### What changes were proposed in this pull request?
Rename config "spark.sql.ansi.enabled" to "spark.sql.dialect.spark.ansi.enabled"
### Why are the changes needed?
The relation between "spark.sql.ansi.enabled" and "spark.sql.dialect" is confusing, since the "PostgreSQL" dialect should contain the features of "spark.sql.ansi.enabled".
To make things clearer, we can rename the "spark.sql.ansi.enabled" to "spark.sql.dialect.spark.ansi.enabled", thus the option "spark.sql.dialect.spark.ansi.enabled" is only for Spark dialect.
For the casting and arithmetic operations, runtime exceptions should be thrown if "spark.sql.dialect" is "spark" and "spark.sql.dialect.spark.ansi.enabled" is true or "spark.sql.dialect" is PostgresSQL.
### Does this PR introduce any user-facing change?
Yes, the config name changed.
### How was this patch tested?
Existing UT.
Closes#26444 from xuanyuanking/SPARK-29807.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to add `io.netty.tryReflectionSetAccessible=true` to the testing configuration for JDK11 because this is an officially documented requirement of Apache Arrow.
Apache Arrow community documented this requirement at `0.15.0` ([ARROW-6206](https://github.com/apache/arrow/pull/5078)).
> #### For java 9 or later, should set "-Dio.netty.tryReflectionSetAccessible=true".
> This fixes `java.lang.UnsupportedOperationException: sun.misc.Unsafe or java.nio.DirectByteBuffer.(long, int) not available`. thrown by netty.
### Why are the changes needed?
After ARROW-3191, Arrow Java library requires the property `io.netty.tryReflectionSetAccessible` to be set to true for JDK >= 9. After https://github.com/apache/spark/pull/26133, JDK11 Jenkins job seem to fail.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-3.2-jdk-11/676/
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-3.2-jdk-11/677/
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-3.2-jdk-11/678/
```scala
Previous exception in task:
sun.misc.Unsafe or java.nio.DirectByteBuffer.<init>(long, int) not available

io.netty.util.internal.PlatformDependent.directBuffer(PlatformDependent.java:473)

io.netty.buffer.NettyArrowBuf.getDirectBuffer(NettyArrowBuf.java:243)

io.netty.buffer.NettyArrowBuf.nioBuffer(NettyArrowBuf.java:233)

io.netty.buffer.ArrowBuf.nioBuffer(ArrowBuf.java:245)

org.apache.arrow.vector.ipc.message.ArrowRecordBatch.computeBodyLength(ArrowRecordBatch.java:222)

```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass the Jenkins with JDK11.
Closes#26552 from dongjoon-hyun/SPARK-ARROW-JDK11.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add ```__repr__``` in Python ML Models
### Why are the changes needed?
In Python ML Models, some of them have ```__repr__```, others don't. In the doctest, when calling Model.setXXX, some of the Models print out the xxxModel... correctly, some of them can't because of lacking the ```__repr__``` method. For example:
```
>>> gm = GaussianMixture(k=3, tol=0.0001, seed=10)
>>> model = gm.fit(df)
>>> model.setPredictionCol("newPrediction")
GaussianMixture...
```
After the change, the above code will become the following:
```
>>> gm = GaussianMixture(k=3, tol=0.0001, seed=10)
>>> model = gm.fit(df)
>>> model.setPredictionCol("newPrediction")
GaussianMixtureModel...
```
### Does this PR introduce any user-facing change?
Yes.
### How was this patch tested?
doctest
Closes#26489 from huaxingao/spark-29876.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This pr is to remove floating-point `Sum/Average/CentralMomentAgg` from order-insensitive aggregates in `EliminateSorts`.
This pr comes from the gatorsmile suggestion: https://github.com/apache/spark/pull/26011#discussion_r344583899
### Why are the changes needed?
Bug fix.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added tests in `SubquerySuite`.
Closes#26534 from maropu/SPARK-29343-FOLLOWUP.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add DescribeNamespaceStatement, DescribeNamespace and DescribeNamespaceExec
to make "DESC DATABASE" look up catalog like v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same catalog/namespace resolution behavior, to avoid confusing end-users.
### Does this PR introduce any user-facing change?
Yes, add "DESC NAMESPACE" whose function is same as "DESC DATABASE" and "DESC SCHEMA".
### How was this patch tested?
New unit test
Closes#26513 from fuwhu/SPARK-29834.
Authored-by: fuwhu <bestwwg@163.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR proposes to show Python, pandas and PyArrow versions in integrated UDF tests as a clue so when the test cases fail, it show the related version information.
I think we don't really need this kind of version information in the test case name for now since I intend that integrated SQL test cases do not target to test different combinations of Python, Pandas and PyArrow.
### Why are the changes needed?
To make debug easier.
