…ark shell
## What changes were proposed in this pull request?
This PR catches TypeError when testing existence of HiveConf when creating pyspark shell
## How was this patch tested?
Manually tested. Here are the manual test cases:
Build with hive:
```
(pyarrow-dev) Lis-MacBook-Pro:spark icexelloss$ bin/pyspark
Python 3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 13:44:09)
[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
18/06/14 14:55:41 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 2.4.0-SNAPSHOT
/_/
Using Python version 3.6.5 (default, Apr 6 2018 13:44:09)
SparkSession available as 'spark'.
>>> spark.conf.get('spark.sql.catalogImplementation')
'hive'
```
Build without hive:
```
(pyarrow-dev) Lis-MacBook-Pro:spark icexelloss$ bin/pyspark
Python 3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 13:44:09)
[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
18/06/14 15:04:52 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 2.4.0-SNAPSHOT
/_/
Using Python version 3.6.5 (default, Apr 6 2018 13:44:09)
SparkSession available as 'spark'.
>>> spark.conf.get('spark.sql.catalogImplementation')
'in-memory'
```
Failed to start shell:
```
(pyarrow-dev) Lis-MacBook-Pro:spark icexelloss$ bin/pyspark
Python 3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 13:44:09)
[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
18/06/14 15:07:53 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
/Users/icexelloss/workspace/spark/python/pyspark/shell.py:45: UserWarning: Failed to initialize Spark session.
warnings.warn("Failed to initialize Spark session.")
Traceback (most recent call last):
File "/Users/icexelloss/workspace/spark/python/pyspark/shell.py", line 41, in <module>
spark = SparkSession._create_shell_session()
File "/Users/icexelloss/workspace/spark/python/pyspark/sql/session.py", line 581, in _create_shell_session
return SparkSession.builder.getOrCreate()
File "/Users/icexelloss/workspace/spark/python/pyspark/sql/session.py", line 168, in getOrCreate
raise py4j.protocol.Py4JError("Fake Py4JError")
py4j.protocol.Py4JError: Fake Py4JError
(pyarrow-dev) Lis-MacBook-Pro:spark icexelloss$
```
Author: Li Jin <ice.xelloss@gmail.com>
Closes#21569 from icexelloss/SPARK-24563-fix-pyspark-shell-without-hive.
## What changes were proposed in this pull request?
`EnsureRequirement` in its `reorder` method currently assumes that the same key appears only once in the join condition. This of course might not be the case, and when it is not satisfied, it returns a wrong plan which produces a wrong result of the query.
## How was this patch tested?
added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21529 from mgaido91/SPARK-24495.
## What changes were proposed in this pull request?
We don't require specific ordering of the input data, the sort action is not necessary and misleading.
## How was this patch tested?
Existing test suite.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#21536 from jiangxb1987/sorterSuite.
## What changes were proposed in this pull request?
The PR updates the 2.3 version tested to the new release 2.3.1.
## How was this patch tested?
existing UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21543 from mgaido91/patch-1.
## What changes were proposed in this pull request?
https://docs.google.com/document/d/1IL4kJoKrZWeyIhklKUJqsW-yEN7V7aL05MmM65AYOfE/edit
Implement continuous shuffle write RDD for a single reader partition. (I don't believe any implementation changes are actually required for multiple reader partitions, but this PR is already very large, so I want to exclude those for now to keep the size down.)
## How was this patch tested?
new unit tests
Author: Jose Torres <torres.joseph.f+github@gmail.com>
Closes#21428 from jose-torres/writerTask.
## What changes were proposed in this pull request?
If you construct catalyst trees using `scala.collection.immutable.Stream` you can run into situations where valid transformations do not seem to have any effect. There are two causes for this behavior:
- `Stream` is evaluated lazily. Note that default implementation will generally only evaluate a function for the first element (this makes testing a bit tricky).
