiment/baseline
OptimizeUpdateFields | 54646396 | 27444424 | 0.5
ReplaceUpdateFieldsExpression | 24694303 | 2087517 | 0.08
Closes#32643 from sigmod/all_expressions.
Authored-by: Yingyi Bu <yingyi.bu@databricks.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
I just noticed that `AdaptiveQueryExecSuite.SPARK-34091: Batch shuffle fetch in AQE partition coalescing` takes more than 10 minutes to finish, which is unacceptable.
This PR sets the shuffle partitions to 10 in that test, so that the test can finish with 5 seconds.
### Why are the changes needed?
speed up the test
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#32695 from cloud-fan/test.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR fixes a build error with Scala 2.13 on GA.
#32301 seems to bring this error.
### Why are the changes needed?
To recover CI.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
GA
Closes#32696 from sarutak/followup-SPARK-35194.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
### What changes were proposed in this pull request?
Refactors `NestedColumnAliasing` and `GeneratorNestedColumnAliasing` for readability.
### Why are the changes needed?
Improves readability for future maintenance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#32301 from karenfeng/refactor-nested-column-aliasing.
Authored-by: Karen Feng <karen.feng@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add a new method `isMaterialized` in `QueryStageExec`.
### Why are the changes needed?
Currently, we use `resultOption().get.isDefined` to check if a query stage has materialized. The code is not readable at a glance. It's better to use a new method like `isMaterialized` to define it.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass CI.
Closes#32689 from ulysses-you/SPARK-35552.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Various small code simplification/cleanup for OptimizeSkewedJoin
### Why are the changes needed?
code refactor
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests
Closes#32685 from cloud-fan/skew-join.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Initial implementation of RocksDBCheckpointMetadata. It persists the metadata for RocksDBFileManager.
### Why are the changes needed?
The RocksDBCheckpointMetadata persists the metadata for each committed batch in JSON format. The object contains all RocksDB file names and the number of total keys.
The metadata binds closely with the directory structure of RocksDBFileManager, as described in the design doc - [Directory Structure and Format for Files stored in DFS](https://docs.google.com/document/d/10wVGaUorgPt4iVe4phunAcjU924fa3-_Kf29-2nxH6Y/edit#heading=h.zgvw85ijoz2).
### Does this PR introduce _any_ user-facing change?
No. Internal implementation only.
### How was this patch tested?
New UT added.
Closes#32272 from xuanyuanking/SPARK-35172.
Lead-authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Co-authored-by: Tathagata Das <tathagata.das1565@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Spark conv function is from MySQL and it's better to follow the MySQL behavior. MySQL returns the max unsigned long if the input string is too big, and Spark should follow it.
However, seems Spark has different behavior in two cases:
MySQL allows leading spaces but Spark does not.
If the input string is way too long, Spark fails with ArrayIndexOutOfBoundException
This patch now help conv follow behavior in those two cases
conv allows leading spaces
conv will return the max unsigned long when the input string is way too long
### Why are the changes needed?
fixing it to match the behavior of conv function to the (almost) only one reference of another DBMS, MySQL
### Does this PR introduce _any_ user-facing change?
Yes, as pointed out above
### How was this patch tested?
Add test
Closes#32684 from dgd-contributor/SPARK-33428.
Authored-by: dgd-contributor <dgd_contributor@viettel.com.vn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add a new data source V2 API: `LocalScan`. It is a special Scan that will happen on Driver locally instead of Executors.
### Why are the changes needed?
The new API improves the flexibility of the DSV2 API. It allows developers to implement connectors for data sources of small data sizes.
For example, we can build a data source for Spark History applications from Spark History Server RESTFUL API. The result set is small and fetching all the results from the Spark driver is good enough. Making it a data source allows us to operate SQL queries with filters or table joins.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test
Closes#32678 from gengliangwang/LocalScan.
Lead-authored-by: Gengliang Wang <ltnwgl@gmail.com>
Co-authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR group exception messages in `sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver`.
### 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#32646 from beliefer/SPARK-35057.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup from https://github.com/apache/spark/pull/32547#discussion_r639916474, where for LEFT ANTI join, we do not need to depend on `loaded` variable, as in `codegenAnti` we only load `streamedAfter` no more than once (i.e. assign column values from streamed row which are not used in join condition).
### Why are the changes needed?
Avoid unnecessary processing in code-gen (though it's just `boolean $loaded = false;`, and `if (!$loaded) { $loaded = true; }`).
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unite tests in `ExistenceJoinSuite`.
Closes#32681 from c21/join-followup.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The main code change is:
* Change rule `DemoteBroadcastHashJoin` to `DynamicJoinSelection` and add shuffle hash join selection code.
* Specify a join strategy hint `SHUFFLE_HASH` if AQE think a join can be converted to SHJ.