### Does this PR introduce any user-facing change?
It will change test name to include related Python, pandas and PyArrow versions.
### How was this patch tested?
Manually tested:
```
[info] - udf/postgreSQL/udf-case.sql - Scala UDF *** FAILED *** (8 seconds, 229 milliseconds)
[info] udf/postgreSQL/udf-case.sql - Scala UDF
...
[info] - udf/postgreSQL/udf-case.sql - Regular Python UDF *** FAILED *** (6 seconds, 298 milliseconds)
[info] udf/postgreSQL/udf-case.sql - Regular Python UDF
[info] Python: 3.7
...
[info] - udf/postgreSQL/udf-case.sql - Scalar Pandas UDF *** FAILED *** (6 seconds, 376 milliseconds)
[info] udf/postgreSQL/udf-case.sql - Scalar Pandas UDF
[info] Python: 3.7 Pandas: 0.25.3 PyArrow: 0.14.0
```
Closes#26538 from HyukjinKwon/investigate-flaky-test.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
When set `spark.yarn.jars=/xxx/xxx` which is just a no schema path, spark will throw a NullPointerException.
The reason is hdfs will return null if pathFs.globStatus(path) is not exist, and spark just use `pathFs.globStatus(path).filter(_.isFile())` without check it.
### Why are the changes needed?
Avoid NullPointerException.
### Does this PR introduce any user-facing change?
Yes. User will get a FileNotFoundException instead NullPointerException when `spark.yarn.jars` does not have schema and not exists.
### How was this patch tested?
Add UT.
Closes#26462 from ulysses-you/check-yarn-jars-path-exist.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
Add ShowTableStatement and make SHOW TABLE EXTENDED go through the same catalog/table resolution framework of v2 commands.
We don’t have this methods in the catalog to implement an V2 command
- catalog.getPartition
- catalog.getTempViewOrPermanentTableMetadata
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing
```sql
USE my_catalog
DESC t // success and describe the table t from my_catalog
SHOW TABLE EXTENDED FROM LIKE 't' // report table not found as there is no table t in the session catalog
```
### Does this PR introduce any user-facing change?
Yes. When running SHOW TABLE EXTENDED Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
Unit tests.
Closes#26540 from planga82/feature/SPARK-29481_ShowTableExtended.
Authored-by: Pablo Langa <soypab@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add listener event queue capacity configuration to documentation
### Why are the changes needed?
We some time see many event drops happening in eventLog listener queue. So, instead of increasing all the queues size, using this config we just need to increase eventLog queue capacity.
```
scala> sc.parallelize(1 to 100000, 100000).count()
[Stage 0:=================================================>(98299 + 4) / 100000]19/11/14 20:56:35 ERROR AsyncEventQueue: Dropping event from queue eventLog. This likely means one of the listeners is too slow and cannot keep up with the rate at which tasks are being started by the scheduler.
19/11/14 20:56:35 WARN AsyncEventQueue: Dropped 1 events from eventLog since the application started.
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests
Closes#26529 from shahidki31/master1.
Authored-by: shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This is a follow-up pr to fix the code coming from #23400; it replaces `update` with `setByte` for ByteType in `JdbcUtils.makeGetter`.
### Why are the changes needed?
For better code.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#26532 from maropu/SPARK-26499-FOLLOWUP.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
In order to avoid frequently changing the value of `spark.sql.adaptive.shuffle.maxNumPostShufflePartitions`, we usually set `spark.sql.adaptive.shuffle.maxNumPostShufflePartitions` much larger than `spark.sql.shuffle.partitions` after enabling adaptive execution, which causes some bucket map join lose efficacy and add more `ShuffleExchange`.
How to reproduce:
```scala
val bucketedTableName = "bucketed_table"
spark.range(10000).write.bucketBy(500, "id").sortBy("id").mode(org.apache.spark.sql.SaveMode.Overwrite).saveAsTable(bucketedTableName)
val bucketedTable = spark.table(bucketedTableName)
val df = spark.range(8)
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", -1)
// Spark 2.4. spark.sql.adaptive.enabled=false
// We set spark.sql.shuffle.partitions <= 500 every time based on our data in this case.
spark.conf.set("spark.sql.shuffle.partitions", 500)
bucketedTable.join(df, "id").explain()
// Since 3.0. We enabled adaptive execution and set spark.sql.adaptive.shuffle.maxNumPostShufflePartitions to a larger values to fit more cases.
