## What changes were proposed in this pull request?
By SPARK-17019, `On Heap Memory` and `Off Heap Memory` are introduced as optional metrics.
But they are not displayed because they are made `display: none` in css and there are no way to appear them.
I know #22595 also try to resolve this issue but that will use `additional-metrics.js`.
Initially, `additional-metrics.js` is created for `StagePage` but `StagePage` currently uses `stagepage.js` for its additional metrics to be toggle because `DataTable (one of jQuery plugins)` was introduced and we needed another mechanism to add/remove columns for additional metrics.
Now that `ExecutorsPage` also uses `DataTable` so it might be better to introduce same mechanism as `StagePage` for additional metrics.
And then, we can remove `additional-metrics.js` which is no longer used from anywhere.
## How was this patch tested?
After this change is applied, I confirmed `ExecutorsPage` and `StagePage` are properly rendered and all checkboxes for additional metrics work.
Closes#25374 from sarutak/remove-additional-metrics.js.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
While processing the Rowdata in the server side ColumnValue BigDecimal type value processed by server has to converted to the HiveDecmal data type for successful processing of query using Hive ODBC client.As per current logic corresponding to the Decimal column datatype, the Spark server uses BigDecimal, and the ODBC client uses HiveDecimal. If the data type does not match, the client fail to parse
Since this handing was missing the query executed in Hive ODBC client wont return or provides result to the user even though the decimal type column value data present.
## How was this patch tested?
Manual test report and impact assessment is done using existing test-cases
Before fix
After Fix
Closes#23899 from sujith71955/master_decimalissue.
Authored-by: s71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Currently, on requesting summary metrics, cached data are returned if the current number of "SUCCESS" tasks is the same as the value in cached data.
However, the number of "SUCCESS" tasks is wrong when there are running tasks. In `AppStatusStore`, the KVStore is `ElementTrackingStore`, instead of `InMemoryStore`. The value count is always the number of "SUCCESS" tasks + "RUNNING" tasks.
Thus, even when the running tasks are finished, the out-of-update cached data is returned.
This PR is to fix the code in getting the number of "SUCCESS" tasks.
## How was this patch tested?
Test manually, run
```
sc.parallelize(1 to 160, 40).map(i => Thread.sleep(i*100)).collect()
```
and keep refreshing the stage page , we can see the task summary metrics is wrong.
### Before fix:
### After fix:
Closes#25369 from gengliangwang/fixStagePage.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Restored comments in `date.sql` removed by 924d794a6f and 997d153e54 . The comments was introduced by 51379b731d .
## How was this patch tested?
By re-running `date.sql` via:
```shell
$ build/sbt "sql/test-only *SQLQueryTestSuite -- -z date.sql"
```
Closes#25422 from MaxGekk/sql-comments-followup.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
* Add log in `NewHadoopRDD`
* Remove some words in logs which related to specific user API.
## How was this patch tested?
Manual.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#25391 from WeichenXu123/log_sf.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR moves `udf_radians` from `HiveCompatibilitySuite` to `HiveQuerySuite` to make it easy to test with JDK 11 because it returns different value from JDK 9:
```java
public class TestRadians {
public static void main(String[] args) {
System.out.println(java.lang.Math.toRadians(57.2958));
}
}
```
```sh
[rootspark-3267648 ~]# javac TestRadians.java
[rootspark-3267648 ~]# /usr/lib/jdk-9.0.4+11/bin/java TestRadians
1.0000003575641672
[rootspark-3267648 ~]# /usr/lib/jdk-11.0.3/bin/java TestRadians
1.0000003575641672
[rootspark-3267648 ~]# /usr/lib/jdk8u222-b10/bin/java TestRadians
1.000000357564167
```
## How was this patch tested?
manual tests
Closes#25417 from wangyum/SPARK-28686.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Current two PySpark version tests in PythonTestsSuite, just test against Python version at driver side. Because the test script doesn't run any spark job requiring python worker, it doesn't actually do version check at worker side. This patch adds pieces of code to the test script, to run a simple job to verify Python version.
## How was this patch tested?
Unit test. Locally manual test.
Closes#25411 from viirya/SPARK-28652.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
SPARK-24817 and SPARK-24819 introduced new 3 non-internal properties for barrier-execution mode but they are not documented.
So I've added a section into configuration.md for barrier-mode execution.
## How was this patch tested?
Built using jekyll and confirm the layout by browser.
Closes#25370 from sarutak/barrier-exec-mode-conf-doc.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
It seems Datanucleus 3.x can not support JDK 11:
```java
[info] Cause: org.datanucleus.exceptions.NucleusException: The java type java.lang.Long (jdbc-type="", sql-type="") cant be mapped for this datastore. No mapping is available.
[info] at org.datanucleus.store.rdbms.mapping.RDBMSMappingManager.getDatastoreMappingClass(RDBMSMappingManager.java:1215)
[info] at org.datanucleus.store.rdbms.mapping.RDBMSMappingManager.createDatastoreMapping(RDBMSMappingManager.java:1378)
[info] at org.datanucleus.store.rdbms.table.AbstractClassTable.addDatastoreId(AbstractClassTable.java:392)
[info] at org.datanucleus.store.rdbms.table.ClassTable.initializePK(ClassTable.java:1087)
[info] at org.datanucleus.store.rdbms.table.ClassTable.preInitialize(ClassTable.java:247)
```
Hive upgrade Datanucleus to 4.x from Hive 2.0([HIVE-6113](https://issues.apache.org/jira/browse/HIVE-6113)). This PR makes it skip `0.12`, `0.13`, `0.14`, `1.0`, `1.1` and `1.2` when testing with JDK 11.
Note that, this pr will not fix sql read hive materialized view. It's another issue:
```
3.0: sql read hive materialized view *** FAILED *** (1 second, 521 milliseconds)
3.1: sql read hive materialized view *** FAILED *** (1 second, 536 milliseconds)
```
## How was this patch tested?
manual tests:
```shell
export JAVA_HOME="/usr/lib/jdk-11.0.3"
build/sbt "hive/test-only *.VersionsSuite *.HiveClientSuites" -Phive -Phadoop-3.2
```
Closes#25405 from wangyum/SPARK-28685.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR replaces `CatalogUtils.maskCredentials` with `SQLConf.get.redactOptions` to match other redacts.