- `TreeNode` and `QueryPlan` use side effects to detect if a tree has changed. Mapping over a stream is lazy and does not need to trigger this side effect. If this happens the node will invalidly assume that it did not change and return itself instead if the newly created node (this is for GC reasons).
This PR fixes this issue by forcing materialization on streams in `TreeNode` and `QueryPlan`.
## How was this patch tested?
Unit tests were added to `TreeNodeSuite` and `LogicalPlanSuite`. An integration test was added to the `PlannerSuite`
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#21539 from hvanhovell/SPARK-24500.
## What changes were proposed in this pull request?
Currently a "StreamingQueryListener" can only be registered programatically. We could have a new config "spark.sql.streamingQueryListeners" similar to "spark.sql.queryExecutionListeners" and "spark.extraListeners" for users to register custom streaming listeners.
## How was this patch tested?
New unit test and running example programs.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Arun Mahadevan <arunm@apache.org>
Closes#21504 from arunmahadevan/SPARK-24480.
## What changes were proposed in this pull request?
This patch measures and logs elapsed time for each operation which communicate with file system (mostly remote HDFS in production) in HDFSBackedStateStoreProvider to help investigating any latency issue.
## How was this patch tested?
Manually tested.
Author: Jungtaek Lim <kabhwan@gmail.com>
Closes#21506 from HeartSaVioR/SPARK-24485.
## What changes were proposed in this pull request?
TextSocketMicroBatchReader was no longer be compatible with netcat due to launching temporary reader for reading schema, and closing reader, and re-opening reader. While reliable socket server should be able to handle this without any issue, nc command normally can't handle multiple connections and simply exits when closing temporary reader.
This patch fixes TextSocketMicroBatchReader to be compatible with netcat again, via deferring opening socket to the first call of planInputPartitions() instead of constructor.
## How was this patch tested?
Added unit test which fails on current and succeeds with the patch. And also manually tested.
Author: Jungtaek Lim <kabhwan@gmail.com>
Closes#21497 from HeartSaVioR/SPARK-24466.
## What changes were proposed in this pull request?
This PR enables using a grouped aggregate pandas UDFs as window functions. The semantics is the same as using SQL aggregation function as window functions.
```
>>> from pyspark.sql.functions import pandas_udf, PandasUDFType
>>> from pyspark.sql import Window
>>> df = spark.createDataFrame(
... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
... ("id", "v"))
>>> pandas_udf("double", PandasUDFType.GROUPED_AGG)
... def mean_udf(v):
... return v.mean()
>>> w = Window.partitionBy('id')
>>> df.withColumn('mean_v', mean_udf(df['v']).over(w)).show()
+---+----+------+
| id| v|mean_v|
+---+----+------+
| 1| 1.0| 1.5|
| 1| 2.0| 1.5|
| 2| 3.0| 6.0|
| 2| 5.0| 6.0|
| 2|10.0| 6.0|
+---+----+------+
```
The scope of this PR is somewhat limited in terms of:
(1) Only supports unbounded window, which acts essentially as group by.
(2) Only supports aggregation functions, not "transform" like window functions (n -> n mapping)
Both of these are left as future work. Especially, (1) needs careful thinking w.r.t. how to pass rolling window data to python efficiently. (2) is a bit easier but does require more changes therefore I think it's better to leave it as a separate PR.
## How was this patch tested?
WindowPandasUDFTests
Author: Li Jin <ice.xelloss@gmail.com>
Closes#21082 from icexelloss/SPARK-22239-window-udf.
## What changes were proposed in this pull request?
Currently, `spark.ui.filters` are not applied to the handlers added after binding the server. This means that every page which is added after starting the UI will not have the filters configured on it. This can allow unauthorized access to the pages.
The PR adds the filters also to the handlers added after the UI starts.
## How was this patch tested?
manual tests (without the patch, starting the thriftserver with `--conf spark.ui.filters=org.apache.hadoop.security.authentication.server.AuthenticationFilter --conf spark.org.apache.hadoop.security.authentication.server.AuthenticationFilter.params="type=simple"` you can access `http://localhost:4040/sqlserver`; with the patch, 401 is the response as for the other pages).