spark.conf.set("spark.sql.adaptive.enabled", true)
spark.conf.set("spark.sql.adaptive.shuffle.maxNumPostShufflePartitions", 1000)
bucketedTable.join(df, "id").explain()
```
```
scala> bucketedTable.join(df, "id").explain()
== Physical Plan ==
*(4) Project [id#5L]
+- *(4) SortMergeJoin [id#5L], [id#7L], Inner
:- *(1) Sort [id#5L ASC NULLS FIRST], false, 0
: +- *(1) Project [id#5L]
: +- *(1) Filter isnotnull(id#5L)
: +- *(1) ColumnarToRow
: +- FileScan parquet default.bucketed_table[id#5L] Batched: true, DataFilters: [isnotnull(id#5L)], Format: Parquet, Location: InMemoryFileIndex[file:/root/opensource/apache-spark/spark-3.0.0-SNAPSHOT-bin-3.2.0/spark-warehou..., PartitionFilters: [], PushedFilters: [IsNotNull(id)], ReadSchema: struct<id:bigint>, SelectedBucketsCount: 500 out of 500
+- *(3) Sort [id#7L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(id#7L, 500), true, [id=#49]
+- *(2) Range (0, 8, step=1, splits=16)
```
vs
```
scala> bucketedTable.join(df, "id").explain()
== Physical Plan ==
AdaptiveSparkPlan(isFinalPlan=false)
+- Project [id#5L]
+- SortMergeJoin [id#5L], [id#7L], Inner
:- Sort [id#5L ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(id#5L, 1000), true, [id=#93]
: +- Project [id#5L]
: +- Filter isnotnull(id#5L)
: +- FileScan parquet default.bucketed_table[id#5L] Batched: true, DataFilters: [isnotnull(id#5L)], Format: Parquet, Location: InMemoryFileIndex[file:/root/opensource/apache-spark/spark-3.0.0-SNAPSHOT-bin-3.2.0/spark-warehou..., PartitionFilters: [], PushedFilters: [IsNotNull(id)], ReadSchema: struct<id:bigint>, SelectedBucketsCount: 500 out of 500
+- Sort [id#7L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(id#7L, 1000), true, [id=#92]
+- Range (0, 8, step=1, splits=16)
```
This PR makes read bucketed tables always obeys `spark.sql.shuffle.partitions` even enabling adaptive execution and set `spark.sql.adaptive.shuffle.maxNumPostShufflePartitions` to avoid add more `ShuffleExchange`.
### Why are the changes needed?
Do not degrade performance after enabling adaptive execution.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#26409 from wangyum/SPARK-29655.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Current string to interval cast logic does not support i.e. cast('.111 second' as interval) which will fail in SIGN state and return null, actually, it is 00:00:00.111.
```scala
-- !query 63
select interval '.111 seconds'
-- !query 63 schema
struct<0.111 seconds:interval>
-- !query 63 output
0.111 seconds
-- !query 64
select cast('.111 seconds' as interval)
-- !query 64 schema
struct<CAST(.111 seconds AS INTERVAL):interval>
-- !query 64 output
NULL
````
### Why are the changes needed?
bug fix.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
add ut
Closes#26514 from yaooqinn/SPARK-29888.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to switch `pygments.rb`, which only support Python 2 and seems inactive for the last few years (https://github.com/tmm1/pygments.rb), to Rouge which is pure Ruby code highlighter that is compatible with Pygments.
I thought it would be pretty difficult to change but thankfully Rouge does a great job as the alternative.
### Why are the changes needed?
We're moving to Python 3 and drop Python 2 completely.
### Does this PR introduce any user-facing change?
Maybe a little bit of different syntax style but should not have a notable change.
### How was this patch tested?
Manually tested the build and checked the documentation.
Closes#26521 from HyukjinKwon/SPARK-28752.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Upgrade Apache Arrow to version 0.15.1. This includes Java artifacts and increases the minimum required version of PyArrow also.
Version 0.12.0 to 0.15.1 includes the following selected fixes/improvements relevant to Spark users:
* ARROW-6898 - [Java] Fix potential memory leak in ArrowWriter and several test classes
* ARROW-6874 - [Python] Memory leak in Table.to_pandas() when conversion to object dtype
* ARROW-5579 - [Java] shade flatbuffer dependency
* ARROW-5843 - [Java] Improve the readability and performance of BitVectorHelper#getNullCount
* ARROW-5881 - [Java] Provide functionalities to efficiently determine if a validity buffer has completely 1 bits/0 bits
* ARROW-5893 - [C++] Remove arrow::Column class from C++ library
* ARROW-5970 - [Java] Provide pointer to Arrow buffer
* ARROW-6070 - [Java] Avoid creating new schema before IPC sending
* ARROW-6279 - [Python] Add Table.slice method or allow slices in \_\_getitem\_\_
* ARROW-6313 - [Format] Tracking for ensuring flatbuffer serialized values are aligned in stream/files.