## How was this patch tested?
unit test and manual tests:
Before this PR:
```sql
spark-sql> DESC EXTENDED test_spark_28675;
id int NULL
# Detailed Table Information
Database default
Table test_spark_28675
Owner root
Created Time Fri Aug 09 08:23:17 GMT-07:00 2019
Last Access Wed Dec 31 17:00:00 GMT-07:00 1969
Created By Spark 3.0.0-SNAPSHOT
Type MANAGED
Provider org.apache.spark.sql.jdbc
Location file:/user/hive/warehouse/test_spark_28675
Serde Library org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat org.apache.hadoop.mapred.SequenceFileInputFormat
OutputFormat org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
Storage Properties [url=###, driver=com.mysql.jdbc.Driver, dbtable=test_spark_28675]
spark-sql> SHOW TABLE EXTENDED LIKE 'test_spark_28675';
default test_spark_28675 false Database: default
Table: test_spark_28675
Owner: root
Created Time: Fri Aug 09 08:23:17 GMT-07:00 2019
Last Access: Wed Dec 31 17:00:00 GMT-07:00 1969
Created By: Spark 3.0.0-SNAPSHOT
Type: MANAGED
Provider: org.apache.spark.sql.jdbc
Location: file:/user/hive/warehouse/test_spark_28675
Serde Library: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat: org.apache.hadoop.mapred.SequenceFileInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
Storage Properties: [url=###, driver=com.mysql.jdbc.Driver, dbtable=test_spark_28675]
Schema: root
|-- id: integer (nullable = true)
```
After this PR:
```sql
spark-sql> DESC EXTENDED test_spark_28675;
id int NULL
# Detailed Table Information
Database default
Table test_spark_28675
Owner root
Created Time Fri Aug 09 08:19:49 GMT-07:00 2019
Last Access Wed Dec 31 17:00:00 GMT-07:00 1969
Created By Spark 3.0.0-SNAPSHOT
Type MANAGED
Provider org.apache.spark.sql.jdbc
Location file:/user/hive/warehouse/test_spark_28675
Serde Library org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat org.apache.hadoop.mapred.SequenceFileInputFormat
OutputFormat org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
Storage Properties [url=*********(redacted), driver=com.mysql.jdbc.Driver, dbtable=test_spark_28675]
spark-sql> SHOW TABLE EXTENDED LIKE 'test_spark_28675';
default test_spark_28675 false Database: default
Table: test_spark_28675
Owner: root
Created Time: Fri Aug 09 08:19:49 GMT-07:00 2019
Last Access: Wed Dec 31 17:00:00 GMT-07:00 1969
Created By: Spark 3.0.0-SNAPSHOT
Type: MANAGED
Provider: org.apache.spark.sql.jdbc
Location: file:/user/hive/warehouse/test_spark_28675
Serde Library: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat: org.apache.hadoop.mapred.SequenceFileInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
Storage Properties: [url=*********(redacted), driver=com.mysql.jdbc.Driver, dbtable=test_spark_28675]
Schema: root
|-- id: integer (nullable = true)
```
Closes#25395 from wangyum/SPARK-28675.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In StagePage, only the first optional column (Scheduler Delay, in this case) appears even though "Select All" checkbox is checked.
The cause is that wrong method is used to manipulate multiple columns. columns should have been used but column was used.
I've fixed this issue by replacing the `column` with `columns`.
## How was this patch tested?
Confirmed behavior of the check-box.
Closes#25397 from sarutak/fix-stagepage.js.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR fixed typos in comments and replace the explicit type with '<>' for Java 8+.
## How was this patch tested?
Manually tested.
Closes#25338 from younggyuchun/younggyu.
Authored-by: younggyu chun <younggyuchun@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In high workload environments, ContextCleaner seems to have excessive logging at INFO level which do not give much information. In one Particular case we see that ``INFO ContextCleaner: Cleaned accumulator`` message is 25-30% of the generated logs. We can log this information for cleanup in DEBUG level instead.
## How was this patch tested?
This do not modify any functionality. This is just changing cleanup log levels to DEBUG for ContextCleaner
Closes#25396 from ajithme/logss.
Authored-by: Ajith <ajith2489@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In the PR, I propose new expressions `Millennium`, `Century` and `Decade`, and support additional parameters of `extract()` for feature parity with PostgreSQL (https://www.postgresql.org/docs/11/functions-datetime.html#FUNCTIONS-DATETIME-EXTRACT):
1. `millennium` - the current millennium for given date (or a timestamp implicitly casted to a date). For example, years in the 1900s are in the second millennium. The third millennium started _January 1, 2001_.
2. `century` - the current millennium for given date (or timestamp). The first century starts at 0001-01-01 AD.
3. `decade` - the current decade for given date (or timestamp). Actually, this is the year field divided by 10.
Here are examples:
```sql
spark-sql> SELECT EXTRACT(MILLENNIUM FROM DATE '1981-01-19');
2
spark-sql> SELECT EXTRACT(CENTURY FROM DATE '1981-01-19');
20
spark-sql> SELECT EXTRACT(DECADE FROM DATE '1981-01-19');
198
```
## How was this patch tested?
Added new tests to `DateExpressionsSuite` and uncommented existing tests in `pgSQL/date.sql`.
Closes#25388 from MaxGekk/extract-ext2.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
some cleanup and tiny optimization
1, since the `transformImpl` method in the .mllib side is no longer used in the .ml side, the scope should be limited;
2, in the `hashUDF`, val `numOfFeatures` is never used;
3, in the udf, it is inefficient to involve param getter (`$(numFeatures)`/`$(binary)`) directly or via method `indexOf` ((`$(numFeatures)`) . instead, the getter should be called outside of the udf;
## How was this patch tested?
existing suites
Closes#25324 from zhengruifeng/hashingtf_cleanup.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In this PR, we implements a complete process of GPU-aware resources scheduling
in Standalone. The whole process looks like: Worker sets up isolated resources
when it starts up and registers to master along with its resources. And, Master
picks up usable workers according to driver/executor's resource requirements to
launch driver/executor on them. Then, Worker launches the driver/executor after
preparing resources file, which is created under driver/executor's working directory,
with specified resource addresses(told by master). When driver/executor finished,
their resources could be recycled to worker. Finally, if a worker stops, it
should always release its resources firstly.
For the case of Workers and Drivers in **client** mode run on the same host, we introduce
a config option named `spark.resources.coordinate.enable`(default true) to indicate
whether Spark should coordinate resources for user. If `spark.resources.coordinate.enable=false`, user should be responsible for configuring different resources for Workers and Drivers when use resourcesFile or discovery script. If true, Spark would help user to assign different resources for Workers and Drivers.
The solution for Spark to coordinate resources among Workers and Drivers is:
Generally, use a shared file named *____allocated_resources____.json* to sync allocated
resources info among Workers and Drivers on the same host.
After a Worker or Driver found all resources using the configured resourcesFile and/or
discovery script during launching, it should filter out available resources by excluding resources already allocated in *____allocated_resources____.json* and acquire resources from available resources according to its own requirement. After that, it should write its allocated resources along with its process id (pid) into *____allocated_resources____.json*. Pid (proposed by tgravescs) here used to check whether the allocated resources are still valid in case of Worker or Driver crashes and doesn't release resources properly. And when a Worker or Driver finished, normally, it would always clean up its own allocated resources in *____allocated_resources____.json*.
Note that we'll always get a file lock before any access to file *____allocated_resources____.json*
and release the lock finally.
Futhermore, we appended resources info in `WorkerSchedulerStateResponse` to work
around master change behaviour in HA mode.
## How was this patch tested?
Added unit tests in WorkerSuite, MasterSuite, SparkContextSuite.
Manually tested with client/cluster mode (e.g. multiple workers) in a single node Standalone.
Closes#25047 from Ngone51/SPARK-27371.
Authored-by: wuyi <ngone_5451@163.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
This PR is a follow-up to https://github.com/apache/spark/pull/24918
## How was this patch tested?
Pass the Jenkins with the newly update test files.
Closes#25393 from beliefer/enable-overlay-tests.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
I made an audit and update all dev scripts to support python3. (except `merge_spark_pr.py` which already updated)
## How was this patch tested?
Manual.
Closes#25289 from WeichenXu123/dev_py3.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Right now, batch DataFrame always changes the schema to nullable automatically (See this line: 325bc8e9c6/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala (L399)). But streaming file source is missing this.
This PR updates the streaming file source schema to force it be nullable. I also added a flag `spark.sql.streaming.fileSource.schema.forceNullable` to disable this change since some users may rely on the old behavior.
## How was this patch tested?
The new unit test.
Closes#25382 from zsxwing/SPARK-28651.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
There are "unsafe" conversions in the Kafka connector.
`CaseInsensitiveStringMap` comes in which is then converted the following way:
```
...
options.asScala.toMap
...
```
The main problem with this is that such case it looses its case insensitive nature
(case insensitive map is converting the key to lower case when get/contains called).
In this PR I'm using `CaseInsensitiveMap` to solve this problem.
## How was this patch tested?
Existing + additional unit tests.
Closes#24967 from gaborgsomogyi/SPARK-28163.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Adds support for V2 catalogs and the V2SessionCatalog for V2 tables for saveAsTable.
If the table can resolve through the V2SessionCatalog, we use SaveMode for datasource v1 for backwards compatibility to select the code path we're going to hit.
