### What changes were proposed in this pull request?
Move functions related test cases from `test_context.py` to `test_functions.py`.
### Why are the changes needed?
To group the similar test cases.
### Does this PR introduce _any_ user-facing change?
Nope, test-only.
### How was this patch tested?
Jenkins and GitHub Actions should test.
Closes#29898 from HyukjinKwon/SPARK-33021.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/29604 supports the ANSI SQL NTH_VALUE.
We should override the `prettyName` and `sql`.
### Why are the changes needed?
Make the name of nth_value correct.
To show the ignoreNulls parameter correctly.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#29886 from beliefer/improve-nth_value.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
When `peakMemoryMetrics` in `ExecutorSummary` is `Option.empty`, then the `ExecutorMetricsJsonSerializer#serialize` method does not execute the `jsonGenerator.writeObject` method. This causes the json to be generated with `peakMemoryMetrics` key added to the serialized string, but no corresponding value.
This causes an error to be thrown when it is the next key `attributes` turn to be added to the json:
`com.fasterxml.jackson.core.JsonGenerationException: Can not write a field name, expecting a value
`
### Why are the changes needed?
At the start of the Spark job, if `peakMemoryMetrics` is `Option.empty`, then it causes
a `com.fasterxml.jackson.core.JsonGenerationException` to be thrown when we navigate to the Executors tab in Spark UI.
Complete stacktrace:
> com.fasterxml.jackson.core.JsonGenerationException: Can not write a field name, expecting a value
> at com.fasterxml.jackson.core.JsonGenerator._reportError(JsonGenerator.java:2080)
> at com.fasterxml.jackson.core.json.WriterBasedJsonGenerator.writeFieldName(WriterBasedJsonGenerator.java:161)
> at com.fasterxml.jackson.databind.ser.BeanPropertyWriter.serializeAsField(BeanPropertyWriter.java:725)
> at com.fasterxml.jackson.databind.ser.std.BeanSerializerBase.serializeFields(BeanSerializerBase.java:721)
> at com.fasterxml.jackson.databind.ser.BeanSerializer.serialize(BeanSerializer.java:166)
> at com.fasterxml.jackson.databind.ser.std.CollectionSerializer.serializeContents(CollectionSerializer.java:145)
> at com.fasterxml.jackson.module.scala.ser.IterableSerializer.serializeContents(IterableSerializerModule.scala:26)
> at com.fasterxml.jackson.module.scala.ser.IterableSerializer.serializeContents$(IterableSerializerModule.scala:25)
> at com.fasterxml.jackson.module.scala.ser.UnresolvedIterableSerializer.serializeContents(IterableSerializerModule.scala:54)
> at com.fasterxml.jackson.module.scala.ser.UnresolvedIterableSerializer.serializeContents(IterableSerializerModule.scala:54)
> at com.fasterxml.jackson.databind.ser.std.AsArraySerializerBase.serialize(AsArraySerializerBase.java:250)
> at com.fasterxml.jackson.databind.ser.DefaultSerializerProvider._serialize(DefaultSerializerProvider.java:480)
> at com.fasterxml.jackson.databind.ser.DefaultSerializerProvider.serializeValue(DefaultSerializerProvider.java:319)
> at com.fasterxml.jackson.databind.ObjectMapper._configAndWriteValue(ObjectMapper.java:4094)
> at com.fasterxml.jackson.databind.ObjectMapper.writeValueAsString(ObjectMapper.java:3404)
> at org.apache.spark.ui.exec.ExecutorsPage.allExecutorsDataScript$1(ExecutorsTab.scala:64)
> at org.apache.spark.ui.exec.ExecutorsPage.render(ExecutorsTab.scala:76)
> at org.apache.spark.ui.WebUI.$anonfun$attachPage$1(WebUI.scala:89)
> at org.apache.spark.ui.JettyUtils$$anon$1.doGet(JettyUtils.scala:80)
> at javax.servlet.http.HttpServlet.service(HttpServlet.java:687)
> at javax.servlet.http.HttpServlet.service(HttpServlet.java:790)
> at org.sparkproject.jetty.servlet.ServletHolder.handle(ServletHolder.java:873)
> at org.sparkproject.jetty.servlet.ServletHandler$CachedChain.doFilter(ServletHandler.java:1623)
> at org.apache.spark.ui.HttpSecurityFilter.doFilter(HttpSecurityFilter.scala:95)
> at org.sparkproject.jetty.servlet.ServletHandler$CachedChain.doFilter(ServletHandler.java:1610)
> at org.sparkproject.jetty.servlet.ServletHandler.doHandle(ServletHandler.java:540)
> at org.sparkproject.jetty.server.handler.ScopedHandler.nextHandle(ScopedHandler.java:255)
> at org.sparkproject.jetty.server.handler.ContextHandler.doHandle(ContextHandler.java:1345)
> at org.sparkproject.jetty.server.handler.ScopedHandler.nextScope(ScopedHandler.java:203)
> at org.sparkproject.jetty.servlet.ServletHandler.doScope(ServletHandler.java:480)
> at org.sparkproject.jetty.server.handler.ScopedHandler.nextScope(ScopedHandler.java:201)
> at org.sparkproject.jetty.server.handler.ContextHandler.doScope(ContextHandler.java:1247)
> at org.sparkproject.jetty.server.handler.ScopedHandler.handle(ScopedHandler.java:144)
> at org.sparkproject.jetty.server.handler.gzip.GzipHandler.handle(GzipHandler.java:753)
> at org.sparkproject.jetty.server.handler.ContextHandlerCollection.handle(ContextHandlerCollection.java:220)
> at org.sparkproject.jetty.server.handler.HandlerWrapper.handle(HandlerWrapper.java:132)
> at org.sparkproject.jetty.server.Server.handle(Server.java:505)
> at org.sparkproject.jetty.server.HttpChannel.handle(HttpChannel.java:370)
> at org.sparkproject.jetty.server.HttpConnection.onFillable(HttpConnection.java:267)
> at org.sparkproject.jetty.io.AbstractConnection$ReadCallback.succeeded(AbstractConnection.java:305)
> at org.sparkproject.jetty.io.FillInterest.fillable(FillInterest.java:103)
> at org.sparkproject.jetty.io.ChannelEndPoint$2.run(ChannelEndPoint.java:117)
> at org.sparkproject.jetty.util.thread.strategy.EatWhatYouKill.runTask(EatWhatYouKill.java:333)
> at org.sparkproject.jetty.util.thread.strategy.EatWhatYouKill.doProduce(EatWhatYouKill.java:310)
> at org.sparkproject.jetty.util.thread.strategy.EatWhatYouKill.tryProduce(EatWhatYouKill.java:168)
> at org.sparkproject.jetty.util.thread.strategy.EatWhatYouKill.run(EatWhatYouKill.java:126)
> at org.sparkproject.jetty.util.thread.ReservedThreadExecutor$ReservedThread.run(ReservedThreadExecutor.java:366)
> at org.sparkproject.jetty.util.thread.QueuedThreadPool.runJob(QueuedThreadPool.java:698)
> at org.sparkproject.jetty.util.thread.QueuedThreadPool$Runner.run(QueuedThreadPool.java:804)
> at java.base/java.lang.Thread.run(Thread.java:834)
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test
Closes#29872 from shrutig/SPARK-32996.