* ARROW-6557 - [Python] Always return pandas.Series from Array/ChunkedArray.to_pandas, propagate field names to Series from RecordBatch, Table
* ARROW-2015 - [Java] Use Java Time and Date APIs instead of JodaTime
* ARROW-1261 - [Java] Add container type for Map logical type
* ARROW-1207 - [C++] Implement Map logical type
Changelog can be seen at https://arrow.apache.org/release/0.15.0.html
### Why are the changes needed?
Upgrade to get bug fixes, improvements, and maintain compatibility with future versions of PyArrow.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests, manually tested with Python 3.7, 3.8
Closes#26133 from BryanCutler/arrow-upgrade-015-SPARK-29376.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
move interval tests to `interval.sql`, and import it to `ansi/interval.sql`
### Why are the changes needed?
improve test coverage
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
N/A
Closes#26515 from cloud-fan/test.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Fix broken links
### How was this patch tested?
Tested using jykyll build --serve
Closes#26528 from huaxingao/spark-29901.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/23977 I made a mistake related to this line: 3725b1324f (diff-71c2cad03f08cb5f6c70462aa4e28d3aL112)
Previously,
1. the reader iterator for R worker read some initial data eagerly during RDD materialization. So it read the data before actual execution. For some reasons, in this case, it showed standard error from R worker.
2. After that, when error happens during actual execution, stderr wasn't shown: 3725b1324f (diff-71c2cad03f08cb5f6c70462aa4e28d3aL260)
After my change 3725b1324f (diff-71c2cad03f08cb5f6c70462aa4e28d3aL112), it now ignores 1. case and only does 2. of previous code path, because 1. does not happen anymore as I avoided to such eager execution (which is consistent with PySpark code path).
This PR proposes to do only 1. before/after execution always because It is pretty much possible R worker was failed during actual execution and it's best to show the stderr from R worker whenever possible.
### Why are the changes needed?
It currently swallows standard error from R worker which makes debugging harder.
### Does this PR introduce any user-facing change?
Yes,
```R
df <- createDataFrame(list(list(n=1)))
collect(dapply(df, function(x) {
stop("asdkjasdjkbadskjbsdajbk")
x
}, structType("a double")))
```
**Before:**
```
Error in handleErrors(returnStatus, conn) :
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 13.0 failed 1 times, most recent failure: Lost task 0.0 in stage 13.0 (TID 13, 192.168.35.193, executor driver): org.apache.spark.SparkException: R worker exited unexpectedly (cranshed)
at org.apache.spark.api.r.RRunner$$anon$1.read(RRunner.scala:130)
at org.apache.spark.api.r.BaseRRunner$ReaderIterator.hasNext(BaseRRunner.scala:118)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:726)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:337)
at org.apache.spark.
```
**After:**
```
Error in handleErrors(returnStatus, conn) :
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1, 192.168.35.193, executor driver): org.apache.spark.SparkException: R unexpectedly exited.
R worker produced errors: Error in computeFunc(inputData) : asdkjasdjkbadskjbsdajbk
at org.apache.spark.api.r.BaseRRunner$ReaderIterator$$anonfun$1.applyOrElse(BaseRRunner.scala:144)
at org.apache.spark.api.r.BaseRRunner$ReaderIterator$$anonfun$1.applyOrElse(BaseRRunner.scala:137)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:38)
at org.apache.spark.api.r.RRunner$$anon$1.read(RRunner.scala:128)
at org.apache.spark.api.r.BaseRRunner$ReaderIterator.hasNext(BaseRRunner.scala:113)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegen
```
### How was this patch tested?
Manually tested and unittest was added.
Closes#26517 from HyukjinKwon/SPARK-26923-followup.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR support defer render the spark UI page.
### Why are the changes needed?
When there are many items, such as tasks and application lists, the renderer of dataTables is heavy, we can enable deferRender to optimize it.
See details in https://datatables.net/examples/ajax/defer_render.html
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Not needed.
Closes#26482 from turboFei/SPARK-29857-defer-render.
Authored-by: turbofei <fwang12@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
This basically does what BasicDriverFeatureStep already does to achieve the
same thing in cluster mode; but since that class (or any other feature) is
not invoked in client mode, it needs to be done elsewhere.
I also modified the client mode integration test to check the executor name
prefix; while there I had to fix the minikube backend to parse the output
from newer minikube versions (I have 1.5.2).
Closes#26488 from vanzin/SPARK-29865.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Erik Erlandson <eerlands@redhat.com>
### What changes were proposed in this pull request?
remove python2.7 tests and test infra for 3.0+
### Why are the changes needed?
because python2.7 is finally going the way of the dodo.
### Does this PR introduce any user-facing change?
newp.
### How was this patch tested?
the build system will test this
Closes#26330 from shaneknapp/remove-py27-tests.
Lead-authored-by: shane knapp <incomplete@gmail.com>
Co-authored-by: shane <incomplete@gmail.com>
Signed-off-by: shane knapp <incomplete@gmail.com>
### What changes were proposed in this pull request?
This PR addresses issues where conflicting attributes in `Expand` are not correctly handled.