* Skip `preferSortMerge` config check in AQE side if a join can be converted to SHJ.
### Why are the changes needed?
Use AQE runtime statistics to decide if we can use shuffled hash join instead of sort merge join. Currently, the formula of shuffled hash join selection dose not work due to the dymanic shuffle partition number.
Add a new config spark.sql.adaptive.shuffledHashJoinLocalMapThreshold to decide if join can be converted to shuffled hash join safely.
### Does this PR introduce _any_ user-facing change?
Yes, add a new config.
### How was this patch tested?
Add test.
Closes#32550 from ulysses-you/SPARK-35282-2.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add the metrics to record how many tasks fallback to sort-based aggregation for hash aggregation. This will help developers and users to debug and optimize query. Object hash aggregation has similar metrics already.
### Why are the changes needed?
Help developers and users to debug and optimize query with hash aggregation.
### Does this PR introduce _any_ user-facing change?
Yes, the added metrics will show up in Spark web UI.
Example:
<img width="604" alt="Screen Shot 2021-05-26 at 12 17 08 AM" src="https://user-images.githubusercontent.com/4629931/119618437-bf3c5880-bdb7-11eb-89bb-5b88db78639f.png">
### How was this patch tested?
Changed unit test in `SQLMetricsSuite.scala`.
Closes#32671 from c21/agg-metrics.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes `HiveExternalCatalogVersionsSuite`.
With this change, only <major>.<minor> version is set to `spark.sql.hive.metastore.version`.
### Why are the changes needed?
I'm personally checking whether all the tests pass with Java 11 for the current `master` and I found `HiveExternalCatalogVersionsSuite` fails.
The reason is that Spark 3.0.2 and 3.1.1 doesn't accept `2.3.8` as a hive metastore version.
`HiveExternalCatalogVersionsSuite` downloads Spark releases from https://dist.apache.org/repos/dist/release/spark/ and run test for each release. The Spark releases are `3.0.2` and `3.1.1` for the current `master` for now.
e47e615c0e/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveExternalCatalogVersionsSuite.scala (L239-L259)
With Java 11, the suite run with a hive metastore version which corresponds to the builtin Hive version and it's `2.3.8` for the current `master`.
20750a3f9e/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveExternalCatalogVersionsSuite.scala (L62-L66)
But `branch-3.0` and `branch-3.1` doesn't accept `2.3.8`, the suite with Java 11 fails.
Another solution would be backporting SPARK-34271 (#31371) but after [a discussion](https://github.com/apache/spark/pull/32668#issuecomment-848435170), we prefer to fix the test,
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests with CI.
Closes#32670 from sarutak/fix-version-suite-for-java11.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to upgrade json4s from 3.7.0-M5 to 3.7.0-M11
Note: json4s version greater than 3.7.0-M11 is not binary compatible with Spark third party jars
### Why are the changes needed?
Multiple defect fixes and improvements like
https://github.com/json4s/json4s/issues/750https://github.com/json4s/json4s/issues/554https://github.com/json4s/json4s/issues/715
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Ran with the existing UTs
Closes#32636 from vinodkc/br_build_upgrade_json4s.
Authored-by: Vinod KC <vinod.kc.in@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Add the function type, such as "scala_udf", "python_udf", "java_udf", "hive", "built-in" to the `ExpressionInfo` for UDF.
### Why are the changes needed?
Make the `ExpressionInfo` of UDF more meaningful
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing and newly added UT
Closes#32587 from linhongliu-db/udf-language.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to use a proper built-in exceptions instead of the plain `Exception` in Python.
While I am here, I fixed another minor issue at `DataFrams.schema` together:
```diff
- except AttributeError as e:
- raise Exception(
- "Unable to parse datatype from schema. %s" % e)
+ except Exception as e:
+ raise ValueError(
+ "Unable to parse datatype from schema. %s" % e) from e
```
Now it catches all exceptions during schema parsing, chains the exception with `ValueError`. Previously it only caught `AttributeError` that does not catch all cases.
### Why are the changes needed?
For users to expect the proper exceptions.
### Does this PR introduce _any_ user-facing change?
Yeah, the exception classes became different but should be compatible because previous exception was plain `Exception` which other exceptions inherit.
### How was this patch tested?
Existing unittests should cover,
Closes#31238Closes#32650 from HyukjinKwon/SPARK-32194.
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 improves the interaction between partition coalescing and skew handling by moving the skew join rule ahead of the partition coalescing rule and making corresponding changes to the two rules:
1. Simplify `OptimizeSkewedJoin` as it doesn't need to handle `CustomShuffleReaderExec` anymore.
2. Update `CoalesceShufflePartitions` to support coalescing non-skewed partitions.
### Why are the changes needed?
It's a bit hard to reason about skew join if partitions have been coalesced. A skewed partition needs to be much larger than other partitions and we need to look at the raw sizes before coalescing.