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21523 from mgaido91/SPARK-24506.
## What changes were proposed in this pull request?
Fix java checkstyle failure of kubernetes-integration-tests
## How was this patch tested?
Checked manually on my local environment.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#21545 from jiangxb1987/k8s-checkstyle.
## What changes were proposed in this pull request?
The PR adds the SQL function `map_from_arrays`. The behavior of the function is based on Presto's `map`. Since SparkSQL already had a `map` function, we prepared the different name for this behavior.
This function returns returns a map from a pair of arrays for keys and values.
## How was this patch tested?
Added UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21258 from kiszk/SPARK-23933.
## What changes were proposed in this pull request?
When user create a aggregator object in scala and pass the aggregator to Spark Dataset's agg() method, Spark's will initialize TypedAggregateExpression with the nodeName field as aggregator.getClass.getSimpleName. However, getSimpleName is not safe in scala environment, depending on how user creates the aggregator object. For example, if the aggregator class full qualified name is "com.my.company.MyUtils$myAgg$2$", the getSimpleName will throw java.lang.InternalError "Malformed class name". This has been reported in scalatest https://github.com/scalatest/scalatest/pull/1044 and discussed in many scala upstream jiras such as SI-8110, SI-5425.
To fix this issue, we follow the solution in https://github.com/scalatest/scalatest/pull/1044 to add safer version of getSimpleName as a util method, and TypedAggregateExpression will invoke this util method rather than getClass.getSimpleName.
## How was this patch tested?
added unit test
Author: Fangshi Li <fli@linkedin.com>
Closes#21276 from fangshil/SPARK-24216.
Signed-off-by: DylanGuedes <djmgguedesgmail.com>
## What changes were proposed in this pull request?
Addition of arrays_zip function to spark sql functions.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Unit tests that checks if the results are correct.
Author: DylanGuedes <djmgguedes@gmail.com>
Closes#21045 from DylanGuedes/SPARK-23931.
## What changes were proposed in this pull request?
spark.blacklist.killBlacklistedExecutors is defined as
(Experimental) If set to "true", allow Spark to automatically kill, and attempt to re-create, executors when they are blacklisted. Note that, when an entire node is added to the blacklist, all of the executors on that node will be killed.
I presume the killing of blacklisted executors only happens after the stage completes successfully and all tasks have completed or on fetch failures (updateBlacklistForFetchFailure/updateBlacklistForSuccessfulTaskSet). It is confusing because the definition states that the executor will be attempted to be recreated as soon as it is blacklisted. This is not true while the stage is in progress and an executor is blacklisted, it will not attempt to cleanup until the stage finishes.
Author: Sanket Chintapalli <schintap@yahoo-inc.com>
Closes#21475 from redsanket/SPARK-24416.
## What changes were proposed in this pull request?
Removing version 2.2.0 from testing versions in HiveExternalCatalogVersionsSuite as it is not present anymore in the mirrors and this is blocking all the open PRs.
## How was this patch tested?
running UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21540 from mgaido91/SPARK-24531.
## What changes were proposed in this pull request?
Add locale support for `StopWordsRemover`.
## How was this patch tested?
[Scala|Python] unit tests.
Author: Lee Dongjin <dongjin@apache.org>
Closes#21501 from dongjinleekr/feature/SPARK-15064.
no => no[t]
## What changes were proposed in this pull request?
Fixing a typo.
## How was this patch tested?
Visual check of the docs.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Tom Saleeba <tom.saleeba@gmail.com>
Closes#21496 from tomsaleeba/patch-1.
