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…uffle ## What changes were proposed in this pull request? In Spark SQL, we usually reuse the `UnsafeRow` instance and need to copy the data when a place buffers non-serialized objects. Shuffle may buffer objects if we don't make it to the bypass merge shuffle or unsafe shuffle. `ShuffleExchangeExec.needToCopyObjectsBeforeShuffle` misses the case that, if `spark.sql.shuffle.partitions` is large enough, we could fail to run unsafe shuffle and go with the non-serialized shuffle. This bug is very hard to hit since users wouldn't set such a large number of partitions(16 million) for Spark SQL exchange. TODO: test ## How was this patch tested? todo. Author: Wenchen Fan <wenchen@databricks.com> Closes #21101 from cloud-fan/shuffle.
## What changes were proposed in this pull request? This PR adds PowerIterationClustering as a Transformer to spark.ml. In the transform method, it calls spark.mllib's PowerIterationClustering.run() method and transforms the return value assignments (the Kmeans output of the pseudo-eigenvector) as a DataFrame (id: LongType, cluster: IntegerType). This PR is copied and modified from #15770 The primary author is wangmiao1981 ## How was this patch tested? This PR has 2 types of tests: * Copies of tests from spark.mllib's PIC tests * New tests specific to the spark.ml APIs Author: wm624@hotmail.com <wm624@hotmail.com> Author: wangmiao1981 <wm624@hotmail.com> Author: Joseph K. Bradley <joseph@databricks.com> Closes #21090 from jkbradley/wangmiao1981-pic.
## What changes were proposed in this pull request? Improving the test coverage of window functions focusing on missing test for window aggregate functions. No new UDAF test is added as it has been tested already. ## How was this patch tested? Only new tests were added, automated tests were executed. Author: “attilapiros” <piros.attila.zsolt@gmail.com> Author: Attila Zsolt Piros <2017933+attilapiros@users.noreply.github.com> Closes #20046 from attilapiros/SPARK-22362.
## What changes were proposed in this pull request?
SparkContextSuite.test("Cancelling stages/jobs with custom reasons.") could stay in an infinite loop because of the problem found and fixed in [SPARK-23775](https://issues.apache.org/jira/browse/SPARK-23775).
This PR solves this mentioned flakyness by removing shared variable usages when cancel happens in a loop and using wait and CountDownLatch for synhronization.
## How was this patch tested?
Existing unit test.
Author: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Closes #21105 from gaborgsomogyi/SPARK-24022.
…ata-only queries ## What changes were proposed in this pull request? This updates the OptimizeMetadataOnlyQuery rule to use filter expressions when listing partitions, if there are filter nodes in the logical plan. This avoids listing all partitions for large tables on the driver. This also fixes a minor bug where the partitions returned from fsRelation cannot be serialized without hitting a stack level too deep error. This is caused by serializing a stream to executors, where the stream is a recursive structure. If the stream is too long, the serialization stack reaches the maximum level of depth. The fix is to create a LocalRelation using an Array instead of the incoming Seq. ## How was this patch tested? Existing tests for metadata-only queries. Author: Ryan Blue <blue@apache.org> Closes #20988 from rdblue/SPARK-23877-metadata-only-push-filters.
…umns
## What changes were proposed in this pull request?
The PR adds a logic for easy concatenation of multiple array columns and covers:
- Concat expression has been extended to support array columns
- A Python wrapper
## How was this patch tested?
New tests added into:
- CollectionExpressionsSuite
- DataFrameFunctionsSuite
- typeCoercion/native/concat.sql
## Codegen examples
### Primitive-type elements
```
val df = Seq(
(Seq(1 ,2), Seq(3, 4)),
(Seq(1, 2, 3), null)
).toDF("a", "b")
df.filter('a.isNotNull).select(concat('a, 'b)).debugCodegen()
```
Result:
```
/* 033 */ boolean inputadapter_isNull = inputadapter_row.isNullAt(0);
/* 034 */ ArrayData inputadapter_value = inputadapter_isNull ?