It also makes `OptimizeSkewedJoin` more robust, as we don't need to worry about a skewed partition being coalesced with a small partition and breaks skew join handling.
It also helps with https://github.com/apache/spark/pull/31653 , which needs to move `OptimizeSkewedJoin` to an earlier phase and run before `CoalesceShufflePartitions`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
new UT and existing tests
Closes#32594 from cloud-fan/shuffle.
Lead-authored-by: Wenchen Fan <wenchen@databricks.com>
Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
* remove `EliminateUnnecessaryJoin`, using `AQEPropagateEmptyRelation` instead.
* eliminate join, aggregate, limit, repartition, sort, generate which is beneficial.
### Why are the changes needed?
Make `EliminateUnnecessaryJoin` available with more case.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#32602 from ulysses-you/SPARK-35455.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Addressed the dongjoon-hyun comments on the previous PR #30018.
Extended the `RemoveRedundantAggregates` rule to remove redundant aggregations in even more queries. For example in
```
dataset
.dropDuplicates()
.groupBy('a)
.agg(max('b))
```
the `dropDuplicates` is not needed, because the result on `max` does not depend on duplicate values.
### Why are the changes needed?
Improve performance.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
UT
Closes#31914 from tanelk/SPARK-33122_redundant_aggs_followup.
Lead-authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Co-authored-by: Tanel Kiis <tanel.kiis@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR fixes an issue that `RemoveRedundantProjects` removes `ProjectExec` which is for generating `UnsafeRow`.
In `DataSourceV2Strategy`, `ProjectExec` will be inserted to ensure internal rows are `UnsafeRow`.
```
private def withProjectAndFilter(
project: Seq[NamedExpression],
filters: Seq[Expression],
scan: LeafExecNode,
needsUnsafeConversion: Boolean): SparkPlan = {
val filterCondition = filters.reduceLeftOption(And)
val withFilter = filterCondition.map(FilterExec(_, scan)).getOrElse(scan)
if (withFilter.output != project || needsUnsafeConversion) {
ProjectExec(project, withFilter)
} else {
withFilter
}
}
...
case PhysicalOperation(project, filters, relation: DataSourceV2ScanRelation) =>
// projection and filters were already pushed down in the optimizer.
// this uses PhysicalOperation to get the projection and ensure that if the batch scan does
// not support columnar, a projection is added to convert the rows to UnsafeRow.
val batchExec = BatchScanExec(relation.output, relation.scan)
withProjectAndFilter(project, filters, batchExec, !batchExec.supportsColumnar) :: Nil
```
So, the hierarchy of the partial tree should be like `ProjectExec(FilterExec(BatchScan))`.
But `RemoveRedundantProjects` doesn't consider this type of hierarchy, leading `ClassCastException`.
A concreate example to reproduce this issue is reported:
```
import scala.collection.JavaConverters._
import org.apache.iceberg.{PartitionSpec, TableProperties}
import org.apache.iceberg.hadoop.HadoopTables
import org.apache.iceberg.spark.SparkSchemaUtil
import org.apache.spark.sql.{DataFrame, QueryTest, SparkSession}
import org.apache.spark.sql.internal.SQLConf
class RemoveRedundantProjectsTest extends QueryTest {
override val spark: SparkSession = SparkSession
.builder()
.master("local[4]")
.config("spark.driver.bindAddress", "127.0.0.1")
.appName(suiteName)
.getOrCreate()
test("RemoveRedundantProjects removes non-redundant projects") {
withSQLConf(
SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1",
SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> "false",
SQLConf.REMOVE_REDUNDANT_PROJECTS_ENABLED.key -> "true") {
withTempDir { dir =>
val path = dir.getCanonicalPath
val data = spark.range(3).toDF
val table = new HadoopTables().create(
SparkSchemaUtil.convert(data.schema),
PartitionSpec.unpartitioned(),
Map(TableProperties.WRITE_NEW_DATA_LOCATION -> path).asJava,
path)
data.write.format("iceberg").mode("overwrite").save(path)
table.refresh()
val df = spark.read.format("iceberg").load(path)
val dfX = df.as("x")
val dfY = df.as("y")
val join = dfX.filter(dfX("id") > 0).join(dfY, "id")
join.explain("extended")
assert(join.count() == 2)
}
}
}
}
```
```
[info] - RemoveRedundantProjects removes non-redundant projects *** FAILED ***
[info] 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 4) (xeroxms100.northamerica.corp.microsoft.com executor driver): java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.GenericInternalRow cannot be cast to org.apache.spark.sql.catalyst.expressions.UnsafeRow
[info] at org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:226)
[info] at org.apache.spark.sql.execution.SortExec.$anonfun$doExecute$1(SortExec.scala:119)
```
### Why are the changes needed?
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#32606 from sarutak/fix-project-removal-issue.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Use `SpecificInternalRow` instead of `GenericInternalRow` to avoid boxing / unboxing cost.