### Why are the changes needed?
```Scala
val numsDF = Seq(1, 2, 3, 4, 5, 6).toDF("nums")
val cubeDF = numsDF.cube("nums").agg(max(lit(0)).as("agcol"))
cubeDF.join(cubeDF, "nums").show
```
fails with the following exception:
```
org.apache.spark.sql.AnalysisException:
Failure when resolving conflicting references in Join:
'Join Inner
:- Aggregate [nums#38, spark_grouping_id#36], [nums#38, max(0) AS agcol#35]
: +- Expand [List(nums#3, nums#37, 0), List(nums#3, null, 1)], [nums#3, nums#38, spark_grouping_id#36]
: +- Project [nums#3, nums#3 AS nums#37]
: +- Project [value#1 AS nums#3]
: +- LocalRelation [value#1]
+- Aggregate [nums#38, spark_grouping_id#36], [nums#38, max(0) AS agcol#58]
+- Expand [List(nums#3, nums#37, 0), List(nums#3, null, 1)], [nums#3, nums#38, spark_grouping_id#36]
^^^^^^^
+- Project [nums#3, nums#3 AS nums#37]
+- Project [value#1 AS nums#3]
+- LocalRelation [value#1]
Conflicting attributes: nums#38
```
As you can see from the above plan, `num#38`, the output of `Expand` on the right side of `Join`, should have been handled to produce new attribute. Since the conflict is not resolved in `Expand`, the failure is happening upstream at `Aggregate`. This PR addresses handling conflicting attributes in `Expand`.
### Does this PR introduce any user-facing change?
Yes, the previous example now shows the following output:
```
+----+-----+-----+
|nums|agcol|agcol|
+----+-----+-----+
| 1| 0| 0|
| 6| 0| 0|
| 4| 0| 0|
| 2| 0| 0|
| 5| 0| 0|
| 3| 0| 0|
+----+-----+-----+
```
### How was this patch tested?
Added new unit test.
Closes#26441 from imback82/spark-29682.
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 is to support `--import` directive to load queries from another test case in SQLQueryTestSuite.
This fix comes from the cloud-fan suggestion in https://github.com/apache/spark/pull/26479#discussion_r345086978
### Why are the changes needed?
This functionality might reduce duplicate test code in `SQLQueryTestSuite`.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Run `SQLQueryTestSuite`.
Closes#26497 from maropu/ImportTests.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Make SparkSQL's `cast to boolean` behavior be consistent with PostgreSQL when
spark.sql.dialect is configured as PostgreSQL.
### Why are the changes needed?
SparkSQL and PostgreSQL have a lot different cast behavior between types by default. We should make SparkSQL's cast behavior be consistent with PostgreSQL when `spark.sql.dialect` is configured as PostgreSQL.
### Does this PR introduce any user-facing change?
Yes. If user switches to PostgreSQL dialect now, they will
* get an exception if they input a invalid string, e.g "erut", while they get `null` before;
* get an exception if they input `TimestampType`, `DateType`, `LongType`, `ShortType`, `ByteType`, `DecimalType`, `DoubleType`, `FloatType` values, while they get `true` or `false` result before.
And here're evidences for those unsupported types from PostgreSQL:
timestamp:
```
postgres=# select cast(cast('2019-11-11' as timestamp) as boolean);
ERROR: cannot cast type timestamp without time zone to boolean
```
date:
```
postgres=# select cast(cast('2019-11-11' as date) as boolean);
ERROR: cannot cast type date to boolean
```
bigint:
```
postgres=# select cast(cast('20191111' as bigint) as boolean);
ERROR: cannot cast type bigint to boolean
```
smallint:
```
postgres=# select cast(cast(2019 as smallint) as boolean);
ERROR: cannot cast type smallint to boolean
```
bytea:
```
postgres=# select cast(cast('2019' as bytea) as boolean);
ERROR: cannot cast type bytea to boolean
```
decimal:
```
postgres=# select cast(cast('2019' as decimal) as boolean);
ERROR: cannot cast type numeric to boolean
```
float:
```
postgres=# select cast(cast('2019' as float) as boolean);
ERROR: cannot cast type double precision to boolean
```
### How was this patch tested?
Added and tested manually.
Closes#26463 from Ngone51/dev-postgre-cast2bool.
Authored-by: wuyi <ngone_5451@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
We already know task attempts that do not clean up output files in staging directory can cause job failure (SPARK-27194). There was proposals trying to fix it by changing output filename, or deleting existing output files. These proposals are not reliable completely.
The difficulty is, as previous failed task attempt wrote the output file, at next task attempt the output file is still under same staging directory, even the output file name is different.