/* 035 */ null : (inputadapter_row.getArray(0));
/* 036 */
/* 037 */ if (!(!inputadapter_isNull)) continue;
/* 038 */
/* 039 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1);
/* 040 */
/* 041 */ ArrayData[] project_args = new ArrayData[2];
/* 042 */
/* 043 */ if (!false) {
/* 044 */ project_args[0] = inputadapter_value;
/* 045 */ }
/* 046 */
/* 047 */ boolean inputadapter_isNull1 = inputadapter_row.isNullAt(1);
/* 048 */ ArrayData inputadapter_value1 = inputadapter_isNull1 ?
/* 049 */ null : (inputadapter_row.getArray(1));
/* 050 */ if (!inputadapter_isNull1) {
/* 051 */ project_args[1] = inputadapter_value1;
/* 052 */ }
/* 053 */
/* 054 */ ArrayData project_value = new Object() {
/* 055 */ public ArrayData concat(ArrayData[] args) {
/* 056 */ for (int z = 0; z < 2; z++) {
/* 057 */ if (args[z] == null) return null;
/* 058 */ }
/* 059 */
/* 060 */ long project_numElements = 0L;
/* 061 */ for (int z = 0; z < 2; z++) {
/* 062 */ project_numElements += args[z].numElements();
/* 063 */ }
/* 064 */ if (project_numElements > 2147483632) {
/* 065 */ throw new RuntimeException("Unsuccessful try to concat arrays with " + project_numElements +
/* 066 */ " elements due to exceeding the array size limit 2147483632.");
/* 067 */ }
/* 068 */
/* 069 */ long project_size = UnsafeArrayData.calculateSizeOfUnderlyingByteArray(
/* 070 */ project_numElements,
/* 071 */ 4);
/* 072 */ if (project_size > 2147483632) {
/* 073 */ throw new RuntimeException("Unsuccessful try to concat arrays with " + project_size +
/* 074 */ " bytes of data due to exceeding the limit 2147483632 bytes" +
/* 075 */ " for UnsafeArrayData.");
/* 076 */ }
/* 077 */
/* 078 */ byte[] project_array = new byte[(int)project_size];
/* 079 */ UnsafeArrayData project_arrayData = new UnsafeArrayData();
/* 080 */ Platform.putLong(project_array, 16, project_numElements);
/* 081 */ project_arrayData.pointTo(project_array, 16, (int)project_size);
/* 082 */ int project_counter = 0;
/* 083 */ for (int y = 0; y < 2; y++) {
/* 084 */ for (int z = 0; z < args[y].numElements(); z++) {
/* 085 */ if (args[y].isNullAt(z)) {
/* 086 */ project_arrayData.setNullAt(project_counter);
/* 087 */ } else {
/* 088 */ project_arrayData.setInt(
/* 089 */ project_counter,
/* 090 */ args[y].getInt(z)
/* 091 */ );
/* 092 */ }
/* 093 */ project_counter++;
/* 094 */ }
/* 095 */ }
/* 096 */ return project_arrayData;
/* 097 */ }
/* 098 */ }.concat(project_args);
/* 099 */ boolean project_isNull = project_value == null;
```
### Non-primitive-type elements
```
val df = Seq(
(Seq("aa" ,"bb"), Seq("ccc", "ddd")),
(Seq("x", "y"), null)
).toDF("a", "b")
df.filter('a.isNotNull).select(concat('a, 'b)).debugCodegen()
```
Result:
```
/* 033 */ boolean inputadapter_isNull = inputadapter_row.isNullAt(0);
/* 034 */ ArrayData inputadapter_value = inputadapter_isNull ?
/* 035 */ null : (inputadapter_row.getArray(0));
/* 036 */
/* 037 */ if (!(!inputadapter_isNull)) continue;
/* 038 */
/* 039 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1);
/* 040 */
/* 041 */ ArrayData[] project_args = new ArrayData[2];
/* 042 */
/* 043 */ if (!false) {
/* 044 */ project_args[0] = inputadapter_value;
/* 045 */ }
/* 046 */
/* 047 */ boolean inputadapter_isNull1 = inputadapter_row.isNullAt(1);
/* 048 */ ArrayData inputadapter_value1 = inputadapter_isNull1 ?