### Why are the changes needed?
Since it doesn't know the input row schema, `GenericInternalRow` potentially need to apply boxing for input arguments. It's better to use `SpecificInternalRow` instead since we know input data types.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#32647 from sunchao/specific-input-row.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR fixes a bug with subexpression elimination for CaseWhen statements. https://github.com/apache/spark/pull/30245 added support for creating subexpressions that are present in all branches of conditional statements. However, for a statement to be in "all branches" of a CaseWhen statement, it must also be in the elseValue.
### Why are the changes needed?
Fix a bug where a subexpression can be created and run for branches of a conditional that don't pass. This can cause issues especially with a UDF in a branch that gets executed assuming the condition is true.
### Does this PR introduce _any_ user-facing change?
Yes, fixes a potential bug where a UDF could be eagerly executed even though it might expect to have already passed some form of validation. For example:
```
val col = when($"id" < 0, myUdf($"id"))
spark.range(1).select(when(col > 0, col)).show()
```
`myUdf($"id")` is considered a subexpression and eagerly evaluated, because it is pulled out as a common expression from both executions of the when clause, but if `id >= 0` it should never actually be run.
### How was this patch tested?
Updated existing test with new case.
Closes#32595 from Kimahriman/bug-case-subexpr-elimination.
Authored-by: Adam Binford <adamq43@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This patch fixes a bug when dealing with common expressions in conditional expressions such as `CaseWhen` during subexpression elimination.
For example, previously we find common expressions among conditions of `CaseWhen`, but children expressions are also counted into. We should not count these children expressions as common expressions.
### Why are the changes needed?
If the redundant children expressions are counted as common expressions too, they will be redundantly evaluated and miss the subexpression elimination opportunity.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added tests.
Closes#32559 from viirya/SPARK-35410.
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 proposes to avoid wrapping if-else to the constant literals for `percentage` and `accuracy` in `percentile_approx`. They are expected to be literals (or foldable expressions).
Pivot works by two phrase aggregations, and it works with manipulating the input to `null` for non-matched values (pivot column and value).
Note that pivot supports an optimized version without such logic with changing input to `null` for some types (non-nested types basically). So the issue fixed by this PR is only for complex types.
```scala
val df = Seq(
("a", -1.0), ("a", 5.5), ("a", 2.5), ("b", 3.0), ("b", 5.2)).toDF("type", "value")
.groupBy().pivot("type", Seq("a", "b")).agg(
percentile_approx(col("value"), array(lit(0.5)), lit(10000)))
df.show()
```
**Before:**
```
org.apache.spark.sql.AnalysisException: cannot resolve 'percentile_approx((IF((type <=> CAST('a' AS STRING)), value, CAST(NULL AS DOUBLE))), (IF((type <=> CAST('a' AS STRING)), array(0.5D), NULL)), (IF((type <=> CAST('a' AS STRING)), 10000, CAST(NULL AS INT))))' due to data type mismatch: The accuracy or percentage provided must be a constant literal;
'Aggregate [percentile_approx(if ((type#7 <=> cast(a as string))) value#8 else cast(null as double), if ((type#7 <=> cast(a as string))) array(0.5) else cast(null as array<double>), if ((type#7 <=> cast(a as string))) 10000 else cast(null as int), 0, 0) AS a#16, percentile_approx(if ((type#7 <=> cast(b as string))) value#8 else cast(null as double), if ((type#7 <=> cast(b as string))) array(0.5) else cast(null as array<double>), if ((type#7 <=> cast(b as string))) 10000 else cast(null as int), 0, 0) AS b#18]
+- Project [_1#2 AS type#7, _2#3 AS value#8]
+- LocalRelation [_1#2, _2#3]
```
**After:**
```
+-----+-----+
| a| b|
+-----+-----+
|[2.5]|[3.0]|
+-----+-----+
```
### Why are the changes needed?
To make percentile_approx work with pivot as expected
### Does this PR introduce _any_ user-facing change?
Yes. It threw an exception but now it returns a correct result as shown above.
### How was this patch tested?
Manually tested and unit test was added.
Closes#32619 from HyukjinKwon/SPARK-35480.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/32411, to fix a mistake and use `sparkSession.sessionState.newHadoopConf` which includes SQL configs instead of `sparkSession.sparkContext.hadoopConfiguration` .
### Why are the changes needed?
fix mistake
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests
Closes#32618 from cloud-fan/follow1.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Kent Yao <yao@apache.org>
### 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>