## What changes were proposed in this pull request?
`UnsafeRowSerializerSuite` calls `UnsafeProjection.create` which accesses `SQLConf.get`, while the current active SparkSession may already be stopped, and we may hit exception like this
```
sbt.ForkMain$ForkError: java.lang.IllegalStateException: LiveListenerBus is stopped.
at org.apache.spark.scheduler.LiveListenerBus.addToQueue(LiveListenerBus.scala:97)
at org.apache.spark.scheduler.LiveListenerBus.addToStatusQueue(LiveListenerBus.scala:80)
at org.apache.spark.sql.internal.SharedState.<init>(SharedState.scala:93)
at org.apache.spark.sql.SparkSession$$anonfun$sharedState$1.apply(SparkSession.scala:120)
at org.apache.spark.sql.SparkSession$$anonfun$sharedState$1.apply(SparkSession.scala:120)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession.sharedState$lzycompute(SparkSession.scala:120)
at org.apache.spark.sql.SparkSession.sharedState(SparkSession.scala:119)
at org.apache.spark.sql.internal.BaseSessionStateBuilder.build(BaseSessionStateBuilder.scala:286)
at org.apache.spark.sql.test.TestSparkSession.sessionState$lzycompute(TestSQLContext.scala:42)
at org.apache.spark.sql.test.TestSparkSession.sessionState(TestSQLContext.scala:41)
at org.apache.spark.sql.SparkSession$$anonfun$1$$anonfun$apply$1.apply(SparkSession.scala:95)
at org.apache.spark.sql.SparkSession$$anonfun$1$$anonfun$apply$1.apply(SparkSession.scala:95)
at scala.Option.map(Option.scala:146)
at org.apache.spark.sql.SparkSession$$anonfun$1.apply(SparkSession.scala:95)
at org.apache.spark.sql.SparkSession$$anonfun$1.apply(SparkSession.scala:94)
at org.apache.spark.sql.internal.SQLConf$.get(SQLConf.scala:126)
at org.apache.spark.sql.catalyst.expressions.CodeGeneratorWithInterpretedFallback.createObject(CodeGeneratorWithInterpretedFallback.scala:54)
at org.apache.spark.sql.catalyst.expressions.UnsafeProjection$.create(Projection.scala:157)
at org.apache.spark.sql.catalyst.expressions.UnsafeProjection$.create(Projection.scala:150)
at org.apache.spark.sql.execution.UnsafeRowSerializerSuite.org$apache$spark$sql$execution$UnsafeRowSerializerSuite$$unsafeRowConverter(UnsafeRowSerializerSuite.scala:54)
at org.apache.spark.sql.execution.UnsafeRowSerializerSuite.org$apache$spark$sql$execution$UnsafeRowSerializerSuite$$toUnsafeRow(UnsafeRowSerializerSuite.scala:49)
at org.apache.spark.sql.execution.UnsafeRowSerializerSuite$$anonfun$2.apply(UnsafeRowSerializerSuite.scala:63)
at org.apache.spark.sql.execution.UnsafeRowSerializerSuite$$anonfun$2.apply(UnsafeRowSerializerSuite.scala:60)
...
```
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#21518 from cloud-fan/test.
Apply the suggestion on the bug to fix source links. Tested with
the 2.3.1 release docs.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#21521 from vanzin/SPARK-23732.
## What changes were proposed in this pull request?
when the length of pre-shuffle's partitions is 0, the length of post-shuffle's partitions should be 0 instead of spark.sql.shuffle.partitions.
## How was this patch tested?
ExchangeCoordinator converted a pre-shuffle that partitions is 0 to a post-shuffle that partitions is 0 instead of one that partitions is spark.sql.shuffle.partitions.
Author: liutang123 <liutang123@yeah.net>
Closes#19364 from liutang123/SPARK-22144.
There are double braces in the markdown, which break the link.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Fokko Driesprong <fokkodriesprong@godatadriven.com>
Closes#21528 from Fokko/patch-1.
## What changes were proposed in this pull request?
When HadoopMapRedCommitProtocol is used (e.g., when using saveAsTextFile() or
saveAsHadoopFile() with RDDs), it's not easy to determine which output committer
class was used, so this PR simply logs the class that was used, similarly to what
is done in SQLHadoopMapReduceCommitProtocol.