/* 049 */ null : (inputadapter_row.getArray(1));
/* 050 */ if (!inputadapter_isNull1) {
/* 051 */ project_args[1] = inputadapter_value1;
/* 052 */ }
/* 053 */
/* 054 */ ArrayData project_value = new Object() {
/* 055 */ public ArrayData concat(ArrayData[] args) {
/* 056 */ for (int z = 0; z < 2; z++) {
/* 057 */ if (args[z] == null) return null;
/* 058 */ }
/* 059 */
/* 060 */ long project_numElements = 0L;
/* 061 */ for (int z = 0; z < 2; z++) {
/* 062 */ project_numElements += args[z].numElements();
/* 063 */ }
/* 064 */ if (project_numElements > 2147483632) {
/* 065 */ throw new RuntimeException("Unsuccessful try to concat arrays with " + project_numElements +
/* 066 */ " elements due to exceeding the array size limit 2147483632.");
/* 067 */ }
/* 068 */
/* 069 */ Object[] project_arrayObjects = new Object[(int)project_numElements];
/* 070 */ int project_counter = 0;
/* 071 */ for (int y = 0; y < 2; y++) {
/* 072 */ for (int z = 0; z < args[y].numElements(); z++) {
/* 073 */ project_arrayObjects[project_counter] = args[y].getUTF8String(z);
/* 074 */ project_counter++;
/* 075 */ }
/* 076 */ }
/* 077 */ return new org.apache.spark.sql.catalyst.util.GenericArrayData(project_arrayObjects);
/* 078 */ }
/* 079 */ }.concat(project_args);
/* 080 */ boolean project_isNull = project_value == null;
```
Author: mn-mikke <mrkAha12346github>
Closes #20858 from mn-mikke/feature/array-api-concat_arrays-to-master.
…ion in CatalystToExternalMap ## What changes were proposed in this pull request? This pr is a follow-up pr of #20979 and fixes code to resolve a map builder method per execution instead of per row in `CatalystToExternalMap`. ## How was this patch tested? Existing tests. Author: Takeshi Yamamuro <yamamuro@apache.org> Closes #21112 from maropu/SPARK-23588-FOLLOWUP.
…ecution ## What changes were proposed in this pull request? This pr supported interpreted mode for `ValidateExternalType`. ## How was this patch tested? Added tests in `ObjectExpressionsSuite`. Author: Takeshi Yamamuro <yamamuro@apache.org> Closes #20757 from maropu/SPARK-23595.
This reverts commit 0c94e48.
This allows sockets to be bound even if there are sockets from a previous application that are still pending closure. It avoids bind issues when, for example, re-starting the SHS. Don't enable the option on Windows though. The following page explains some odd behavior that this option can have there: https://msdn.microsoft.com/en-us/library/windows/desktop/ms740621%28v=vs.85%29.aspx I intentionally ignored server sockets that always bind to ephemeral ports, since those don't benefit from this option. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #21110 from vanzin/SPARK-24029.
…e(RowFrame, -1, -1) ## What changes were proposed in this pull request? When the OffsetWindowFunction's frame is `UnaryMinus(Literal(1))` but the specified window frame has been simplified to `Literal(-1)` by some optimizer rules e.g., `ConstantFolding`. Thus, they do not match and cause the following error: ``` org.apache.spark.sql.AnalysisException: Window Frame specifiedwindowframe(RowFrame, -1, -1) must match the required frame specifiedwindowframe(RowFrame, -1, -1); at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:41) at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:91) at ``` ## How was this patch tested? Added a test Author: gatorsmile <gatorsmile@gmail.com> Closes #21115 from gatorsmile/fixLag.