Authored-by: Shruti Gumma <shruti_gumma@apple.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to promote the stability annotation to `Evolving` for MLEvent traits/classes.
### Why are the changes needed?
The feature is released in Spark 3.0.0 having SPARK-26818 as the last change in Feb. 2020, and haven't changed in Spark 3.0.1. (There's no change more than a half of year.)
While we'd better to wait for some minor releases to consider the API as stable, it would worth to promote to Evolving so that we clearly state that we support the API.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Just changed the annotation, no tests required.
Closes#29887 from HeartSaVioR/SPARK-33011.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to document PySpark specific packaging guidelines.
### Why are the changes needed?
To have a single place for PySpark users, and better documentation.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
```
cd python/docs
make clean html
```
Closes#29806 from fhoering/add_doc_python_packaging.
Lead-authored-by: Fabian Höring <f.horing@criteo.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add canonicalization rules for commutative bitwise operations.
### Why are the changes needed?
Canonical form is used in many other optimization rules. Reduces the number of cases, where plans with identical results are considered to be distinct.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
UT
Closes#29794 from tanelk/SPARK-32927.
Lead-authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Co-authored-by: Tanel Kiis <tanel.kiis@reach-u.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The purpose of this pr is to resolve SPARK-32972, total of 51 Scala failed test cases and 3 Java failed test cases were fixed, the main change of this pr as follow:
- Specified `Seq` to `scala.collection.Seq` in case match `Seq` scene and `x.asInstanceOf[Seq[T]]` scene
- Use `Row.getSeq[T]` instead of `Row.getAs[Seq]`
- Manual call `toMap` method to convert `MapView` to `Map` in Scala 2.13
- Change the tol in the last test to 0.75 to pass `RandomForestRegressorSuite#training with sample weights` in Scala 2.13
### Why are the changes needed?
We need to support a Scala 2.13 build.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Scala 2.12: Pass the Jenkins or GitHub Action
- Scala 2.13: Pass GitHub 2.13 Build Action
Do the follow:
```
dev/change-scala-version.sh 2.13
mvn clean install -DskipTests -pl mllib -Pscala-2.13 -am
mvn test -pl mllib -Pscala-2.13 -fn
```
**Before**
```
[ERROR] Errors:
[ERROR] JavaVectorIndexerSuite.vectorIndexerAPI:51 » ClassCast scala.collection.conver...
[ERROR] JavaWord2VecSuite.testJavaWord2Vec:51 » Spark Job aborted due to stage failure...
[ERROR] JavaPrefixSpanSuite.runPrefixSpanSaveLoad:79 » Spark Job aborted due to stage ...
Tests: succeeded 1567, failed 51, canceled 0, ignored 7, pending 0
*** 51 TESTS FAILED ***
```
**After**
```
[INFO] Tests run: 122, Failures: 0, Errors: 0, Skipped: 0
Tests: succeeded 1617, failed 0, canceled 0, ignored 7, pending 0
All tests passed.
```
Closes#29857 from LuciferYang/fix-mllib-2.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
1, update the comment: `Note, the relevant columns must also be set in inputCols` -> `Note, the relevant columns should also be set in inputCols`;
2, add a check, and if there are `categoricalCols` not set in `inputCols`, log.warn it;
### Why are the changes needed?
1, there is no check to make sure `categoricalCols` are all set in `inputCols`, to keep existing behavior, update this comments;
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
repl
Closes#29868 from zhengruifeng/feature_hash_cat_doc.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR adds two `type: ignores`, one in `pyspark.install` and one in related tests.
### Why are the changes needed?
To satisfy MyPy type checks. It seems like we originally missed some changes that happened around merge of
31a16fbb40
```
python/pyspark/install.py:30: error: Need type annotation for 'UNSUPPORTED_COMBINATIONS' (hint: "UNSUPPORTED_COMBINATIONS: List[<type>] = ...") [var-annotated]
python/pyspark/tests/test_install_spark.py:105: error: Cannot find implementation or library stub for module named 'xmlrunner' [import]
python/pyspark/tests/test_install_spark.py:105: note: See https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- Existing tests.