Depending on the SaveMode:
- SaveMode.Append:
a) If table exists: Use AppendData.byName
b) If table doesn't exist, use CTAS (ignoreIfExists = false)
- SaveMode.Overwrite: Use RTAS (orCreate = true)
- SaveMode.Ignore: Use CTAS (ignoreIfExists = true)
- SaveMode.ErrorIfExists: Use CTAS (ignoreIfExists = false)
## How was this patch tested?
Unit tests in DataSourceV2DataFrameSuite
Closes#25330 from brkyvz/saveAsTable.
Lead-authored-by: Burak Yavuz <brkyvz@gmail.com>
Co-authored-by: Burak Yavuz <burak@databricks.com>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
## What changes were proposed in this pull request?
I propose new levels of truncations for the `date_trunc()` and `trunc()` functions:
1. `MICROSECOND` and `MILLISECOND` truncate values of the `TIMESTAMP` type to microsecond and millisecond precision.
2. `DECADE`, `CENTURY` and `MILLENNIUM` truncate dates/timestamps to lowest date of current decade/century/millennium.
Also the `WEEK` and `QUARTER` levels have been supported by the `trunc()` function.
The function is implemented similarly to `date_trunc` in PostgreSQL: https://www.postgresql.org/docs/11/functions-datetime.html#FUNCTIONS-DATETIME-TRUNC to maintain feature parity with it.
Here are examples of `TRUNC`:
```sql
spark-sql> SELECT TRUNC('2015-10-27', 'DECADE');
2010-01-01
spark-sql> set spark.sql.datetime.java8API.enabled=true;
spark.sql.datetime.java8API.enabled true
spark-sql> SELECT TRUNC('1999-10-27', 'millennium');
1001-01-01
```
Examples of `DATE_TRUNC`:
```sql
spark-sql> SELECT DATE_TRUNC('CENTURY', '2015-03-05T09:32:05.123456');
2001-01-01T00:00:00Z
```
## How was this patch tested?
Added new tests to `DateTimeUtilsSuite`, `DateExpressionsSuite` and `DateFunctionsSuite`, and uncommented existing tests in `pgSQL/date.sql`.
Closes#25336 from MaxGekk/date_truct-ext.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Adds checks around:
- The existence of transforms in the table schema (even in nested fields)
- Duplications of transforms
- Case sensitivity checks around column names
in the V2 table creation code paths.
## How was this patch tested?
Unit tests.
Closes#25305 from brkyvz/v2CreateTable.
Authored-by: Burak Yavuz <brkyvz@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Plans after "Extract Python UDFs" are very flaky and error-prone to other rules.
For instance, if we add some rules, for instance, `PushDownPredicates` in `postHocOptimizationBatches`, the test in `BatchEvalPythonExecSuite` fails:
```scala
test("Python UDF refers to the attributes from more than one child") {
val df = Seq(("Hello", 4)).toDF("a", "b")
val df2 = Seq(("Hello", 4)).toDF("c", "d")
val joinDF = df.crossJoin(df2).where("dummyPythonUDF(a, c) == dummyPythonUDF(d, c)")
val qualifiedPlanNodes = joinDF.queryExecution.executedPlan.collect {
case b: BatchEvalPythonExec => b
}
assert(qualifiedPlanNodes.size == 1)
}
```
```
Invalid PythonUDF dummyUDF(a#63, c#74), requires attributes from more than one child.
```
This is because Python UDF extraction optimization is rolled back as below:
```
=== Applying Rule org.apache.spark.sql.catalyst.optimizer.PushDownPredicates ===
!Filter (dummyUDF(a#7, c#18) = dummyUDF(d#19, c#18)) Join Cross, (dummyUDF(a#7, c#18) = dummyUDF(d#19, c#18))
!+- Join Cross :- Project [_1#2 AS a#7, _2#3 AS b#8]
! :- Project [_1#2 AS a#7, _2#3 AS b#8] : +- LocalRelation [_1#2, _2#3]
! : +- LocalRelation [_1#2, _2#3] +- Project [_1#13 AS c#18, _2#14 AS d#19]
! +- Project [_1#13 AS c#18, _2#14 AS d#19] +- LocalRelation [_1#13, _2#14]
! +- LocalRelation [_1#13, _2#14]
```
Seems we should do Python UDFs cases at the last even after post hoc rules.
Note that this actually rather follows the way in previous versions when those were in physical plans (see SPARK-24721 and SPARK-12981). Those optimization rules were supposed to be placed at the end.
Note that I intentionally didn't move `ExperimentalMethods` (`spark.experimental.extraStrategies`). This is an explicit experimental API and I wanted to just-in-case workaround after this change for now.
## How was this patch tested?
Existing tests should cover.
Closes#25386 from HyukjinKwon/SPARK-28654.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR port [HIVE-10646](https://issues.apache.org/jira/browse/HIVE-10646) to fix Hive 0.12's JDBC client can not handle `NULL_TYPE`:
```sql
Connected to: Hive (version 3.0.0-SNAPSHOT)
Driver: Hive (version 0.12.0)
Transaction isolation: TRANSACTION_REPEATABLE_READ
Beeline version 0.12.0 by Apache Hive
0: jdbc:hive2://localhost:10000> select null;
org.apache.thrift.transport.TTransportException
at org.apache.thrift.transport.TIOStreamTransport.read(TIOStreamTransport.java:132)
at org.apache.thrift.transport.TTransport.readAll(TTransport.java:84)
at org.apache.thrift.transport.TSaslTransport.readLength(TSaslTransport.java:346)
at org.apache.thrift.transport.TSaslTransport.readFrame(TSaslTransport.java:423)
at org.apache.thrift.transport.TSaslTransport.read(TSaslTransport.java:405)
```
Server log:
```
19/08/07 09:34:07 ERROR TThreadPoolServer: Error occurred during processing of message.
java.lang.NullPointerException
at org.apache.hive.service.cli.thrift.TRow$TRowStandardScheme.write(TRow.java:388)
at org.apache.hive.service.cli.thrift.TRow$TRowStandardScheme.write(TRow.java:338)
at org.apache.hive.service.cli.thrift.TRow.write(TRow.java:288)
at org.apache.hive.service.cli.thrift.TRowSet$TRowSetStandardScheme.write(TRowSet.java:605)
at org.apache.hive.service.cli.thrift.TRowSet$TRowSetStandardScheme.write(TRowSet.java:525)
at org.apache.hive.service.cli.thrift.TRowSet.write(TRowSet.java:455)
at org.apache.hive.service.cli.thrift.TFetchResultsResp$TFetchResultsRespStandardScheme.write(TFetchResultsResp.java:550)
at org.apache.hive.service.cli.thrift.TFetchResultsResp$TFetchResultsRespStandardScheme.write(TFetchResultsResp.java:486)
at org.apache.hive.service.cli.thrift.TFetchResultsResp.write(TFetchResultsResp.java:412)
at org.apache.hive.service.cli.thrift.TCLIService$FetchResults_result$FetchResults_resultStandardScheme.write(TCLIService.java:13192)
at org.apache.hive.service.cli.thrift.TCLIService$FetchResults_result$FetchResults_resultStandardScheme.write(TCLIService.java:13156)
at org.apache.hive.service.cli.thrift.TCLIService$FetchResults_result.write(TCLIService.java:13107)
at org.apache.thrift.ProcessFunction.process(ProcessFunction.java:58)
at org.apache.thrift.TBaseProcessor.process(TBaseProcessor.java:39)
at org.apache.hive.service.auth.TSetIpAddressProcessor.process(TSetIpAddressProcessor.java:53)
at org.apache.thrift.server.TThreadPoolServer$WorkerProcess.run(TThreadPoolServer.java:310)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:819)