…y zero in a case of empty table with analyzed statistics >What changes were proposed in this pull request? During evaluation of IN conditions, if the source data frame, is represented by a plan, that uses hive table with columns, which were previously analysed, and the plan has conditions for these fields, that cannot be satisfied (which leads us to an empty data frame), FilterEstimation.evaluateInSet method produces NumberFormatException and ClassCastException. In order to fix this bug, method FilterEstimation.evaluateInSet at first checks, if distinct count is not zero, and also checks if colStat.min and colStat.max are defined, and only in this case proceeds with the calculation. If at least one of the conditions is not satisfied, zero is returned. >How was this patch tested? In order to test the PR two tests were implemented: one in FilterEstimationSuite, that tests the plan with the statistics that violates the conditions mentioned above, and another one in StatisticsCollectionSuite, that test the whole process of analysis/optimisation of the query, that leads to the problems, mentioned in the first section. Author: Mykhailo Shtelma <mykhailo.shtelma@bearingpoint.com> Author: smikesh <mshtelma@gmail.com> Closes #21052 from mshtelma/filter_estimation_evaluateInSet_Bugs.
…y in MemoryBlockSuite ## What changes were proposed in this pull request? As viirya pointed out [here](#19222 (comment)), this PR explicitly frees unused off-heap memory in `MemoryBlockSuite` ## How was this patch tested? Existing UTs Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #21117 from kiszk/SPARK-10399-free-offheap.
…ndling of Project and Filter over partitioned relation ## What changes were proposed in this pull request? A followup of #20988 `PhysicalOperation` can collect Project and Filters over a certain plan and substitute the alias with the original attributes in the bottom plan. We can use it in `OptimizeMetadataOnlyQuery` rule to handle the Project and Filter over partitioned relation. ## How was this patch tested? existing test Author: Wenchen Fan <wenchen@databricks.com> Closes #21111 from cloud-fan/refactor.
…'s children ## What changes were proposed in this pull request? The existing query constraints framework has 2 steps: 1. propagate constraints bottom up. 2. use constraints to infer additional filters for better data pruning. For step 2, it mostly helps with Join, because we can connect the constraints from children to the join condition and infer powerful filters to prune the data of the join sides. e.g., the left side has constraints `a = 1`, the join condition is `left.a = right.a`, then we can infer `right.a = 1` to the right side and prune the right side a lot. However, the current logic of inferring filters from constraints for Join is pretty weak. It infers the filters from Join's constraints. Some joins like left semi/anti exclude output from right side and the right side constraints will be lost here. This PR propose to check the left and right constraints individually, expand the constraints with join condition and add filters to children of join directly, instead of adding to the join condition. This reverts #20670 , covers #20717 and #20816 This is inspired by the original PRs and the tests are all from these PRs. Thanks to the authors mgaido91 maryannxue KaiXinXiaoLei ! ## How was this patch tested? new tests Author: Wenchen Fan <wenchen@databricks.com> Closes #21083 from cloud-fan/join.
…xecution ## What changes were proposed in this pull request? This pr supported interpreted mode for `ExternalMapToCatalyst`. ## How was this patch tested? Added tests in `ObjectExpressionsSuite`. Author: Takeshi Yamamuro <yamamuro@apache.org> Closes #20980 from maropu/SPARK-23589.
…y = 0
## What changes were proposed in this pull request?
It is reported by Spark users that the deviance calculation for poisson regression does not handle y = 0. Thus, the correct model summary cannot be obtained. The user has confirmed the the issue is in
```
override def deviance(y: Double, mu: Double, weight: Double): Double =
{ 2.0 * weight * (y * math.log(y / mu) - (y - mu)) }
when y = 0.
```
The user also mentioned there are many other places he believe we should check the same thing. However, no other changes are needed, including Gamma distribution.
## How was this patch tested?
Add a comparison with R deviance calculation to the existing unit test.
Author: Teng Peng <josephtengpeng@gmail.com>
Closes #21125 from tengpeng/Spark24024GLM.
[https://issues.apache.org/jira/browse/SPARK-21168](https://issues.apache.org/jira/browse/SPARK-21168) There are no a number of other places that a client ID should be set,and I think we should use consumer.clientId in the clientId method,because the fetch request will be used by the same consumer behind. Author: liuzhaokun <liu.zhaokun@zte.com.cn> Closes #19887 from liu-zhaokun/master1205.