- MyPy tests
```
mypy --show-error-code --no-incremental --config python/mypy.ini python/pyspark
```
Closes#29878 from zero323/SPARK-32714-FOLLOW-UP.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
pre-compute the output indices of numerical columns, instead of computing them on each row.
### Why are the changes needed?
for a numerical column, its output index is a hash of its `col_name`, we can pre-compute it at first, instead of computing it on each row.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#29850 from zhengruifeng/hash_opt.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Use `Utils.getSimpleName` to avoid hitting `Malformed class name` error in `TreeNode`.
### Why are the changes needed?
On older JDK versions (e.g. JDK8u), nested Scala classes may trigger `java.lang.Class.getSimpleName` to throw an `java.lang.InternalError: Malformed class name` error.
Similar to https://github.com/apache/spark/pull/29050, we should use Spark's `Utils.getSimpleName` utility function in place of `Class.getSimpleName` to avoid hitting the issue.
### Does this PR introduce _any_ user-facing change?
Fixes a bug that throws an error when invoking `TreeNode.nodeName`, otherwise no changes.
### How was this patch tested?
Added new unit test case in `TreeNodeSuite`. Note that the test case assumes the test code can trigger the expected error, otherwise it'll skip the test safely, for compatibility with newer JDKs.
Manually tested on JDK8u and JDK11u and observed expected behavior:
- JDK8u: the test case triggers the "Malformed class name" issue and the fix works;
- JDK11u: the test case does not trigger the "Malformed class name" issue, and the test case is safely skipped.
Closes#29875 from rednaxelafx/spark-32999-getsimplename.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
`HashingTF` use `util.collection.OpenHashMap` instead of `mutable.HashMap`
### Why are the changes needed?
according to `util.collection.OpenHashMap` 's doc:
> This map is about 5X faster than java.util.HashMap, while using much less space overhead.
according to performance tests like ([Simple microbenchmarks comparing Scala vs Java mutable map performance ](https://gist.github.com/pchiusano/1423303)), `mutable.HashMap` maybe more inefficient than `java.util.HashMap`
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#29852 from zhengruifeng/hashingtf_opt.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR aims to support dynamic PVC creation and deletion in K8s driver.
**Configuration**
This PR reuses the existing PVC volume configs.
```
spark.kubernetes.driver.volumes.persistentVolumeClaim.spark-local-dir-1.options.claimName=OnDemand
spark.kubernetes.driver.volumes.persistentVolumeClaim.spark-local-dir-1.options.storageClass=gp2
spark.kubernetes.driver.volumes.persistentVolumeClaim.spark-local-dir-1.options.sizeLimit=200Gi
spark.kubernetes.driver.volumes.persistentVolumeClaim.spark-local-dir-1.mount.path=/data
spark.kubernetes.driver.volumes.persistentVolumeClaim.spark-local-dir-1.mount.readOnly=false
```
**PVC**
```
$ kubectl get pvc | grep driver
tpcds-d6087874c6705564-driver-pvc-0 Bound pvc-fae914a2-ca5c-4e1e-8aba-54a35357d072 200Gi RWO gp2 12m
```
**Disk**
```
$ k exec -it tpcds-d6087874c6705564-driver -- df -h | grep data
/dev/nvme5n1 197G 61M 197G 1% /data
```
```
$ k exec -it tpcds-d6087874c6705564-driver -- ls -al /data
total 28
drwxr-xr-x 5 root root 4096 Sep 25 18:06 .
drwxr-xr-x 1 root root 63 Sep 25 18:06 ..
drwxr-xr-x 66 root root 4096 Sep 25 18:09 blockmgr-2c9a8cc5-a05c-45fe-a58e-b8f42da88a57
drwx------ 2 root root 16384 Sep 25 18:06 lost+found
drwx------ 4 root root 4096 Sep 25 18:07 spark-0448efe7-da2c-4f3a-bd3c-769aadb11dd6
```
**NOTE**
This should be used carefully because Apache Spark doesn't delete driver pod automatically. Since the driver PVC shares the lifecycle of driver pod, it will exist after the job completion until the pod deletion. However, if the users are already using pre-populated PVCs, this isn't a regression at all in terms of the cost.
```
$ k get pod -l spark-role=driver
NAME READY STATUS RESTARTS AGE
tpcds-d6087874c6705564-driver 0/1 Completed 0 35m
```
### Why are the changes needed?
Like executors, driver also needs larger PVC.
### Does this PR introduce _any_ user-facing change?
Yes. This is a new feature.
### How was this patch tested?
Pass the newly added test case.
Closes#29873 from dongjoon-hyun/SPARK-32997.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Unevaluable expressions are not foldable because we don't have an eval for it. This PR is to clean up the code and enforce it.
### Why are the changes needed?
Ensure that we will not hit the weird cases that trigger ConstantFolding.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
The existing tests.
Closes#29798 from gatorsmile/refactorUneval.
Lead-authored-by: gatorsmile <gatorsmile@gmail.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr aims to add a new `table` API in DataStreamReader, which is similar to the table API in DataFrameReader.
### Why are the changes needed?
Users can directly use this API to get a Streaming DataFrame on a table. Below is a simple example:
Application 1 for initializing and starting the streaming job:
```
val path = "/home/yuanjian.li/runtime/to_be_deleted"
val tblName = "my_table"
// Write some data to `my_table`
spark.range(3).write.format("parquet").option("path", path).saveAsTable(tblName)
// Read the table as a streaming source, write result to destination directory
val table = spark.readStream.table(tblName)
table.writeStream.format("parquet").option("checkpointLocation", "/home/yuanjian.li/runtime/to_be_deleted_ck").start("/home/yuanjian.li/runtime/to_be_deleted_2")
```
Application 2 for appending new data:
```
// Append new data into the path
spark.range(5).write.format("parquet").option("path", "/home/yuanjian.li/runtime/to_be_deleted").mode("append").save()
```
Check result:
```
// The desitination directory should contains all written data
spark.read.parquet("/home/yuanjian.li/runtime/to_be_deleted_2").show()
```
### Does this PR introduce _any_ user-facing change?
Yes, a new API added.