## How was this patch tested?
Built Spark then manually inspected logging when calling saveAsTextFile():
```scala
scala> sc.setLogLevel("INFO")
scala> sc.textFile("README.md").saveAsTextFile("/tmp/out")
...
18/05/29 10:06:20 INFO HadoopMapRedCommitProtocol: Using output committer class org.apache.hadoop.mapred.FileOutputCommitter
```
Author: Jonathan Kelly <jonathak@amazon.com>
Closes#21452 from ejono/master.
## What changes were proposed in this pull request?
add spark.ml Python API for PIC
## How was this patch tested?
add doctest
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21513 from huaxingao/spark--19826.
## What changes were proposed in this pull request?
SPARK-23754 was fixed in #21383 by changing the UDF code to wrap the user function, but this required a hack to save its argspec. This PR reverts this change and fixes the `StopIteration` bug in the worker
## How does this work?
The root of the problem is that when an user-supplied function raises a `StopIteration`, pyspark might stop processing data, if this function is used in a for-loop. The solution is to catch `StopIteration`s exceptions and re-raise them as `RuntimeError`s, so that the execution fails and the error is reported to the user. This is done using the `fail_on_stopiteration` wrapper, in different ways depending on where the function is used:
- In RDDs, the user function is wrapped in the driver, because this function is also called in the driver itself.
- In SQL UDFs, the function is wrapped in the worker, since all processing happens there. Moreover, the worker needs the signature of the user function, which is lost when wrapping it, but passing this signature to the worker requires a not so nice hack.
## How was this patch tested?
Same tests, plus tests for pandas UDFs
Author: edorigatti <emilio.dorigatti@gmail.com>
Closes#21467 from e-dorigatti/fix_udf_hack.
## What changes were proposed in this pull request?
In SPARK-22036 we introduced the possibility to allow precision loss in arithmetic operations (according to the SQL standard). The implementation was drawn from Hive's one, where Decimals with a negative scale are not allowed in the operations.
The PR handles the case when the scale is negative, removing the assertion that it is not.
## How was this patch tested?
added UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21499 from mgaido91/SPARK-24468.
## What changes were proposed in this pull request?
Update documentation for `isInCollection` API to clealy explain the "auto-casting" of elements if their types are different.
## How was this patch tested?
No-Op
Author: Thiruvasakan Paramasivan <thiru@apple.com>
Closes#21519 from trvskn/sql-doc-update.
These tests were developed in the https://github.com/apache-spark-on-k8s/spark-integration repo
by several contributors. This is a copy of the current state into the main apache spark repo.
The only changes from the current spark-integration repo state are:
* Move the files from the repo root into resource-managers/kubernetes/integration-tests
* Add a reference to these tests in the root README.md
* Fix a path reference in dev/dev-run-integration-tests.sh
* Add a TODO in include/util.sh
## What changes were proposed in this pull request?
Incorporation of Kubernetes integration tests.
## How was this patch tested?
This code has its own unit tests, but the main purpose is to provide the integration tests.
I tested this on my laptop by running dev/dev-run-integration-tests.sh --spark-tgz ~/spark-2.4.0-SNAPSHOT-bin--.tgz
The spark-integration tests have already been running for months in AMPLab, here is an example:
https://amplab.cs.berkeley.edu/jenkins/job/testing-k8s-scheduled-spark-integration-master/
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Sean Suchter <sean-github@suchter.com>
Author: Sean Suchter <ssuchter@pepperdata.com>
Closes#20697 from ssuchter/ssuchter-k8s-integration-tests.
## What changes were proposed in this pull request?
This PR proposes to wrap the transformed rdd within `TransformFunction`. `PythonTransformFunction` looks requiring to return `JavaRDD` in `_jrdd`.