…treaming aggregation task ## What changes were proposed in this pull request? A structured streaming query with a streaming aggregation can throw the following error in rare cases. ``` java.lang.IllegalStateException: Cannot commit after already committed or aborted at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider.org$apache$spark$sql$execution$streaming$state$HDFSBackedStateStoreProvider$$verify(HDFSBackedStateStoreProvider.scala:643) at org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider$HDFSBackedStateStore.commit(HDFSBackedStateStoreProvider.scala:135) at org.apache.spark.sql.execution.streaming.StateStoreSaveExec$$anonfun$doExecute$3$$anon$2$$anonfun$hasNext$2.apply$mcV$sp(statefulOperators.scala:359) at org.apache.spark.sql.execution.streaming.StateStoreWriter$class.timeTakenMs(statefulOperators.scala:102) at org.apache.spark.sql.execution.streaming.StateStoreSaveExec.timeTakenMs(statefulOperators.scala:251) at org.apache.spark.sql.execution.streaming.StateStoreSaveExec$$anonfun$doExecute$3$$anon$2.hasNext(statefulOperators.scala:359) at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.processInputs(ObjectAggregationIterator.scala:188) at org.apache.spark.sql.execution.aggregate.ObjectAggregationIterator.<init>(ObjectAggregationIterator.scala:78) at org.apache.spark.sql.execution.aggregate.ObjectHashAggregateExec$$anonfun$doExecute$1$$anonfun$2.apply(ObjectHashAggregateExec.scala:114) at org.apache.spark.sql.execution.aggregate.ObjectHashAggregateExec$$anonfun$doExecute$1$$anonfun$2.apply(ObjectHashAggregateExec.scala:105) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$apply$24.apply(RDD.scala:830) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$apply$24.apply(RDD.scala:830) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:42) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:336) ``` This can happen when the following conditions are accidentally hit. - Streaming aggregation with aggregation function that is a subset of [`TypedImperativeAggregation`](https://github.com/apache/spark/blob/76b8b840ddc951ee6203f9cccd2c2b9671c1b5e8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala#L473) (for example, `collect_set`, `collect_list`, `percentile`, etc.). - Query running in `update}` mode - After the shuffle, a partition has exactly 128 records. This causes StateStore.commit to be called twice. See the [JIRA](https://issues.apache.org/jira/browse/SPARK-23004) for a more detailed explanation. The solution is to use `NextIterator` or `CompletionIterator`, each of which has a flag to prevent the "onCompletion" task from being called more than once. In this PR, I chose to implement using `NextIterator`. ## How was this patch tested? Added unit test that I have confirm will fail without the fix. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #21124 from tdas/SPARK-23004.
## What changes were proposed in this pull request? Adding PMML export to Spark ML's KMeans Model. ## How was this patch tested? New unit test for Spark ML PMML export based on the old Spark MLlib unit test. Author: Holden Karau <holden@pigscanfly.ca> Closes #20907 from holdenk/SPARK-11237-Add-PMML-Export-for-KMeans.
TaskSetManager.hasAttemptOnHost had a misleading comment. The comment said that it only checked for running tasks, but really it checked for any tasks that might have run in the past as well. This updates to line up with the implementation. Author: wuyi <ngone_5451@163.com> Closes #20998 from Ngone51/SPARK-23888.
"childOption" is for the remote connections, not for the server socket that actually listens for incoming connections. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #21132 from vanzin/SPARK-24029.2.
## What changes were proposed in this pull request? In SPARK-23375 we introduced the ability of removing `Sort` operation during query optimization if the data is already sorted. In this follow-up we remove also a `Sort` which is followed by another `Sort`: in this case the first sort is not needed and can be safely removed. The PR starts from henryr's comment: #20560 (comment). So credit should be given to him. ## How was this patch tested? added UT Author: Marco Gaido <marcogaido91@gmail.com> Closes #21072 from mgaido91/SPARK-23973.
## What changes were proposed in this pull request? Fix comment. Change `BroadcastHashJoin.broadcastFuture` to `BroadcastExchangeExec.relationFuture`: https://github.com/apache/spark/blob/d28d5732ae205771f1f443b15b10e64dcffb5ff0/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/BroadcastExchangeExec.scala#L66 ## How was this patch tested? N/A Author: seancxmao <seancxmao@gmail.com> Closes #21113 from seancxmao/SPARK-13136.