### How was this patch tested?
New UT added and integrated testing.
Closes#29756 from xuanyuanking/SPARK-32885.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add test to cover Hive UDF whose input contains complex decimal type.
Add comment to explain why we can't make `HiveSimpleUDF` extend `ImplicitTypeCasts`.
### Why are the changes needed?
For better test coverage with Hive which we compatible or not.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#29863 from ulysses-you/SPARK-32877-test.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR proposes to migrate `REFRESH TABLE` to use `UnresolvedTableOrView` to resolve the table/view identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
### Why are the changes needed?
The current behavior is not consistent between v1 and v2 commands when resolving a temp view.
In v2, the `t` in the following example is resolved to a table:
```scala
sql("CREATE TABLE testcat.ns.t (id bigint) USING foo")
sql("CREATE TEMPORARY VIEW t AS SELECT 2")
sql("USE testcat.ns")
sql("REFRESH TABLE t") // 't' is resolved to testcat.ns.t
```
whereas in v1, the `t` is resolved to a temp view:
```scala
sql("CREATE DATABASE test")
sql("CREATE TABLE spark_catalog.test.t (id bigint) USING csv")
sql("CREATE TEMPORARY VIEW t AS SELECT 2")
sql("USE spark_catalog.test")
sql("REFRESH TABLE t") // 't' is resolved to a temp view
```
### Does this PR introduce _any_ user-facing change?
After this PR, `REFRESH TABLE t` is resolved to a temp view `t` instead of `testcat.ns.t`.
### How was this patch tested?
Added a new test
Closes#29866 from imback82/refresh_table_consistent.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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### What changes were proposed in this pull request?
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This moves and refactors the parallel listing utilities from `InMemoryFileIndex` to Spark core so it can be reused by modules beside SQL. Along the process this also did some cleanups/refactorings:
- Created a `HadoopFSUtils` class under core
- Moved `InMemoryFileIndex.bulkListLeafFiles` into `HadoopFSUtils.parallelListLeafFiles`. It now depends on a `SparkContext` instead of `SparkSession` in SQL. Also added a few parameters which used to be read from `SparkSession.conf`: `ignoreMissingFiles`, `ignoreLocality`, `parallelismThreshold`, `parallelismMax ` and `filterFun` (for additional filtering support but we may be able to merge this with `filter` parameter in future).
- Moved `InMemoryFileIndex.listLeafFiles` into `HadoopFSUtils.listLeafFiles` with similar changes above.
### Why are the changes needed?
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Currently the locality-aware parallel listing mechanism only applies to `InMemoryFileIndex`. By moving this to core, we can potentially reuse the same mechanism for other code paths as well.
### Does this PR introduce _any_ user-facing change?
<!--
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If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible.
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No.
### How was this patch tested?
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Since this is mostly a refactoring, it relies on existing unit tests such as those for `InMemoryFileIndex`.
Closes#29471 from sunchao/SPARK-32381.
Lead-authored-by: Chao Sun <sunchao@apache.org>
Co-authored-by: Holden Karau <hkarau@apple.com>
Co-authored-by: Chao Sun <sunchao@uber.com>
Signed-off-by: Holden Karau <hkarau@apple.com>
### What changes were proposed in this pull request?
When I tried to verify that the `resource-managers/yarn` module passed all UTs in Scala 2.13 , I found that there is a
issue related to classpath order maybe blocked the UTs because there are more than one `servlet-api` dependency in spark now:
- One is `javax.servlet:javax.servlet-api:3.10:compile` config in core/pom.xml,
- The other is `jakarta.servlet:jakarta.servlet-api:4.0.3:test` cascaded by `org.glassfish.jersey.test-framework.providers`
we can use `mvn dependency:tree` to check it .
So when we execute `resource-managers/yarn` module test use
```
mvn clean test -pl resource-managers/yarn -Pyarn
```
or
```
mvn clean test -pl resource-managers/yarn -Pyarn -Pscala-2.13
```
and if the position of `javax.servlet-api` in the in classpath is before `jakarta.servlet-api`, there are some cases failed in `YarnClusterSuite`, `YarnShuffleIntegrationSuite` and `YarnShuffleAuthSuite`.
The failed reason as follow:
```
20/09/18 19:14:07.486 launcher-proc-1 INFO YarnClusterDriver: Exception in thread "main" java.lang.ExceptionInInitializerError
...
20/09/18 19:14:07.486 launcher-proc-1 INFO YarnClusterDriver: Caused by: java.lang.SecurityException: class "javax.servlet.http.HttpSessionIdListener"'s signer information does not match signer information of other classes in the same package
...
```
### Why are the changes needed?
Avoid UTs error caused by classpath order .
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Scala 2.12: Pass the Jenkins or GitHub Action
- Scala 2.13: Pass 2.13 Build GitHub Action and do the following:
```
dev/change-scala-version.sh 2.13
mvn clean install -DskipTests -pl resource-managers/yarn -Pyarn -Pscala-2.13 -am
mvn clean test -pl resource-managers/yarn -Pyarn -Pscala-2.13
```
```
Tests: succeeded 136, failed 0, canceled 1, ignored 0, pending 0
All tests passed.
```
Closes#29824 from LuciferYang/yarn-tests-deps.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
The main change of this pr is add a manual sort to `defaultConf ++ driverConf` before constructing `--conf` options to ensure options has same order in Scala 2.12 and Scala 2.13.