```
## How was this patch tested?
unit tests
Closes#25378 from wangyum/SPARK-28644.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR fix Hive 0.12 JDBC client can not handle binary type:
```sql
Connected to: Hive (version 3.0.0-SNAPSHOT)
Driver: Hive (version 0.12.0)
Transaction isolation: TRANSACTION_REPEATABLE_READ
Beeline version 0.12.0 by Apache Hive
0: jdbc:hive2://localhost:10000> SELECT cast('ABC' as binary);
Error: java.lang.ClassCastException: [B incompatible with java.lang.String (state=,code=0)
```
Server log:
```
19/08/07 10:10:04 WARN ThriftCLIService: Error fetching results:
java.lang.RuntimeException: java.lang.ClassCastException: [B incompatible with java.lang.String
at org.apache.hive.service.cli.session.HiveSessionProxy.invoke(HiveSessionProxy.java:83)
at org.apache.hive.service.cli.session.HiveSessionProxy.access$000(HiveSessionProxy.java:36)
at org.apache.hive.service.cli.session.HiveSessionProxy$1.run(HiveSessionProxy.java:63)
at java.security.AccessController.doPrivileged(AccessController.java:770)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1746)
at org.apache.hive.service.cli.session.HiveSessionProxy.invoke(HiveSessionProxy.java:59)
at com.sun.proxy.$Proxy26.fetchResults(Unknown Source)
at org.apache.hive.service.cli.CLIService.fetchResults(CLIService.java:455)
at org.apache.hive.service.cli.thrift.ThriftCLIService.FetchResults(ThriftCLIService.java:621)
at org.apache.hive.service.cli.thrift.TCLIService$Processor$FetchResults.getResult(TCLIService.java:1553)
at org.apache.hive.service.cli.thrift.TCLIService$Processor$FetchResults.getResult(TCLIService.java:1538)
at org.apache.thrift.ProcessFunction.process(ProcessFunction.java:38)
at org.apache.thrift.TBaseProcessor.process(TBaseProcessor.java:39)
at org.apache.hive.service.auth.TSetIpAddressProcessor.process(TSetIpAddressProcessor.java:53)
at org.apache.thrift.server.TThreadPoolServer$WorkerProcess.run(TThreadPoolServer.java:310)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:819)
Caused by: java.lang.ClassCastException: [B incompatible with java.lang.String
at org.apache.hive.service.cli.ColumnValue.toTColumnValue(ColumnValue.java:198)
at org.apache.hive.service.cli.RowBasedSet.addRow(RowBasedSet.java:60)
at org.apache.hive.service.cli.RowBasedSet.addRow(RowBasedSet.java:32)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.$anonfun$getNextRowSet$1(SparkExecuteStatementOperation.scala:151)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$Lambda$1923.000000009113BFE0.apply(Unknown Source)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.withSchedulerPool(SparkExecuteStatementOperation.scala:299)
at org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.getNextRowSet(SparkExecuteStatementOperation.scala:113)
at org.apache.hive.service.cli.operation.OperationManager.getOperationNextRowSet(OperationManager.java:220)
at org.apache.hive.service.cli.session.HiveSessionImpl.fetchResults(HiveSessionImpl.java:785)
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.hive.service.cli.session.HiveSessionProxy.invoke(HiveSessionProxy.java:78)
... 18 more
```
## How was this patch tested?
unit tests
Closes#25379 from wangyum/SPARK-28474.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Fix the typo in java doc.
## How was this patch tested?
N/A
Signed-off-by: Yishuang Lu <luystugmail.com>
Closes#25377 from lys0716/dev.
Authored-by: Yishuang Lu <luystu@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR makes `spark.sql.function.preferIntegralDivision` to internal configuration because it is only used for PostgreSQL test cases.
More details:
https://github.com/apache/spark/pull/25158#discussion_r309764541
## How was this patch tested?
N/A
Closes#25376 from wangyum/SPARK-28395-2.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
If you build Spark distributions you potentially end up with a `python/.eggs` directory in your working copy which is not currently ignored by Spark's `.gitignore` file. Since these are transient build artifacts there is no reason to ever commit these to Git so this should be placed in the `.gitignore` list
## How was this patch tested?
Verified the offending artifacts were no longer reported as untracked content by Git
Closes#25380 from rvesse/patch-1.
Authored-by: Rob Vesse <rvesse@dotnetrdf.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In `Catalogs.load`, the `pluginClassName` in the following code
```
String pluginClassName = conf.getConfString("spark.sql.catalog." + name, null);
```
is always null for built-in catalogs, e.g there is a SQLConf entry `spark.sql.catalog.session`.
This is because of https://github.com/apache/spark/pull/18852: SQLConf.conf.getConfString(key, null) always returns null.
## How was this patch tested?
Apply code changes of https://github.com/apache/spark/pull/24768 and tried loading session catalog.
Closes#25094 from gengliangwang/fixCatalogLoad.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
## What changes were proposed in this pull request?
The flag `spark.sql.decimalOperations.nullOnOverflow` is not honored by the `Cast` operator. This means that a casting which causes an overflow currently returns `null`.
The PR makes `Cast` respecting that flag, ie. when it is turned to false and a decimal overflow occurs, an exception id thrown.
## How was this patch tested?
Added UT
Closes#25253 from mgaido91/SPARK-28470.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
Subqueries do not have their own execution id, thus when calling `AdaptiveSparkPlanExec.onUpdatePlan`, it will actually get the `QueryExecution` instance of the main query, which is wasteful and problematic. It could cause issues like stack overflow or dead locks in some circumstances.
This PR fixes this issue by making `AdaptiveSparkPlanExec` compare the `QueryExecution` object retrieved by current execution ID against the `QueryExecution` object from which this plan is created, and only update the UI when the two instances are the same.
## How was this patch tested?
Manual tests on TPC-DS queries.
Closes#25316 from maryannxue/aqe-updateplan-fix.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: herman <herman@databricks.com>
## What changes were proposed in this pull request?
Sometimes when you explain a query, you will get stuck for a while. What's worse, you will get stuck again if you explain again.
This is caused by `FileSourceScanExec`:
1. In its `toString`, it needs to report the number of partitions it reads. This needs to query the hive metastore.
2. In its `outputOrdering`, it needs to get all the files. This needs to query the hive metastore.
This PR fixes by:
1. `toString` do not need to report the number of partitions it reads. We should report it via SQL metrics.
2. The `outputOrdering` is not very useful. We can only apply it if a) all the bucket columns are read. b) there is only one file in each bucket. This condition is really hard to meet, and even if we meet, sorting an already sorted file is pretty fast and avoiding the sort is not that useful. I think it's worth to give up this optimization so that explain don't need to get stuck.
## How was this patch tested?
existing tests
Closes#25328 from cloud-fan/ui.
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 is a follow-up to https://github.com/apache/spark/pull/25074
## How was this patch tested?
Pass the Jenkins with the newly update test files.
Closes#25366 from beliefer/uncomment-boolean-test.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Implements the `DESCRIBE TABLE` logical and physical plans for data source v2 tables.
## How was this patch tested?
Added unit tests to `DataSourceV2SQLSuite`.
Closes#25040 from mccheah/describe-table-v2.
Authored-by: mcheah <mcheah@palantir.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add a guide line for dataframe functions, say:
```
This function APIs usually have methods with Column signature only because it can support not only Column but also other types such as a native string. The other variants currently exist for historical reasons.
```
## How was this patch tested?
N/A
Closes#25355 from WeichenXu123/update_functions_guide2.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add's the higher order function `forall`, which tests an array to see if a predicate holds for every element.
The function is implemented in `org.apache.spark.sql.catalyst.expressions.ArrayForAll`.
The function is added to the function registry under the pretty name `forall`.
## How was this patch tested?
I've added appropriate unit tests for the new ArrayForAll expression in
`sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/HigherOrderFunctionsSuite.scala`.
Also added tests for the function in `sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala`.