## What changes were proposed in this pull request? This PR proposes to add array_position and element_at in R side too. array_position: ```r df <- createDataFrame(cbind(model = rownames(mtcars), mtcars)) mutated <- mutate(df, v1 = create_array(df$gear, df$am, df$carb)) head(select(mutated, array_position(mutated$v1, 1))) ``` ``` array_position(v1, 1.0) 1 2 2 2 3 2 4 3 5 0 6 3 ``` element_at: ```r df <- createDataFrame(cbind(model = rownames(mtcars), mtcars)) mutated <- mutate(df, v1 = create_array(df$mpg, df$cyl, df$hp)) head(select(mutated, element_at(mutated$v1, 1))) ``` ``` element_at(v1, 1.0) 1 21.0 2 21.0 3 22.8 4 21.4 5 18.7 6 18.1 ``` ```r df <- createDataFrame(cbind(model = rownames(mtcars), mtcars)) mutated <- mutate(df, v1 = create_map(df$model, df$cyl)) head(select(mutated, element_at(mutated$v1, "Valiant"))) ``` ``` element_at(v3, Valiant) 1 NA 2 NA 3 NA 4 NA 5 NA 6 6 ``` ## How was this patch tested? Unit tests were added in `R/pkg/tests/fulltests/test_sparkSQL.R` and manually tested. Documentation was manually built and verified. Author: hyukjinkwon <gurwls223@apache.org> Closes #21130 from HyukjinKwon/sparkr_array_position_element_at.
…yst eval ## What changes were proposed in this pull request? This pr is a follow-up of #20980 and fixes code to reuse `InternalRow` for converting input keys/values in `ExternalMapToCatalyst` eval. ## How was this patch tested? Existing tests. Author: Takeshi Yamamuro <yamamuro@apache.org> Closes #21137 from maropu/SPARK-23589-FOLLOWUP.
…le the parallelism of the dynamic allocation ## What changes were proposed in this pull request? By default, the dynamic allocation will request enough executors to maximize the parallelism according to the number of tasks to process. While this minimizes the latency of the job, with small tasks this setting can waste a lot of resources due to executor allocation overhead, as some executor might not even do any work. This setting allows to set a ratio that will be used to reduce the number of target executors w.r.t. full parallelism. The number of executors computed with this setting is still fenced by `spark.dynamicAllocation.maxExecutors` and `spark.dynamicAllocation.minExecutors` ## How was this patch tested? Units tests and runs on various actual workloads on a Yarn Cluster Author: Julien Cuquemelle <j.cuquemelle@criteo.com> Closes #19881 from jcuquemelle/AddTaskPerExecutorSlot.
…features ## What changes were proposed in this pull request? - Multiple possible input types is added in validateAndTransformSchema() and computeCost() while checking column type - Add if statement in transform() to support array type as featuresCol - Add the case statement in fit() while selecting columns from dataset These changes will be applied to KMeans first, then to other clustering method ## How was this patch tested? unit test is added Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Lu WANG <lu.wang@databricks.com> Closes #21081 from ludatabricks/SPARK-23975.