### Why are the changes needed?
We need to support a Scala 2.13 build.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Scala 2.12: Pass the Jenkins or GitHub Action
- Scala 2.13: Pass GitHub 2.13 Build Action
Do the following:
```
dev/change-scala-version.sh 2.13
mvn clean install -DskipTests -pl resource-managers/mesos -Pscala-2.13 -Pmesos -am
mvn test -pl resource-managers/mesos -Pscala-2.13 -Pmesos
```
**Before**
```
Tests: succeeded 106, failed 1, canceled 0, ignored 0, pending 0
*** 1 TESTS FAILED ***
```
**After**
```
Tests: succeeded 107, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```
Closes#29865 from LuciferYang/SPARK-32987-2.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR removes Hive 1.2 option (and therefore `HIVE_VERSION` environment variable as well).
### Why are the changes needed?
Hive 1.2 is a fork version. We shouldn't promote users to use.
### Does this PR introduce _any_ user-facing change?
Nope, `HIVE_VERSION` and Hive 1.2 are removed but this is new experimental feature in master only.
### How was this patch tested?
Manually tested:
```bash
SPARK_VERSION=3.0.1 HADOOP_VERSION=3.2 pip install pyspark-3.1.0.dev0.tar.gz -v
SPARK_VERSION=3.0.1 HADOOP_VERSION=2.7 pip install pyspark-3.1.0.dev0.tar.gz -v
SPARK_VERSION=3.0.1 HADOOP_VERSION=invalid pip install pyspark-3.1.0.dev0.tar.gz -v
```
Closes#29858 from HyukjinKwon/SPARK-32981.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes migration of [`pyspark-stubs`](https://github.com/zero323/pyspark-stubs) into Spark codebase.
### Why are the changes needed?
### Does this PR introduce _any_ user-facing change?
Yes. This PR adds type annotations directly to Spark source.
This can impact interaction with development tools for users, which haven't used `pyspark-stubs`.
### How was this patch tested?
- [x] MyPy tests of the PySpark source
```
mypy --no-incremental --config python/mypy.ini python/pyspark
```
- [x] MyPy tests of Spark examples
```
MYPYPATH=python/ mypy --no-incremental --config python/mypy.ini examples/src/main/python/ml examples/src/main/python/sql examples/src/main/python/sql/streaming
```
- [x] Existing Flake8 linter
- [x] Existing unit tests
Tested against:
- `mypy==0.790+dev.e959952d9001e9713d329a2f9b196705b028f894`
- `mypy==0.782`
Closes#29591 from zero323/SPARK-32681.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a follow-up to fix Scala 2.13 compilation at Kubernetes module.
### Why are the changes needed?
To fix Scala 2.13 compilation.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the GitHub Action Scala 2.13 compilation job.
Closes#29859 from dongjoon-hyun/SPARK-32971-2.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
The default is always ErrorsOnExist regardless of DataSource version. Fixing the JavaDoc to reflect this.
### Why are the changes needed?
To fix documentation
### Does this PR introduce _any_ user-facing change?
Doc change.
### How was this patch tested?
Manual.
Closes#29853 from RussellSpitzer/SPARK-32977.
Authored-by: Russell Spitzer <russell.spitzer@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR aims to support dynamic PVC creation and deletion for K8s executors. The PVCs are created with executor pods and deleted when the executor pods are deleted.
**Configuration**
Mostly, this PR reuses the existing PVC volume configs and `storageClass` is added.
```
spark.executor.instances=2
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.options.claimName=OnDemand
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.options.storageClass=gp2
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.options.sizeLimit=500Gi
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.mount.path=/data
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.mount.readOnly=false
```
**Executors**
```
$ kubectl get pod -l spark-role=executor
NAME READY STATUS RESTARTS AGE
spark-pi-f4d80574b9bb0941-exec-1 1/1 Running 0 2m6s
spark-pi-f4d80574b9bb0941-exec-2 1/1 Running 0 2m6s
```
**PVCs**
```
$ kubectl get pvc
NAME STATUS VOLUME CAPACITY ACCESS MODES STORAGECLA
SS AGE
spark-pi-f4d80574b9bb0941-exec-1-pvc-0 Bound pvc-7d20173f-278b-4c7b-b7e5-7f0ed414ee64 500Gi RWO gp2
48s
spark-pi-f4d80574b9bb0941-exec-2-pvc-0 Bound pvc-1138f00d-87f1-47f4-9b58-ce5d13ea0c3a 500Gi RWO gp2
48s
```
**Executor Disk**
```
$ k exec -it spark-pi-f4d80574b9bb0941-exec-1 -- df -h /data
Filesystem Size Used Avail Use% Mounted on
/dev/nvme3n1 493G 74M 492G 1% /data
```
```
$ k exec -it spark-pi-f4d80574b9bb0941-exec-1 -- ls /data
blockmgr-81dcebaf-11a7-4d7b-91d6-3c580187d914
lost+found
spark-6be42db8-2c58-4389-b52c-8aeeafe76bd5
```
### Why are the changes needed?
While SPARK-32655 supports to mount a pre-created PVC, this PR can create PVC itself dynamically and reduce lots of manual efforts.
### Does this PR introduce _any_ user-facing change?
Yes. This is a new feature.
### How was this patch tested?
Pass the newly added test cases.
Closes#29846 from dongjoon-hyun/SPARK-32971.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Fixes the log strings the decom integration tests looks for and add comments reminding people to run the K8s integration tests when changing those code paths.
### Why are the changes needed?
The strings it looks for have been changed.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
WIP: Verify that the K8s jenkins job succeeds
Closes#29854 from holdenk/SPARK-32979-spark-k8s-decom-test-is-broken.