39e2bad6a8/python/pyspark/streaming/util.py (L67)6ee28423ad/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala (L43)
However, this could be `JavaPairRDD` by some APIs, for example, `zip` in PySpark's RDD API.
`_jrdd` could be checked as below:
```python
>>> rdd.zip(rdd)._jrdd.getClass().toString()
u'class org.apache.spark.api.java.JavaPairRDD'
```
So, here, I wrapped it with `map` so that it ensures returning `JavaRDD`.
```python
>>> rdd.zip(rdd).map(lambda x: x)._jrdd.getClass().toString()
u'class org.apache.spark.api.java.JavaRDD'
```
I tried to elaborate some failure cases as below:
```python
from pyspark.streaming import StreamingContext
ssc = StreamingContext(spark.sparkContext, 10)
ssc.queueStream([sc.range(10)]) \
.transform(lambda rdd: rdd.cartesian(rdd)) \
.pprint()
ssc.start()
```
```python
from pyspark.streaming import StreamingContext
ssc = StreamingContext(spark.sparkContext, 10)
ssc.queueStream([sc.range(10)]).foreachRDD(lambda rdd: rdd.cartesian(rdd))
ssc.start()
```
```python
from pyspark.streaming import StreamingContext
ssc = StreamingContext(spark.sparkContext, 10)
ssc.queueStream([sc.range(10)]).foreachRDD(lambda rdd: rdd.zip(rdd))
ssc.start()
```
```python
from pyspark.streaming import StreamingContext
ssc = StreamingContext(spark.sparkContext, 10)
ssc.queueStream([sc.range(10)]).foreachRDD(lambda rdd: rdd.zip(rdd).union(rdd.zip(rdd)))
ssc.start()
```
```python
from pyspark.streaming import StreamingContext
ssc = StreamingContext(spark.sparkContext, 10)
ssc.queueStream([sc.range(10)]).foreachRDD(lambda rdd: rdd.zip(rdd).coalesce(1))
ssc.start()
```
## How was this patch tested?
Unit tests were added in `python/pyspark/streaming/tests.py` and manually tested.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19498 from HyukjinKwon/SPARK-17756.
## What changes were proposed in this pull request?
Introducing Python Bindings for PySpark.
- [x] Running PySpark Jobs
- [x] Increased Default Memory Overhead value
- [ ] Dependency Management for virtualenv/conda
## How was this patch tested?
This patch was tested with
- [x] Unit Tests
- [x] Integration tests with [this addition](https://github.com/apache-spark-on-k8s/spark-integration/pull/46)
```
KubernetesSuite:
- Run SparkPi with no resources
- Run SparkPi with a very long application name.
- Run SparkPi with a master URL without a scheme.
- Run SparkPi with an argument.
- Run SparkPi with custom labels, annotations, and environment variables.
- Run SparkPi with a test secret mounted into the driver and executor pods
- Run extraJVMOptions check on driver
- Run SparkRemoteFileTest using a remote data file
- Run PySpark on simple pi.py example
- Run PySpark with Python2 to test a pyfiles example
- Run PySpark with Python3 to test a pyfiles example
Run completed in 4 minutes, 28 seconds.
Total number of tests run: 11
Suites: completed 2, aborted 0
Tests: succeeded 11, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```
Author: Ilan Filonenko <if56@cornell.edu>
Author: Ilan Filonenko <ifilondz@gmail.com>
Closes#21092 from ifilonenko/master.
## What changes were proposed in this pull request?
This PR attaches submodules to ml's `__init__.py` module.
Also, adds `ImageSchema` into `image.py` explicitly.