If the job will go to fail eventually, there is no point to re-run the task until max attempts are reached. For the jobs running a lot of time, re-running the task can waste a lot of time.
This patch proposes to let Spark detect such file already exist exception and stop the task set early.
### Why are the changes needed?
For now, if FileAlreadyExistsException is thrown during data writing job in SQL, the job will continue re-running task attempts until max failure number is reached. It is no point for re-running tasks as task attempts will also fail because they can not write to the existing file too. We should stop the task set early.
### Does this PR introduce any user-facing change?
Yes. If FileAlreadyExistsException is thrown during data writing job in SQL, no more task attempts are re-tried and the task set will be stoped early.
### How was this patch tested?
Unit test.
Closes#26312 from viirya/stop-taskset-if-outputfile-exists.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Corrected ShortType and ByteType mapping to SmallInt and TinyInt, corrected setter methods to set ShortType and ByteType as setShort() and setByte(). Changes in JDBCUtils.scala
Fixed Unit test cases to where applicable and added new E2E test cases in to test table read/write using ShortType and ByteType.
#### Problems
- In master in JDBCUtils.scala line number 547 and 551 have a problem where ShortType and ByteType are set as Integers rather than set as Short and Byte respectively.
```
case ShortType =>
(stmt: PreparedStatement, row: Row, pos: Int) =>
stmt.setInt(pos + 1, row.getShort(pos))
The issue was pointed out by maropu
case ByteType =>
(stmt: PreparedStatement, row: Row, pos: Int) =>
stmt.setInt(pos + 1, row.getByte(pos))
```
- Also at line JDBCUtils.scala 247 TinyInt is interpreted wrongly as IntergetType in getCatalystType()
``` case java.sql.Types.TINYINT => IntegerType ```
- At line 172 ShortType was wrongly interpreted as IntegerType
``` case ShortType => Option(JdbcType("INTEGER", java.sql.Types.SMALLINT)) ```
- All thru out tests, ShortType and ByteType were being interpreted as IntegerTypes.
### Why are the changes needed?
A given type should be set using the right type.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Corrected Unit test cases where applicable. Validated in CI/CD
Added a test case in MsSqlServerIntegrationSuite.scala, PostgresIntegrationSuite.scala , MySQLIntegrationSuite.scala to write/read tables from dataframe with cols as shorttype and bytetype. Validated by manual as follows.
```
./build/mvn install -DskipTests
./build/mvn test -Pdocker-integration-tests -pl :spark-docker-integration-tests_2.12
```
Closes#26301 from shivsood/shorttype_fix_maropu.
Authored-by: shivsood <shivsood@microsoft.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add `LaunchedExecuto`r message and send it to the driver when the executor if fully constructed, then the driver can assign the associated executor's totalCores to freeCores for making offers.
### Why are the changes needed?
The executors send RegisterExecutor messages to the driver when onStart.
The driver put the executor data in “the ready to serve map” if it could be, then send RegisteredExecutor back to the executor. The driver now can make an offer to this executor.
But the executor is not fully constructed yet. When it received RegisteredExecutor, it start to construct itself, initializing block manager, maybe register to the local shuffle server in the way of retrying, then start the heart beating to driver ...
The task allocated here may fail if the executor fails to start or cannot get heart beating to the driver in time.
Sometimes, even worse, when dynamic allocation and blacklisting is enabled and when the runtime executor number down to min executor setting, and those executors receive tasks before fully constructed and if any error happens, the application may be blocked or tear down.
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
Closes#25964 from yaooqinn/SPARK-29287.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
`saveAsTable` had an oversight where write options were not considered in the append save mode.
### Why are the changes needed?
Address the bug so that write options can be considered during appends.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Unit test added that looks in the logic plan of `AppendData` for the existing write options.
Closes#26474 from SpaceRangerWes/master.
Authored-by: Wesley Hoffman <wesleyhoffman109@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
With this change, executor's bindAddress is passed as an input parameter for RPCEnv.create.
A previous PR https://github.com/apache/spark/pull/21261 which addressed the same, was using a Spark Conf property to get the bindAddress which wouldn't have worked for multiple executors.
This PR is to enable anyone overriding CoarseGrainedExecutorBackend with their custom one to be able to invoke CoarseGrainedExecutorBackend.main() along with the option to configure bindAddress.
### Why are the changes needed?
This is required when Kernel-based Virtual Machine (KVM)'s are used inside Linux container where the hostname is not the same as container hostname.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Tested by running jobs with executors on KVMs inside a linux container.
Closes#26331 from nishchalv/SPARK-29670.
Lead-authored-by: Nishchal Venkataramana <nishchal@apple.com>
Co-authored-by: nishchal <nishchal@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
This PR adds a SQL Conf: `spark.sql.streaming.stopActiveRunOnRestart`. When this conf is `true` (by default it is), an already running stream will be stopped, if a new copy gets launched on the same checkpoint location.