Not sure who is best to ask about this PR so:
HyukjinKwon rxin gatorsmile ueshin srowen hvanhovell gatorsmile
Closes#24761 from nvander1/feature/for_all.
Lead-authored-by: Nik Vanderhoof <nikolasrvanderhoof@gmail.com>
Co-authored-by: Nik <nikolasrvanderhoof@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to use existing expressions `DayOfYear`, `WeekDay` and `DayOfWeek`, and support additional parameters of `extract()` for feature parity with PostgreSQL (https://www.postgresql.org/docs/11/functions-datetime.html#FUNCTIONS-DATETIME-EXTRACT):
1. `dow` - the day of the week as Sunday (0) to Saturday (6)
2. `isodow` - the day of the week as Monday (1) to Sunday (7)
3. `doy` - the day of the year (1 - 365/366)
Here are examples:
```sql
spark-sql> SELECT EXTRACT(DOW FROM TIMESTAMP '2001-02-16 20:38:40');
5
spark-sql> SELECT EXTRACT(ISODOW FROM TIMESTAMP '2001-02-18 20:38:40');
7
spark-sql> SELECT EXTRACT(DOY FROM TIMESTAMP '2001-02-16 20:38:40');
47
```
## How was this patch tested?
Updated `extract.sql`.
Closes#25367 from MaxGekk/extract-ext.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Remove the redundant and confusing transformImpl method in RF & GBT;
1, In `GBTClassifier` & `RandomForestClassifier`, the real `transform` methods inherit from `ProbabilisticClassificationModel` which can deal with multi output columns.
The `transformImpl` method, which deals with only one column - `predictionCol`, completely does nothing. This is quite confusing.
2, In `GBTRegressor` & `RandomForestRegressor`, the `transformImpl` do exactly what the superclass `PredictionModel` does (except model broadcasting), so can be removed.
## How was this patch tested?
existing suites
Closes#25256 from zhengruifeng/del_ensamble_transformImpl.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR targets to rename `PullOutPythonUDFInJoinCondition` to `ExtractPythonUDFFromJoinCondition` and move to 'Extract Python UDFs' together with other Python UDF related rules.
Currently `PullOutPythonUDFInJoinCondition` rule is alone outside of other 'Extract Python UDFs' rules together.
and the name `ExtractPythonUDFFromJoinCondition` is matched to existing Python UDF extraction rules.
## How was this patch tested?
Existing tests should cover.
Closes#25358 from HyukjinKwon/move-python-join-rule.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR adds UDF cases into group by clause in 'pgSQL/select_implicit.sql'
<details><summary>Diff comparing to 'pgSQL/select_implicit.sql'</summary>
<p>
```diff
diff --git a/home/root1/src/spark/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-select_implicit.sql.out b/home/root1/src/spark/sql/core/src/test/resources/sql-tests/results/pgSQL/select_implicit.sql.out
index 17303b2..0675820 100755
--- a/home/root1/src/spark/sql/core/src/test/resources/sql-tests/results/udf/pgSQL/udf-select_implicit.sql.out
+++ b/home/root1/src/spark/sql/core/src/test/resources/sql-tests/results/pgSQL/select_implicit.sql.out
-91,11 +91,9 struct<>
-- !query 11
-SELECT udf(c), udf(count(*)) FROM test_missing_target GROUP BY
-udf(test_missing_target.c)
-ORDER BY udf(c)
+SELECT c, count(*) FROM test_missing_target GROUP BY test_missing_target.c ORDER BY c
-- !query 11 schema
-struct<CAST(udf(cast(c as string)) AS STRING):string,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<c:string,count(1):bigint>
-- !query 11 output
ABAB 2
BBBB 2
-106,10 +104,9 cccc 2
-- !query 12
-SELECT udf(count(*)) FROM test_missing_target GROUP BY udf(test_missing_target.c)
-ORDER BY udf(c)
+SELECT count(*) FROM test_missing_target GROUP BY test_missing_target.c ORDER BY c
-- !query 12 schema
-struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<count(1):bigint>
-- !query 12 output
2
2
-120,18 +117,18 struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 13
-SELECT udf(count(*)) FROM test_missing_target GROUP BY udf(a) ORDER BY udf(b)
+SELECT count(*) FROM test_missing_target GROUP BY a ORDER BY b
-- !query 13 schema
struct<>
-- !query 13 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`b`' given input columns: [CAST(udf(cast(count(1) as string)) AS BIGINT)]; line 1 pos 75
+cannot resolve '`b`' given input columns: [count(1)]; line 1 pos 61
-- !query 14
-SELECT udf(count(*)) FROM test_missing_target GROUP BY udf(b) ORDER BY udf(b)
+SELECT count(*) FROM test_missing_target GROUP BY b ORDER BY b
-- !query 14 schema
-struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<count(1):bigint>
-- !query 14 output
1
2
-140,10 +137,10 struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 15
-SELECT udf(test_missing_target.b), udf(count(*))
- FROM test_missing_target GROUP BY udf(b) ORDER BY udf(b)
+SELECT test_missing_target.b, count(*)
+ FROM test_missing_target GROUP BY b ORDER BY b
-- !query 15 schema
-struct<CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<b:int,count(1):bigint>
-- !query 15 output
1 1
2 2
-152,9 +149,9 struct<CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(count(1) as string)
-- !query 16
-SELECT udf(c) FROM test_missing_target ORDER BY udf(a)
+SELECT c FROM test_missing_target ORDER BY a
-- !query 16 schema
-struct<CAST(udf(cast(c as string)) AS STRING):string>
+struct<c:string>
-- !query 16 output
XXXX
ABAB
-169,10 +166,9 CCCC
-- !query 17
-SELECT udf(count(*)) FROM test_missing_target GROUP BY udf(b) ORDER BY udf(b)
-desc
+SELECT count(*) FROM test_missing_target GROUP BY b ORDER BY b desc
-- !query 17 schema
-struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<count(1):bigint>
-- !query 17 output
4
3
-181,17 +177,17 struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 18
-SELECT udf(count(*)) FROM test_missing_target ORDER BY udf(1) desc
+SELECT count(*) FROM test_missing_target ORDER BY 1 desc
-- !query 18 schema
-struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<count(1):bigint>
-- !query 18 output
10
-- !query 19
-SELECT udf(c), udf(count(*)) FROM test_missing_target GROUP BY 1 ORDER BY 1
+SELECT c, count(*) FROM test_missing_target GROUP BY 1 ORDER BY 1
-- !query 19 schema
-struct<CAST(udf(cast(c as string)) AS STRING):string,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<c:string,count(1):bigint>
-- !query 19 output
ABAB 2
BBBB 2
-202,30 +198,30 cccc 2
-- !query 20
-SELECT udf(c), udf(count(*)) FROM test_missing_target GROUP BY 3
+SELECT c, count(*) FROM test_missing_target GROUP BY 3
-- !query 20 schema
struct<>
-- !query 20 output
org.apache.spark.sql.AnalysisException
-GROUP BY position 3 is not in select list (valid range is [1, 2]); line 1 pos 63
+GROUP BY position 3 is not in select list (valid range is [1, 2]); line 1 pos 53
-- !query 21
-SELECT udf(count(*)) FROM test_missing_target x, test_missing_target y
- WHERE udf(x.a) = udf(y.a)
- GROUP BY udf(b) ORDER BY udf(b)
+SELECT count(*) FROM test_missing_target x, test_missing_target y