Authored-by: Holden Karau <hkarau@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Apache Spark 3.0 switches its Hive execution version from 1.2 to 2.3, but it still provides the unofficial forked Hive 1.2 version from our distribution like the following. This PR aims to remove it from Apache Spark 3.1.0 officially while keeping `hive-1.2` profile.
```
spark-3.0.1-bin-hadoop2.7-hive1.2.tgz
spark-3.0.1-bin-hadoop2.7-hive1.2.tgz.asc
spark-3.0.1-bin-hadoop2.7-hive1.2.tgz.sha512
```
### Why are the changes needed?
The unofficial Hive 1.2.1 fork has many bugs and is not maintained for a long time. We had better not recommend this in the official Apache Spark distribution.
### Does this PR introduce _any_ user-facing change?
There is no user-facing change in the default distribution (Hadoop 3.2/Hive 2.3).
### How was this patch tested?
Manually because this is a change in release script .
Closes#29856 from dongjoon-hyun/SPARK-32981.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Remove unnecessary code.
### Why are the changes needed?
General housekeeping. Might be a slight performance improvement, especially on big-endian systems.
There is no need for separate code paths for big- and little-endian
platforms in putDoubles and putFloats anymore (since PR #24861). On
all platforms values are encoded in native byte order and can just
be copied directly.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#29815 from mundaym/clean-putfloats.
Authored-by: Michael Munday <mike.munday@ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
MurmurHash3 and xxHash64 interpret sequences of bytes as integers
encoded in little-endian byte order. This requires a byte reversal
on big endian platforms.
I've left the hashInt and hashLong functions as-is for now. My
interpretation of these functions is that they perform the hash on
the integer value as if it were serialized in little-endian byte
order. Therefore no byte reversal is necessary.
### What changes were proposed in this pull request?
Modify hash functions to produce correct results on big-endian platforms.
### Why are the changes needed?
Hash functions produce incorrect results on big-endian platforms which, amongst other potential issues, causes test failures.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests run on the IBM Z (s390x) platform which uses a big-endian byte order.
Closes#29762 from mundaym/fix-hashes.
Authored-by: Michael Munday <mike.munday@ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
revert blockify gmm
### Why are the changes needed?
WeichenXu123 and I thought we should use memory size instead of number of rows to blockify instance; then if a buffer's size is large and determined by number of rows, we should discard it.
In GMM, we found that the pre-allocated memory maybe too large and should be discarded:
```
transient private lazy val auxiliaryPDFMat = DenseMatrix.zeros(blockSize, numFeatures)
```
We had some offline discuss and thought it is better to revert blockify GMM.
### Does this PR introduce _any_ user-facing change?
blockSize added in master branch will be removed
### How was this patch tested?
existing testsuites
Closes#29782 from zhengruifeng/unblockify_gmm.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
This PR addresses two issues related to the `Relation: view text` test in `DataSourceV2SQLSuite`.
1. The test has the following block:
```scala
withView("view1") { v1: String =>
sql(...)
}
```
Since `withView`'s signature is `withView(v: String*)(f: => Unit): Unit`, the `f` that will be executed is ` v1: String => sql(..)`, which is just defining the anonymous function, and _not_ executing it.
2. Once the test is fixed to run, it actually fails. The reason is that the v2 session catalog implementation used in tests does not correctly handle `V1Table` for views in `loadTable`. And this results in views resolved to `ResolvedTable` instead of `ResolvedView`, causing the test failure: f1dc479d39/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala (L1007-L1011)
### Why are the changes needed?
Fixing a bug in test.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing test.
Closes#29811 from imback82/fix_minor_test.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to replace current examples for `percentile_approx()` with **only one** input value by example **with multiple values** in the input column.
### Why are the changes needed?
Current examples are pretty trivial, and don't demonstrate function's behaviour on a sequence of values.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- by running `ExpressionInfoSuite`
- `./dev/scalastyle`
Closes#29841 from MaxGekk/example-percentile_approx.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to add a way to select Hadoop and Hive versions in pip installation.
Users can select Hive or Hadoop versions as below:
```bash
HADOOP_VERSION=3.2 pip install pyspark
HIVE_VERSION=1.2 pip install pyspark
HIVE_VERSION=1.2 HADOOP_VERSION=2.7 pip install pyspark
```
When the environment variables are set, internally it downloads the corresponding Spark version and then sets the Spark home to it. Also this PR exposes a mirror to set as an environment variable, `PYSPARK_RELEASE_MIRROR`.
**Please NOTE that:**
- We cannot currently leverage pip's native installation option, for example:
```bash
pip install pyspark --install-option="hadoop3.2"
```
This is because of a limitation and bug in pip itself. Once they fix this issue, we can switch from the environment variables to the proper installation options, see SPARK-32837.
It IS possible to workaround but very ugly or hacky with a big change. See [this PR](https://github.com/microsoft/nni/pull/139/files) as an example.
- In pip installation, we pack the relevant jars together. This PR _does not touch existing packaging way_ in order to prevent any behaviour changes.
Once this experimental way is proven to be safe, we can avoid packing the relevant jars together (and keep only the relevant Python scripts). And downloads the Spark distribution as this PR proposes.
- This way is sort of consistent with SparkR:
SparkR provides a method `SparkR::install.spark` to support CRAN installation. This is fine because SparkR is provided purely as a R library. For example, `sparkr` script is not packed together.
PySpark cannot take this approach because PySpark packaging ships relevant executable script together, e.g.) `pyspark` shell.
If PySpark has a method such as `pyspark.install_spark`, users cannot call it in `pyspark` because `pyspark` already assumes relevant Spark is installed, JVM is launched, etc.