### Why are the changes needed?
In multi-tenant environments where you have multiple SparkSessions, you can accidentally start multiple copies of the same stream (i.e. streams using the same checkpoint location). This will cause all new instantiations of the new stream to fail. However, sometimes you may want to turn off the old stream, as the old stream may have turned into a zombie (you no longer have access to the query handle or SparkSession).
It would be nice to have a SQL flag that allows the stopping of the old stream for such zombie cases.
### Does this PR introduce any user-facing change?
Yes. Now by default, if you launch a new copy of an already running stream on a multi-tenant cluster, the existing stream will be stopped.
### How was this patch tested?
Unit tests in StreamingQueryManagerSuite
Closes#26225 from brkyvz/stopStream.
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?
Add multi-cols support in StopWordsRemover
### Why are the changes needed?
As a basic Transformer, StopWordsRemover should support multi-cols.
Param stopWords can be applied across all columns.
### Does this PR introduce any user-facing change?
```StopWordsRemover.setInputCols```
```StopWordsRemover.setOutputCols```
### How was this patch tested?
Unit tests
Closes#26480 from huaxingao/spark-29808.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Adjust RDD to persist.
### Why are the changes needed?
To handle the improper persist strategy.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manually
Closes#26483 from amanomer/SPARK-29823.
Authored-by: Aman Omer <amanomer1996@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
rename EveryAgg/AnyAgg to BoolAnd/BoolOr
### Why are the changes needed?
Under ansi mode, `every`, `any` and `some` are reserved keywords and can't be used as function names. `EveryAgg`/`AnyAgg` has several aliases and I think it's better to not pick reserved keywords as the primary name.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#26486 from cloud-fan/naming.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
rename the config to address the comment: https://github.com/apache/spark/pull/24594#discussion_r285431212
improve the config description, provide a default value to simplify the code.
### Why are the changes needed?
make the config more understandable.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#26395 from cloud-fan/config.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add alter view link to drop view
### Why are the changes needed?
create view has links to drop view and alter view
alter view has links to create view and drop view
drop view currently doesn't have a link to alter view. I think it's better to link to alter view as well.
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
Tested using jykyll build --serve
Closes#26495 from huaxingao/spark-28798.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Use html files for the links
### Why are the changes needed?
links not working
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
Used jekyll build and serve to verify.
Closes#26494 from huaxingao/spark-28795.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
I want to say sorry! this PR follows the previous https://github.com/apache/spark/pull/26050.
I didn't find them at the same time.
The `LICENSE-binary` file exists a minor issue has an incorrect path.
This PR will fix it.
### Why are the changes needed?
This is a minor bug.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Exists UT.
Closes#26490 from beliefer/fix-minor-license-issue.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
The current parse and analyze flow for DELETE is: 1, the SQL string will be firstly parsed to `DeleteFromStatement`; 2, the `DeleteFromStatement` be converted to `DeleteFromTable`. However, the SQL string can be parsed to `DeleteFromTable` directly, where a `DeleteFromStatement` seems to be redundant.
It is the same for UPDATE.
This pr removes the unnecessary `DeleteFromStatement` and `UpdateTableStatement`.
### Why are the changes needed?
This makes the codes for DELETE and UPDATE cleaner, and keep align with MERGE INTO.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existed tests and new tests.
Closes#26464 from xianyinxin/SPARK-29835.
Authored-by: xy_xin <xianyin.xxy@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, `SupportsNamespaces.dropNamespace` drops a namespace only if it is empty. Thus, to implement a cascading drop, one needs to iterate all objects (tables, view, etc.) within the namespace (including its sub-namespaces recursively) and drop them one by one. This can have a negative impact on the performance when there are large number of objects.
Instead, this PR proposes to change the default behavior of dropping a namespace to cascading such that implementing cascading/non-cascading drop is simpler without performance penalties.
### Why are the changes needed?
The new behavior makes implementing cascading/non-cascading drop simple without performance penalties.
### Does this PR introduce any user-facing change?
Yes. The default behavior of `SupportsNamespaces.dropNamespace` is now cascading.
### How was this patch tested?
Added new unit tests.