+ WHERE x.a = y.a
+ GROUP BY b ORDER BY b
-- !query 21 schema
struct<>
-- !query 21 output
org.apache.spark.sql.AnalysisException
-Reference 'b' is ambiguous, could be: x.b, y.b.; line 3 pos 14
+Reference 'b' is ambiguous, could be: x.b, y.b.; line 3 pos 10
-- !query 22
-SELECT udf(a), udf(a) FROM test_missing_target
- ORDER BY udf(a)
+SELECT a, a FROM test_missing_target
+ ORDER BY a
-- !query 22 schema
-struct<CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(a as string)) AS INT):int>
+struct<a:int,a:int>
-- !query 22 output
0 0
1 1
-240,10 +236,10 struct<CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(a as string)) AS IN
-- !query 23
-SELECT udf(udf(a)/2), udf(udf(a)/2) FROM test_missing_target
- ORDER BY udf(udf(a)/2)
+SELECT a/2, a/2 FROM test_missing_target
+ ORDER BY a/2
-- !query 23 schema
-struct<CAST(udf(cast((cast(udf(cast(a as string)) as int) div 2) as string)) AS INT):int,CAST(udf(cast((cast(udf(cast(a as string)) as int) div 2) as string)) AS INT):int>
+struct<(a div 2):int,(a div 2):int>
-- !query 23 output
0 0
0 0
-258,10 +254,10 struct<CAST(udf(cast((cast(udf(cast(a as string)) as int) div 2) as string)) AS
-- !query 24
-SELECT udf(a/2), udf(a/2) FROM test_missing_target
- GROUP BY udf(a/2) ORDER BY udf(a/2)
+SELECT a/2, a/2 FROM test_missing_target
+ GROUP BY a/2 ORDER BY a/2
-- !query 24 schema
-struct<CAST(udf(cast((a div 2) as string)) AS INT):int,CAST(udf(cast((a div 2) as string)) AS INT):int>
+struct<(a div 2):int,(a div 2):int>
-- !query 24 output
0 0
1 1
-271,11 +267,11 struct<CAST(udf(cast((a div 2) as string)) AS INT):int,CAST(udf(cast((a div 2) a
-- !query 25
-SELECT udf(x.b), udf(count(*)) FROM test_missing_target x, test_missing_target y
- WHERE udf(x.a) = udf(y.a)
- GROUP BY udf(x.b) ORDER BY udf(x.b)
+SELECT x.b, count(*) FROM test_missing_target x, test_missing_target y
+ WHERE x.a = y.a
+ GROUP BY x.b ORDER BY x.b
-- !query 25 schema
-struct<CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<b:int,count(1):bigint>
-- !query 25 output
1 1
2 2
-284,11 +280,11 struct<CAST(udf(cast(b as string)) AS INT):int,CAST(udf(cast(count(1) as string)
-- !query 26
-SELECT udf(count(*)) FROM test_missing_target x, test_missing_target y
- WHERE udf(x.a) = udf(y.a)
- GROUP BY udf(x.b) ORDER BY udf(x.b)
+SELECT count(*) FROM test_missing_target x, test_missing_target y
+ WHERE x.a = y.a
+ GROUP BY x.b ORDER BY x.b
-- !query 26 schema
-struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
+struct<count(1):bigint>
-- !query 26 output
1
2
-297,22 +293,22 struct<CAST(udf(cast(count(1) as string)) AS BIGINT):bigint>
-- !query 27
-SELECT udf(a%2), udf(count(udf(b))) FROM test_missing_target
-GROUP BY udf(test_missing_target.a%2)
-ORDER BY udf(test_missing_target.a%2)
+SELECT a%2, count(b) FROM test_missing_target
+GROUP BY test_missing_target.a%2
+ORDER BY test_missing_target.a%2
-- !query 27 schema
-struct<CAST(udf(cast((a % 2) as string)) AS INT):int,CAST(udf(cast(count(cast(udf(cast(b as string)) as int)) as string)) AS BIGINT):bigint>
+struct<(a % 2):int,count(b):bigint>
-- !query 27 output
0 5
1 5
-- !query 28
-SELECT udf(count(c)) FROM test_missing_target
-GROUP BY udf(lower(test_missing_target.c))
-ORDER BY udf(lower(test_missing_target.c))
+SELECT count(c) FROM test_missing_target
+GROUP BY lower(test_missing_target.c)
+ORDER BY lower(test_missing_target.c)
-- !query 28 schema
-struct<CAST(udf(cast(count(c) as string)) AS BIGINT):bigint>
+struct<count(c):bigint>
-- !query 28 output
2
3
-321,18 +317,18 struct<CAST(udf(cast(count(c) as string)) AS BIGINT):bigint>
-- !query 29
-SELECT udf(count(udf(a))) FROM test_missing_target GROUP BY udf(a) ORDER BY udf(b)
+SELECT count(a) FROM test_missing_target GROUP BY a ORDER BY b
-- !query 29 schema
struct<>
-- !query 29 output
org.apache.spark.sql.AnalysisException
-cannot resolve '`b`' given input columns: [CAST(udf(cast(count(cast(udf(cast(a as string)) as int)) as string)) AS BIGINT)]; line 1 pos 80
+cannot resolve '`b`' given input columns: [count(a)]; line 1 pos 61
-- !query 30
-SELECT udf(count(b)) FROM test_missing_target GROUP BY udf(b/2) ORDER BY udf(b/2)
+SELECT count(b) FROM test_missing_target GROUP BY b/2 ORDER BY b/2
-- !query 30 schema
-struct<CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
+struct<count(b):bigint>
-- !query 30 output
1
5
-340,10 +336,10 struct<CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
-- !query 31
-SELECT udf(lower(test_missing_target.c)), udf(count(udf(c)))
- FROM test_missing_target GROUP BY udf(lower(c)) ORDER BY udf(lower(c))
+SELECT lower(test_missing_target.c), count(c)
+ FROM test_missing_target GROUP BY lower(c) ORDER BY lower(c)
-- !query 31 schema
-struct<CAST(udf(cast(lower(c) as string)) AS STRING):string,CAST(udf(cast(count(cast(udf(cast(c as string)) as string)) as string)) AS BIGINT):bigint>
+struct<lower(c):string,count(c):bigint>
-- !query 31 output
abab 2
bbbb 3
-352,9 +348,9 xxxx 1
-- !query 32
-SELECT udf(a) FROM test_missing_target ORDER BY udf(upper(udf(d)))
+SELECT a FROM test_missing_target ORDER BY upper(d)
-- !query 32 schema
-struct<CAST(udf(cast(a as string)) AS INT):int>
+struct<a:int>
-- !query 32 output
0
1
-369,33 +365,32 struct<CAST(udf(cast(a as string)) AS INT):int>
-- !query 33
-SELECT udf(count(b)) FROM test_missing_target
- GROUP BY udf((b + 1) / 2) ORDER BY udf((b + 1) / 2) desc
+SELECT count(b) FROM test_missing_target
+ GROUP BY (b + 1) / 2 ORDER BY (b + 1) / 2 desc
-- !query 33 schema
-struct<CAST(udf(cast(count(b) as string)) AS BIGINT):bigint>
+struct<count(b):bigint>
-- !query 33 output
7
3
-- !query 34
-SELECT udf(count(udf(x.a))) FROM test_missing_target x, test_missing_target y
- WHERE udf(x.a) = udf(y.a)
- GROUP BY udf(b/2) ORDER BY udf(b/2)
+SELECT count(x.a) FROM test_missing_target x, test_missing_target y
+ WHERE x.a = y.a
+ GROUP BY b/2 ORDER BY b/2
-- !query 34 schema
struct<>
-- !query 34 output
org.apache.spark.sql.AnalysisException
-Reference 'b' is ambiguous, could be: x.b, y.b.; line 3 pos 14
+Reference 'b' is ambiguous, could be: x.b, y.b.; line 3 pos 10
-- !query 35
-SELECT udf(x.b/2), udf(count(udf(x.b))) FROM test_missing_target x,
-test_missing_target y
- WHERE udf(x.a) = udf(y.a)
- GROUP BY udf(x.b/2) ORDER BY udf(x.b/2)
+SELECT x.b/2, count(x.b) FROM test_missing_target x, test_missing_target y
+ WHERE x.a = y.a
+ GROUP BY x.b/2 ORDER BY x.b/2
-- !query 35 schema
-struct<CAST(udf(cast((b div 2) as string)) AS INT):int,CAST(udf(cast(count(cast(udf(cast(b as string)) as int)) as string)) AS BIGINT):bigint>
+struct<(b div 2):int,count(b):bigint>
-- !query 35 output
0 1
1 5
-403,14 +398,14 struct<CAST(udf(cast((b div 2) as string)) AS INT):int,CAST(udf(cast(count(cast(
-- !query 36
-SELECT udf(count(udf(b))) FROM test_missing_target x, test_missing_target y
- WHERE udf(x.a) = udf(y.a)
- GROUP BY udf(x.b/2)