- There looks no way to release that contains different Hadoop or Hive to PyPI due to [the version semantics](https://www.python.org/dev/peps/pep-0440/). This is not an option.
The usual way looks either `--install-option` above with hacks or environment variables given my investigation.
### Why are the changes needed?
To provide users the options to select Hadoop and Hive versions.
### Does this PR introduce _any_ user-facing change?
Yes, users will be able to select Hive and Hadoop version as below when they install it from `pip`;
```bash
HADOOP_VERSION=3.2 pip install pyspark
HIVE_VERSION=1.2 pip install pyspark
HIVE_VERSION=1.2 HADOOP_VERSION=2.7 pip install pyspark
```
### How was this patch tested?
Unit tests were added. I also manually tested in Mac and Windows (after building Spark with `python/dist/pyspark-3.1.0.dev0.tar.gz`):
```bash
./build/mvn -DskipTests -Phive-thriftserver clean package
```
Mac:
```bash
SPARK_VERSION=3.0.1 HADOOP_VERSION=3.2 pip install pyspark-3.1.0.dev0.tar.gz
```
Windows:
```bash
set HADOOP_VERSION=3.2
set SPARK_VERSION=3.0.1
pip install pyspark-3.1.0.dev0.tar.gz
```
Closes#29703 from HyukjinKwon/SPARK-32017.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adjusts signatures of methods decorated with `keyword_only` to indicate using [Python 3 keyword-only syntax](https://www.python.org/dev/peps/pep-3102/).
__Note__:
For the moment the goal is not to replace `keyword_only`. For justification see https://github.com/apache/spark/pull/29591#discussion_r489402579
### Why are the changes needed?
Right now it is not clear that `keyword_only` methods are indeed keyword only. This proposal addresses that.
In practice we could probably capture `locals` and drop `keyword_only` completel, i.e:
```python
keyword_only
def __init__(self, *, featuresCol="features"):
...
kwargs = self._input_kwargs
self.setParams(**kwargs)
```
could be replaced with
```python
def __init__(self, *, featuresCol="features"):
kwargs = locals()
del kwargs["self"]
...
self.setParams(**kwargs)
```
### Does this PR introduce _any_ user-facing change?
Docstrings and inspect tools will now indicate that `keyword_only` methods expect only keyword arguments.
For example with ` LinearSVC` will change from
```
>>> from pyspark.ml.classification import LinearSVC
>>> ?LinearSVC.__init__
Signature:
LinearSVC.__init__(
self,
featuresCol='features',
labelCol='label',
predictionCol='prediction',
maxIter=100,
regParam=0.0,
tol=1e-06,
rawPredictionCol='rawPrediction',
fitIntercept=True,
standardization=True,
threshold=0.0,
weightCol=None,
aggregationDepth=2,
)
Docstring: __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, tol=1e-6, rawPredictionCol="rawPrediction", fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, aggregationDepth=2):
File: /path/to/python/pyspark/ml/classification.py
Type: function
```
to
```
>>> from pyspark.ml.classification import LinearSVC
>>> ?LinearSVC.__init__
Signature:
LinearSVC.__init__ (
self,
*,
featuresCol='features',
labelCol='label',
predictionCol='prediction',
maxIter=100,
regParam=0.0,
tol=1e-06,
rawPredictionCol='rawPrediction',
fitIntercept=True,
standardization=True,
threshold=0.0,
weightCol=None,
aggregationDepth=2,
blockSize=1,
)
Docstring: __init__(self, \*, featuresCol="features", labelCol="label", predictionCol="prediction", maxIter=100, regParam=0.0, tol=1e-6, rawPredictionCol="rawPrediction", fitIntercept=True, standardization=True, threshold=0.0, weightCol=None, aggregationDepth=2, blockSize=1):
File: ~/Workspace/spark/python/pyspark/ml/classification.py
Type: function
```
### How was this patch tested?
Existing tests.
Closes#29799 from zero323/SPARK-32933.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
More precise description of the result of the `percentile_approx()` function and its synonym `approx_percentile()`. The proposed sentence clarifies that the function returns **one of elements** (or array of elements) from the input column.
### Why are the changes needed?
To improve Spark docs and avoid misunderstanding of the function behavior.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
`./dev/scalastyle`
Closes#29835 from MaxGekk/doc-percentile_approx.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This is a minor followup of https://github.com/apache/spark/pull/29601 , to preserve the attribute name in `SubqueryBroadcastExec.output`.
### Why are the changes needed?
During explain, it's better to see the origin column name instead of always "key".
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests.
Closes#29839 from cloud-fan/followup2.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
There is only one failed case of `streaming` module in Scala 2.13: `start with non-serializable DStream checkpoint ` in `StreamingContextSuite`.
`StackOverflowError` is thrown here when `SerializationDebugger#visit` method is called.
I found that `inputStreams` and `outputStreams` in `DStreamGraph` can not be matched in `SerializationDebugger#visit` method because `ArrayBuffer` in not `Array` in Scala 2.13.
The main change of this pr is use `mutable.ArraySeq` instead of `ArrayBuffer` to store `inputStreams` and `outputStreams` in `DStreamGraph`, then it can be matched in `SerializationDebugger#visit` method.
### Why are the changes needed?
We need to support a Scala 2.13 build.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Scala 2.12: Pass the Jenkins or GitHub Action
- Scala 2.13: Pass GitHub 2.13 Build Action
Do the following:
```
dev/change-scala-version.sh 2.13
mvn clean install -DskipTests -pl streaming -Pscala-2.13 -am
mvn test -pl streaming -Pscala-2.13
mvn test -pl core -Pscala-2.13
```
streaming module:
```
Tests: succeeded 339, failed 0, canceled 0, ignored 2, pending 0
All tests passed.