## How was this patch tested?
Before:
```python
>>> from pyspark import ml
>>> ml.image
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'module' object has no attribute 'image'
>>> ml.image.ImageSchema
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'module' object has no attribute 'image'
```
```python
>>> "image" in globals()
False
>>> from pyspark.ml import *
>>> "image" in globals()
False
>>> image
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'image' is not defined
```
After:
```python
>>> from pyspark import ml
>>> ml.image
<module 'pyspark.ml.image' from '/.../spark/python/pyspark/ml/image.pyc'>
>>> ml.image.ImageSchema
<pyspark.ml.image._ImageSchema object at 0x10d973b10>
```
```python
>>> "image" in globals()
False
>>> from pyspark.ml import *
>>> "image" in globals()
True
>>> image
<module 'pyspark.ml.image' from #'/.../spark/python/pyspark/ml/image.pyc'>
```
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21483 from HyukjinKwon/SPARK-24454.
## What changes were proposed in this pull request?
Added example code for Power Iteration Clustering in Spark ML examples
Author: Shahid <shahidki31@gmail.com>
Closes#21248 from shahidki31/sparkCommit.
## What changes were proposed in this pull request?
Java example code for Power Iteration Clustering in spark.ml
## How was this patch tested?
Locally tested
Author: Shahid <shahidki31@gmail.com>
Closes#21283 from shahidki31/JavaPicExample.
## What changes were proposed in this pull request?
Implemented eval in SortPrefix expression.
## How was this patch tested?
- ran existing sbt SQL tests
- added unit test
- ran existing Python SQL tests
- manual tests: disabling codegen -- patching code to disable beyond what spark.sql.codegen.wholeStage=false can do -- and running sbt SQL tests
Author: Bruce Robbins <bersprockets@gmail.com>
Closes#21231 from bersprockets/sortprefixeval.
## What changes were proposed in this pull request?
support bucket pruning when filtering on a single bucketed column on the following predicates -
EqualTo, EqualNullSafe, In, And/Or predicates
## How was this patch tested?
refactored unit tests to test the above.
based on gatorsmile work in e3c75c6398
Author: Asher Saban <asaban@palantir.com>
Author: asaban <asaban@palantir.com>
Closes#20915 from sabanas/filter-prune-buckets.
## What changes were proposed in this pull request?
This PR adds array_join function to SparkR
## How was this patch tested?
Add unit test in test_sparkSQL.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21313 from huaxingao/spark-24187.
## What changes were proposed in this pull request?
Sql below will get all partitions from metastore, which put much burden on metastore;
```
CREATE TABLE `partition_test`(`col` int) PARTITIONED BY (`pt` byte)
SELECT * FROM partition_test WHERE CAST(pt AS INT)=1
```
The reason is that the the analyzed attribute `dt` is wrapped in `Cast` and `HiveShim` fails to generate a proper partition filter.
This pr proposes to take `Cast` into consideration when generate partition filter.
## How was this patch tested?
Test added.
This pr proposes to use analyzed expressions in `HiveClientSuite`
Author: jinxing <jinxing6042@126.com>
Closes#19602 from jinxing64/SPARK-22384.
## What changes were proposed in this pull request?
The error occurs when we are recovering from a failure in a no-data batch (say X) that has been planned (i.e. written to offset log) but not executed (i.e. not written to commit log). Upon recovery the following sequence of events happen.
1. `MicroBatchExecution.populateStartOffsets` sets `currentBatchId` to X. Since there was no data in the batch, the `availableOffsets` is same as `committedOffsets`, so `isNewDataAvailable` is `false`.
2. When `MicroBatchExecution.constructNextBatch` is called, ideally it should immediately return true because the next batch has already been constructed. However, the check of whether the batch has been constructed was `if (isNewDataAvailable) return true`. Since the planned batch is a no-data batch, it escaped this check and proceeded to plan the same batch X *once again*.
The solution is to have an explicit flag that signifies whether a batch has already been constructed or not. `populateStartOffsets` is going to set the flag appropriately.
## How was this patch tested?
new unit test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#21491 from tdas/SPARK-24453.
## What changes were proposed in this pull request?
According to the discussion on JIRA. I rewrite the Power Iteration Clustering API in `spark.ml`.
## How was this patch tested?
Unit test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#21493 from WeichenXu123/pic_api.