+SELECT count(b) FROM test_missing_target x, test_missing_target y
+ WHERE x.a = y.a
+ GROUP BY x.b/2
-- !query 36 schema
struct<>
-- !query 36 output
org.apache.spark.sql.AnalysisException
-Reference 'b' is ambiguous, could be: x.b, y.b.; line 1 pos 21
+Reference 'b' is ambiguous, could be: x.b, y.b.; line 1 pos 13
-- !query 37
```
</p>
</details>
## How was this patch tested?
Tested as Guided in SPARK-27921
Closes#25350 from Udbhav30/master.
Authored-by: Udbhav30 <u.agrawal30@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR add supportColumnar in DebugExec. Seems there was a conflict between https://github.com/apache/spark/pull/25274 and https://github.com/apache/spark/pull/25264
Currently tests are broken in Jenkins:
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/108687/https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/108688/https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/108693/
```
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: makeCopy, tree: ColumnarToRow +- InMemoryTableScan [id#356956L] +- InMemoryRelation [id#356956L], StorageLevel(disk, memory, deserialized, 1 replicas) +- *(1) Range (0, 5, step=1, splits=2)
Stacktrace
sbt.ForkMain$ForkError: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: makeCopy, tree:
ColumnarToRow
+- InMemoryTableScan [id#356956L]
+- InMemoryRelation [id#356956L], StorageLevel(disk, memory, deserialized, 1 replicas)
+- *(1) Range (0, 5, step=1, splits=2)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
at org.apache.spark.sql.catalyst.trees.TreeNode.makeCopy(TreeNode.scala:431)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:404)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:323)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:287)
```
## How was this patch tested?
Manually tested the failed test.
Closes#25365 from HyukjinKwon/SPARK-28537.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR is a followup of a fix as described in here: #25215 (comment)
<details><summary>Diff comparing to 'group-analytics.sql'</summary>
<p>
```diff
diff --git a/sql/core/src/test/resources/sql-tests/results/udf/udf-group-analytics.sql.out b/sql/core/src/test/resources/sql-tests/results/udf/udf-group-analytics.sql.out
index 3439a05727..de297ab166 100644
--- a/sql/core/src/test/resources/sql-tests/results/udf/udf-group-analytics.sql.out
+++ b/sql/core/src/test/resources/sql-tests/results/udf/udf-group-analytics.sql.out
-13,9 +13,9 struct<>
-- !query 1
-SELECT a + b, b, SUM(a - b) FROM testData GROUP BY a + b, b WITH CUBE
+SELECT udf(a + b), b, udf(SUM(a - b)) FROM testData GROUP BY udf(a + b), b WITH CUBE
-- !query 1 schema
-struct<(a + b):int,b:int,sum((a - b)):bigint>
+struct<CAST(udf(cast((a + b) as string)) AS INT):int,b:int,CAST(udf(cast(sum(cast((a - b) as bigint)) as string)) AS BIGINT):bigint>
-- !query 1 output
2 1 0
2 NULL 0
-33,9 +33,9 NULL NULL 3
-- !query 2
-SELECT a, b, SUM(b) FROM testData GROUP BY a, b WITH CUBE
+SELECT udf(a), udf(b), SUM(b) FROM testData GROUP BY udf(a), b WITH CUBE
-- !query 2 schema
-struct<a:int,b:int,sum(b):bigint>
+struct<CAST(udf(cast(a as string)) AS INT):int,CAST(udf(cast(b as string)) AS INT):int,sum(b):bigint>
-- !query 2 output
1 1 1
1 2 2
-52,9 +52,9 NULL NULL 9
-- !query 3
-SELECT a + b, b, SUM(a - b) FROM testData GROUP BY a + b, b WITH ROLLUP
+SELECT udf(a + b), b, SUM(a - b) FROM testData GROUP BY a + b, b WITH ROLLUP
-- !query 3 schema
-struct<(a + b):int,b:int,sum((a - b)):bigint>
+struct<CAST(udf(cast((a + b) as string)) AS INT):int,b:int,sum((a - b)):bigint>
-- !query 3 output
2 1 0
2 NULL 0
-70,9 +70,9 NULL NULL 3
-- !query 4
-SELECT a, b, SUM(b) FROM testData GROUP BY a, b WITH ROLLUP
+SELECT udf(a), b, udf(SUM(b)) FROM testData GROUP BY udf(a), b WITH ROLLUP
-- !query 4 schema
-struct<a:int,b:int,sum(b):bigint>
+struct<CAST(udf(cast(a as string)) AS INT):int,b:int,CAST(udf(cast(sum(cast(b as bigint)) as string)) AS BIGINT):bigint>
-- !query 4 output
1 1 1
1 2 2
-97,7 +97,7 struct<>
-- !query 6
-SELECT course, year, SUM(earnings) FROM courseSales GROUP BY ROLLUP(course, year) ORDER BY course, year
+SELECT course, year, SUM(earnings) FROM courseSales GROUP BY ROLLUP(course, year) ORDER BY udf(course), year
-- !query 6 schema
struct<course:string,year:int,sum(earnings):bigint>
-- !query 6 output
-111,7 +111,7 dotNET 2013 48000
-- !query 7
-SELECT course, year, SUM(earnings) FROM courseSales GROUP BY CUBE(course, year) ORDER BY course, year
+SELECT course, year, SUM(earnings) FROM courseSales GROUP BY CUBE(course, year) ORDER BY course, udf(year)
-- !query 7 schema
struct<course:string,year:int,sum(earnings):bigint>
-- !query 7 output
-127,9 +127,9 dotNET 2013 48000
-- !query 8
-SELECT course, year, SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(course, year)
+SELECT course, udf(year), SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(course, year)
-- !query 8 schema
-struct<course:string,year:int,sum(earnings):bigint>
+struct<course:string,CAST(udf(cast(year as string)) AS INT):int,sum(earnings):bigint>
-- !query 8 output
Java NULL 50000
NULL 2012 35000
-138,26 +138,26 dotNET NULL 63000
-- !query 9
-SELECT course, year, SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(course)
+SELECT course, year, udf(SUM(earnings)) FROM courseSales GROUP BY course, year GROUPING SETS(course)
-- !query 9 schema
-struct<course:string,year:int,sum(earnings):bigint>
+struct<course:string,year:int,CAST(udf(cast(sum(cast(earnings as bigint)) as string)) AS BIGINT):bigint>
-- !query 9 output
Java NULL 50000
dotNET NULL 63000
-- !query 10
-SELECT course, year, SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(year)
+SELECT udf(course), year, SUM(earnings) FROM courseSales GROUP BY course, year GROUPING SETS(year)
-- !query 10 schema
-struct<course:string,year:int,sum(earnings):bigint>
+struct<CAST(udf(cast(course as string)) AS STRING):string,year:int,sum(earnings):bigint>
-- !query 10 output
NULL 2012 35000
NULL 2013 78000
-- !query 11
-SELECT course, SUM(earnings) AS sum FROM courseSales
-GROUP BY course, earnings GROUPING SETS((), (course), (course, earnings)) ORDER BY course, sum
+SELECT course, udf(SUM(earnings)) AS sum FROM courseSales
+GROUP BY course, earnings GROUPING SETS((), (course), (course, earnings)) ORDER BY course, udf(sum)