Closes#26476 from imback82/drop_ns_cascade.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add 3 interval functions justify_days, justify_hours, justif_interval to support justify interval values
### Why are the changes needed?
For feature parity with postgres
add three interval functions to justify interval values.
justify_days(interval) | interval | Adjust interval so 30-day time periods are represented as months | justify_days(interval '35 days') | 1 mon 5 days
-- | -- | -- | -- | --
justify_hours(interval) | interval | Adjust interval so 24-hour time periods are represented as days | justify_hours(interval '27 hours') | 1 day 03:00:00
justify_interval(interval) | interval | Adjust interval using justify_days and justify_hours, with additional sign adjustments | justify_interval(interval '1 mon -1 hour') | 29 days 23:00:00
### Does this PR introduce any user-facing change?
yes. new interval functions are added
### How was this patch tested?
add ut
Closes#26465 from yaooqinn/SPARK-29390.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Follow comment of https://github.com/apache/spark/pull/25854#discussion_r342383272
### Why are the changes needed?
NO
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
ADD TEST CASE
Closes#26406 from AngersZhuuuu/SPARK-29145-FOLLOWUP.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
SPARK-29397 added new interfaces for creating driver and executor
plugins. These were added in a new, more isolated package that does
not pollute the main o.a.s package.
The old interface is now redundant. Since it's a DeveloperApi and
we're about to have a new major release, let's remove it instead of
carrying more baggage forward.
Closes#26390 from vanzin/SPARK-29399.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
All tooltips message will display in centre.
### Why are the changes needed?
Some time tooltips will hide the data of column and tooltips display position will be inconsistent in UI.
### Does this PR introduce any user-facing change?
yes.
### How was this patch tested?
Manual test.
Closes#26263 from 07ARB/SPARK-29570.
Lead-authored-by: Ankitraj <8948111+07ARB@users.noreply.github.com>
Co-authored-by: 07ARB <ankitrajboudh@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Adjust improper unpersist timing on RDD.
### Why are the changes needed?
Improper unpersist timing will result in memory waste
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manually
Closes#26469 from Icysandwich/SPARK-29844.
Authored-by: DongWang <cqwd123@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
When creating v2 expressions, we have public java APIs, as well as interval scala APIs. All of these APIs take a string column name and parse it to `NamedReference`.
This is convenient for end-users, but not for interval development. For example, the query plan already contains the parsed partition/bucket column names, and it's tricky if we need to quote the names before creating v2 expressions.
This PR proposes to change the interval scala APIs to take `NamedReference` directly, with a new method to create `NamedReference` with the exact name parts. The public java APIs are not changed.
### Why are the changes needed?
fix a bug, and make it easier to create v2 expressions correctly in the future.
### Does this PR introduce any user-facing change?
yes, now v2 CREATE TABLE works as expected.
### How was this patch tested?
a new test
Closes#26425 from cloud-fan/extract.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Ryan Blue <blue@apache.org>
### What changes were proposed in this pull request?
When whole stage codegen `HashAggregateExec`, create the hash map when we begin to process inputs.
### Why are the changes needed?
Sort-merge join completes directly if the left side table is empty. If there is an aggregate in the right side, the aggregate will not be triggered at all, but its hash map is created during codegen and can't be released.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
a new test
Closes#26471 from cloud-fan/memory.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
```sql
-- !query 83
select -integer '7'
-- !query 83 schema
struct<7:int>
-- !query 83 output
7
-- !query 86
select -date '1999-01-01'
-- !query 86 schema
struct<DATE '1999-01-01':date>
-- !query 86 output
1999-01-01
-- !query 87
select -timestamp '1999-01-01'
-- !query 87 schema
struct<TIMESTAMP('1999-01-01 00:00:00'):timestamp>
-- !query 87 output
1999-01-01 00:00:00
```
the integer should be -7 and the date and timestamp results are confusing which should throw exceptions
### Why are the changes needed?
bug fix
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
ADD UTs
Closes#26479 from yaooqinn/SPARK-29855.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Executor's heartbeat will send synchronously to BlockManagerMaster to let it know that the block manager is still alive. In a heavy cluster, it will timeout and cause block manager re-register unexpected.
This improvement will separate a heartbeat endpoint from the driver endpoint. In our production environment, this was really helpful to prevent executors from unstable up and down.
### Why are the changes needed?
`BlockManagerMasterEndpoint` handles many events from executors like `RegisterBlockManager`, `GetLocations`, `RemoveShuffle`, `RemoveExecutor` etc. In a heavy cluster/app, it is always busy. The `BlockManagerHeartbeat` event also was handled in this endpoint. We found it may timeout when it's busy. So we add a new endpoint `BlockManagerMasterHeartbeatEndpoint` to handle heartbeat separately.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Exist UTs
Closes#25971 from LantaoJin/SPARK-29298.
Lead-authored-by: lajin <lajin@ebay.com>
Co-authored-by: Alan Jin <lajin@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Print events that take too long time to process, to help find out what type of events is slow.
Introduce two extra configs:
* **spark.scheduler.listenerbus.logSlowEvent.enabled** Whether to enable log the events that are slow
* **spark.scheduler.listenerbus.logSlowEvent.threshold** The time threshold of whether an event is considered to be slow.
### How was this patch tested?
N/A
Closes#25702 from jiangxb1987/SPARK-29001.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