Currently, in spark-shell, if the session fails to start, the
user sees a bunch of unrelated errors which are caused by code
in the shell initialization that references the "spark" variable,
which does not exist in that case. Things like:
```
<console>:14: error: not found: value spark
import spark.sql
```
The user is also left with a non-working shell (unless they want
to just write non-Spark Scala or Python code, that is).
This change fails the whole shell session at the point where the
failure occurs, so that the last error message is the one with
the actual information about the failure.
For the python error handling, I moved the session initialization code
to session.py, so that traceback.print_exc() only shows the last error.
Otherwise, the printed exception would contain all previous exceptions
with a message "During handling of the above exception, another
exception occurred", making the actual error kinda hard to parse.
Tested with spark-shell, pyspark (with 2.7 and 3.5), by forcing an
error during SparkContext initialization.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#21368 from vanzin/SPARK-16451.
## What changes were proposed in this pull request?
Using different RNG in all different partitions.
## How was this patch tested?
manually
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21492 from ludatabricks/SPARK-24300.
## What changes were proposed in this pull request?
Extend instrumentation.logNamedValue to support Array input
change the logging for "clusterSizes" to new method
## How was this patch tested?
N/A
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21347 from ludatabricks/SPARK-24290.
## What changes were proposed in this pull request?
This PR explicitly prohibits window functions inside aggregates. Currently, this will cause StackOverflow during analysis. See PR #19193 for previous discussion.
## How was this patch tested?
This PR comes with a dedicated unit test.
Author: aokolnychyi <anton.okolnychyi@sap.com>
Closes#21473 from aokolnychyi/fix-stackoverflow-window-funcs.
## What changes were proposed in this pull request?
Add support for date `extract` function:
```sql
spark-sql> SELECT EXTRACT(YEAR FROM TIMESTAMP '2000-12-16 12:21:13');
2000
```
Supported field same as [Hive](https://github.com/apache/hive/blob/rel/release-2.3.3/ql/src/java/org/apache/hadoop/hive/ql/parse/IdentifiersParser.g#L308-L316): `YEAR`, `QUARTER`, `MONTH`, `WEEK`, `DAY`, `DAYOFWEEK`, `HOUR`, `MINUTE`, `SECOND`.
## How was this patch tested?
unit tests
Author: Yuming Wang <yumwang@ebay.com>
Closes#21479 from wangyum/SPARK-23903.
## What changes were proposed in this pull request?
Currently column names of headers in CSV files are not checked against provided schema of CSV data. It could cause errors like showed in the [SPARK-23786](https://issues.apache.org/jira/browse/SPARK-23786) and https://github.com/apache/spark/pull/20894#issuecomment-375957777. I introduced new CSV option - `enforceSchema`. If it is enabled (by default `true`), Spark forcibly applies provided or inferred schema to CSV files. In that case, CSV headers are ignored and not checked against the schema. If `enforceSchema` is set to `false`, additional checks can be performed. For example, if column in CSV header and in the schema have different ordering, the following exception is thrown:
```
java.lang.IllegalArgumentException: CSV file header does not contain the expected fields
Header: depth, temperature
Schema: temperature, depth
CSV file: marina.csv
```
## How was this patch tested?
The changes were tested by existing tests of CSVSuite and by 2 new tests.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Author: Maxim Gekk <max.gekk@gmail.com>
Closes#20894 from MaxGekk/check-column-names.
## What changes were proposed in this pull request?
bring back https://github.com/apache/spark/pull/21443
This is a different approach: just change the check to count distinct columns with `toSet`
## How was this patch tested?
a new test to verify the planner behavior.
Author: Wenchen Fan <wenchen@databricks.com>
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#21487 from cloud-fan/back.
change runTasks to submitTasks in the TaskSchedulerImpl.scala 's comment
Author: xueyu <xueyu@yidian-inc.com>
Author: Xue Yu <278006819@qq.com>
Closes#21485 from xueyumusic/fixtypo1.