-- !query 11 schema
struct<course:string,sum:bigint>
-- !query 11 output
-173,7 +173,7 dotNET 63000
-- !query 12
SELECT course, SUM(earnings) AS sum, GROUPING_ID(course, earnings) FROM courseSales
-GROUP BY course, earnings GROUPING SETS((), (course), (course, earnings)) ORDER BY course, sum
+GROUP BY course, earnings GROUPING SETS((), (course), (course, earnings)) ORDER BY udf(course), sum
-- !query 12 schema
struct<course:string,sum:bigint,grouping_id(course, earnings):int>
-- !query 12 output
-188,10 +188,10 dotNET 63000 1
-- !query 13
-SELECT course, year, GROUPING(course), GROUPING(year), GROUPING_ID(course, year) FROM courseSales
+SELECT udf(course), udf(year), GROUPING(course), GROUPING(year), GROUPING_ID(course, year) FROM courseSales
GROUP BY CUBE(course, year)
-- !query 13 schema
-struct<course:string,year:int,grouping(course):tinyint,grouping(year):tinyint,grouping_id(course, year):int>
+struct<CAST(udf(cast(course as string)) AS STRING):string,CAST(udf(cast(year as string)) AS INT):int,grouping(course):tinyint,grouping(year):tinyint,grouping_id(course, year):int>
-- !query 13 output
Java 2012 0 0 0
Java 2013 0 0 0
-205,7 +205,7 dotNET NULL 0 1 1
-- !query 14
-SELECT course, year, GROUPING(course) FROM courseSales GROUP BY course, year
+SELECT course, udf(year), GROUPING(course) FROM courseSales GROUP BY course, udf(year)
-- !query 14 schema
struct<>
-- !query 14 output
-214,7 +214,7 grouping() can only be used with GroupingSets/Cube/Rollup;
-- !query 15
-SELECT course, year, GROUPING_ID(course, year) FROM courseSales GROUP BY course, year
+SELECT course, udf(year), GROUPING_ID(course, year) FROM courseSales GROUP BY udf(course), year
-- !query 15 schema
struct<>
-- !query 15 output
-223,7 +223,7 grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 16
-SELECT course, year, grouping__id FROM courseSales GROUP BY CUBE(course, year) ORDER BY grouping__id, course, year
+SELECT course, year, grouping__id FROM courseSales GROUP BY CUBE(course, year) ORDER BY grouping__id, course, udf(year)
-- !query 16 schema
struct<course:string,year:int,grouping__id:int>
-- !query 16 output
-240,7 +240,7 NULL NULL 3
-- !query 17
SELECT course, year FROM courseSales GROUP BY CUBE(course, year)
-HAVING GROUPING(year) = 1 AND GROUPING_ID(course, year) > 0 ORDER BY course, year
+HAVING GROUPING(year) = 1 AND GROUPING_ID(course, year) > 0 ORDER BY course, udf(year)
-- !query 17 schema
struct<course:string,year:int>
-- !query 17 output
-250,7 +250,7 dotNET NULL
-- !query 18
-SELECT course, year FROM courseSales GROUP BY course, year HAVING GROUPING(course) > 0
+SELECT course, udf(year) FROM courseSales GROUP BY udf(course), year HAVING GROUPING(course) > 0
-- !query 18 schema
struct<>
-- !query 18 output
-259,7 +259,7 grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 19
-SELECT course, year FROM courseSales GROUP BY course, year HAVING GROUPING_ID(course) > 0
+SELECT course, udf(udf(year)) FROM courseSales GROUP BY course, year HAVING GROUPING_ID(course) > 0
-- !query 19 schema
struct<>
-- !query 19 output
-268,9 +268,9 grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 20
-SELECT course, year FROM courseSales GROUP BY CUBE(course, year) HAVING grouping__id > 0
+SELECT udf(course), year FROM courseSales GROUP BY CUBE(course, year) HAVING grouping__id > 0
-- !query 20 schema
-struct<course:string,year:int>
+struct<CAST(udf(cast(course as string)) AS STRING):string,year:int>
-- !query 20 output
Java NULL
NULL 2012
-281,7 +281,7 dotNET NULL
-- !query 21
SELECT course, year, GROUPING(course), GROUPING(year) FROM courseSales GROUP BY CUBE(course, year)
-ORDER BY GROUPING(course), GROUPING(year), course, year
+ORDER BY GROUPING(course), GROUPING(year), course, udf(year)
-- !query 21 schema
struct<course:string,year:int,grouping(course):tinyint,grouping(year):tinyint>
-- !query 21 output
-298,7 +298,7 NULL NULL 1 1
-- !query 22
SELECT course, year, GROUPING_ID(course, year) FROM courseSales GROUP BY CUBE(course, year)
-ORDER BY GROUPING(course), GROUPING(year), course, year
+ORDER BY GROUPING(course), GROUPING(year), course, udf(year)
-- !query 22 schema
struct<course:string,year:int,grouping_id(course, year):int>
-- !query 22 output
-314,7 +314,7 NULL NULL 3
-- !query 23
-SELECT course, year FROM courseSales GROUP BY course, year ORDER BY GROUPING(course)
+SELECT course, udf(year) FROM courseSales GROUP BY course, udf(year) ORDER BY GROUPING(course)
-- !query 23 schema
struct<>
-- !query 23 output
-323,7 +323,7 grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 24
-SELECT course, year FROM courseSales GROUP BY course, year ORDER BY GROUPING_ID(course)
+SELECT course, udf(year) FROM courseSales GROUP BY course, udf(year) ORDER BY GROUPING_ID(course)
-- !query 24 schema
struct<>
-- !query 24 output
-332,7 +332,7 grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup;
-- !query 25
-SELECT course, year FROM courseSales GROUP BY CUBE(course, year) ORDER BY grouping__id, course, year
+SELECT course, year FROM courseSales GROUP BY CUBE(course, year) ORDER BY grouping__id, udf(course), year
-- !query 25 schema
struct<course:string,year:int>
-- !query 25 output
-348,7 +348,7 NULL NULL
-- !query 26
-SELECT a + b AS k1, b AS k2, SUM(a - b) FROM testData GROUP BY CUBE(k1, k2)
+SELECT udf(a + b) AS k1, udf(b) AS k2, SUM(a - b) FROM testData GROUP BY CUBE(k1, k2)
-- !query 26 schema
struct<k1:int,k2:int,sum((a - b)):bigint>
-- !query 26 output
-368,7 +368,7 NULL NULL 3
-- !query 27
-SELECT a + b AS k, b, SUM(a - b) FROM testData GROUP BY ROLLUP(k, b)
+SELECT udf(udf(a + b)) AS k, b, SUM(a - b) FROM testData GROUP BY ROLLUP(k, b)
-- !query 27 schema
struct<k:int,b:int,sum((a - b)):bigint>
-- !query 27 output
-386,9 +386,9 NULL NULL 3
-- !query 28
-SELECT a + b, b AS k, SUM(a - b) FROM testData GROUP BY a + b, k GROUPING SETS(k)
+SELECT udf(a + b), udf(udf(b)) AS k, SUM(a - b) FROM testData GROUP BY a + b, k GROUPING SETS(k)
-- !query 28 schema
-struct<(a + b):int,k:int,sum((a - b)):bigint>
+struct<CAST(udf(cast((a + b) as string)) AS INT):int,k:int,sum((a - b)):bigint>
-- !query 28 output
NULL 1 3
NULL 2 0
```
</p>
</details>
## How was this patch tested?
Tested as instructed in SPARK-27921.
Closes#25362 from skonto/group-analytics-followup.
Authored-by: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