```
core module:
```
Tests: succeeded 2648, failed 0, canceled 4, ignored 7, pending 0
All tests passed.
```
Closes#29836 from LuciferYang/fix-streaming-213.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/29475.
This PR updates the code to broadcast the Array instead of Set, which was the behavior before #29475
### Why are the changes needed?
The size of Set can be much bigger than Array. It's safer to keep the behavior the same as before and build the set at the executor side.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing tests
Closes#29838 from cloud-fan/followup.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR fixes an link in `_layouts/global.html`.
The item `More` in the navigation bar in the WebUI links to `api.html` but it seems to be wrong.
This PR also removes `api.md` because it and `api.html` generated from it are not referred from anywhere.
### Why are the changes needed?
Fix the wrong link.
### Does this PR introduce _any_ user-facing change?
Yes. "More" item no longer links to `api.html`.
### How was this patch tested?
`SKIP_API=1 jekyll build` and confirmed that the item no longer links to `api.html`.
I also confirmed `api.md` and `api.html` are no longer referred from anywhere by the following command.
```
$ grep -Erl "api\.(html|md)" docs
```
Closes#29821 from sarutak/fix-api-doc-link.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds R wrapper for `timestamp_seconds` function.
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
Yes, it adds a new R function.
### How was this patch tested?
New unit tests.
Closes#29822 from zero323/SPARK-32949.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR adds foldable propagation from `Aggregate` as per: https://github.com/apache/spark/pull/29771#discussion_r490412031
### Why are the changes needed?
This is an improvement as `Aggregate`'s `aggregateExpressions` can contain foldables that can be propagated up.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New UT.
Closes#29816 from peter-toth/SPARK-32951-foldable-propagation-from-aggregate.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Now that the minimum version of PyArrow is `1.0.0`, we should update the version in the installation guide.
### Why are the changes needed?
The minimum version of PyArrow was upgraded to `1.0.0`.
### Does this PR introduce _any_ user-facing change?
Users see the correct minimum version in the installation guide.
### How was this patch tested?
N/A
Closes#29829 from ueshin/issues/SPARK-32312/doc.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Fix ".../jobs/undefined" link from "Event Timeline" in jobs page. Job page link in "Event Timeline" view is constructed by fetching job page link defined in job list below. when job count exceeds page size of job table, only links of jobs in job table can be fetched from page. Other jobs' link would be 'undefined', and links of them in "Event Timeline" are broken, they are redirected to some wired URL like ".../jobs/undefined". This PR is fixing this wrong link issue. With this PR, job link in "Event Timeline" view would always redirect to correct job page.
### Why are the changes needed?
Wrong link (".../jobs/undefined") in "Event Timeline" of jobs page. for example, the first job in below page is not in table below, as job count(116) exceeds page size(100). When clicking it's item in "Event Timeline", page is redirected to ".../jobs/undefined", which is wrong. Links in "Event Timeline" should always be correct.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manually tested.
Closes#29757 from zhli1142015/fix-link-event-timeline-view.
Authored-by: Zhen Li <zhli@microsoft.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
In current mode, when explain a SQL plan with HiveTableRelation, it will show so many info about HiveTableRelation's prunedPartition, this make plan hard to read, this pr make this information simpler.
Before:
For UT
```
test("Make HiveTableScanExec message simple") {
withSQLConf("hive.exec.dynamic.partition.mode" -> "nonstrict") {
withTable("df") {
spark.range(30)
.select(col("id"), col("id").as("k"))
.write
.partitionBy("k")
.format("hive")
.mode("overwrite")
.saveAsTable("df")
val df = sql("SELECT df.id, df.k FROM df WHERE df.k < 2")
df.explain(true)
}
}
}
```
After this pr will show
```
== Parsed Logical Plan ==
'Project ['df.id, 'df.k]
+- 'Filter ('df.k < 2)
+- 'UnresolvedRelation [df], []
== Analyzed Logical Plan ==
id: bigint, k: bigint
Project [id#11L, k#12L]
+- Filter (k#12L < cast(2 as bigint))
+- SubqueryAlias spark_catalog.default.df
+- HiveTableRelation [`default`.`df`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Data Cols: [id#11L], Partition Cols: [k#12L]]
== Optimized Logical Plan ==
Filter (isnotnull(k#12L) AND (k#12L < 2))
+- HiveTableRelation [`default`.`df`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Data Cols: [id#11L], Partition Cols: [k#12L], Pruned Partitions: [(k=0), (k=1)]]
== Physical Plan ==
Scan hive default.df [id#11L, k#12L], HiveTableRelation [`default`.`df`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Data Cols: [id#11L], Partition Cols: [k#12L], Pruned Partitions: [(k=0), (k=1)]], [isnotnull(k#12L), (k#12L < 2)]
```
In my pr, I will construct `HiveTableRelation`'s `simpleString` method to avoid show too much unnecessary info in explain plan. compared to what we had before，I decrease the detail metadata of each partition and only retain the partSpec to show each partition was pruned. Since for detail information, we always don't see this in Plan but to use DESC EXTENDED statement.
### Why are the changes needed?
Make plan about HiveTableRelation more readable
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No
Closes#29739 from AngersZhuuuu/HiveTableScan-meta-location-info.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds `withColumn` function SparkR.
### Why are the changes needed?
### Does this PR introduce _any_ user-facing change?
Yes, new function, equivalent to Scala and PySpark equivalents, is exposed to the end user.
### How was this patch tested?
New unit tests added.
Closes#29814 from zero323/SPARK-32946.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add optional `allowMissingColumns` argument to SparkR `unionByName`.
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
`unionByName` supports `allowMissingColumns`.
### How was this patch tested?
Existing unit tests. New unit tests targeting this feature.
Closes#29813 from zero323/SPARK-32799.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