## What changes were proposed in this pull request? 1. Adds a `hadoop-3.1` profile build depending on the hadoop-3.1 artifacts. 1. In the hadoop-cloud module, adds an explicit hadoop-3.1 profile which switches from explicitly pulling in cloud connectors (hadoop-openstack, hadoop-aws, hadoop-azure) to depending on the hadoop-cloudstorage POM artifact, which pulls these in, has pre-excluded things like hadoop-common, and stays up to date with new connectors (hadoop-azuredatalake, hadoop-allyun). Goal: it becomes the Hadoop projects homework of keeping this clean, and the spark project doesn't need to handle new hadoop releases adding more dependencies. 1. the hadoop-cloud/hadoop-3.1 profile also declares support for jetty-ajax and jetty-util to ensure that these jars get into the distribution jar directory when needed by unshaded libraries. 1. Increases the curator and zookeeper versions to match those in hadoop-3, fixing spark core to build in sbt with the hadoop-3 dependencies. ## How was this patch tested? * Everything this has been built and tested against both ASF Hadoop branch-3.1 and hadoop trunk. * spark-shell was used to create connectors to all the stores and verify that file IO could take place. The spark hive-1.2.1 JAR has problems here, as it's version check logic fails for Hadoop versions > 2. This can be avoided with either of * The hadoop JARs built to declare their version as Hadoop 2.11 `mvn install -DskipTests -DskipShade -Ddeclared.hadoop.version=2.11` . This is safe for local test runs, not for deployment (HDFS is very strict about cross-version deployment). * A modified version of spark hive whose version check switch statement is happy with hadoop 3. I've done both, with maven and SBT. Three issues surfaced 1. A spark-core test failure —fixed in SPARK-23787. 1. SBT only: Zookeeper not being found in spark-core. Somehow curator 2.12.0 triggers some slightly different dependency resolution logic from previous versions, and Ivy was missing zookeeper.jar entirely. This patch adds the explicit declaration for all spark profiles, setting the ZK version = 3.4.9 for hadoop-3.1 1. Marking jetty-utils as provided in spark was stopping hadoop-azure from being able to instantiate the azure wasb:// client; it was using jetty-util-ajax, which could then not find a class in jetty-util. Author: Steve Loughran <stevel@hortonworks.com> Closes #20923 from steveloughran/cloud/SPARK-23807-hadoop-31.
…metadata ## What changes were proposed in this pull request? We save ML's user-supplied params and default params as one entity in metadata. During loading the saved models, we set all the loaded params into created ML model instances as user-supplied params. It causes some problems, e.g., if we strictly disallow some params to be set at the same time, a default param can fail the param check because it is treated as user-supplied param after loading. The loaded default params should not be set as user-supplied params. We should save ML default params separately in metadata. For backward compatibility, when loading metadata, if it is a metadata file from previous Spark, we shouldn't raise error if we can't find the default param field. ## How was this patch tested? Pass existing tests and added tests. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #20633 from viirya/save-ml-default-params.
…CSV parser ## What changes were proposed in this pull request? uniVocity parser allows to specify only required column names or indexes for [parsing](https://www.univocity.com/pages/parsers-tutorial) like: ``` // Here we select only the columns by their indexes. // The parser just skips the values in other columns parserSettings.selectIndexes(4, 0, 1); CsvParser parser = new CsvParser(parserSettings); ``` In this PR, I propose to extract indexes from required schema and pass them into the CSV parser. Benchmarks show the following improvements in parsing of 1000 columns: ``` Select 100 columns out of 1000: x1.76 Select 1 column out of 1000: x2 ``` **Note**: Comparing to current implementation, the changes can return different result for malformed rows in the `DROPMALFORMED` and `FAILFAST` modes if only subset of all columns is requested. To have previous behavior, set `spark.sql.csv.parser.columnPruning.enabled` to `false`. ## How was this patch tested? It was tested by new test which selects 3 columns out of 15, by existing tests and by new benchmarks. Author: Maxim Gekk <maxim.gekk@databricks.com> Author: Maxim Gekk <max.gekk@gmail.com> Closes #21415 from MaxGekk/csv-column-pruning2.
## What changes were proposed in this pull request? SPARK-17874 introduced a new configuration to set the port where SSL services bind to. We missed to update the scaladoc and the `toString` method, though. The PR adds it in the missing places ## How was this patch tested? checked the `toString` output in the logs Author: Marco Gaido <marcogaido91@gmail.com> Closes #21429 from mgaido91/minor_ssl.
## What changes were proposed in this pull request? Currently, users are getting the following error messages on type conversions: ``` scala.MatchError: test (of class java.lang.String) ``` The message doesn't give any clues to the users where in the schema the error happened. In this PR, I would like to improve the error message like: ``` The value (test) of the type (java.lang.String) cannot be converted to struct<f1:int> ``` ## How was this patch tested? Added tests for converting of wrong values to `struct`, `map`, `array`, `string` and `decimal`. Author: Maxim Gekk <maxim.gekk@databricks.com> Closes #21410 from MaxGekk/type-conv-error.
… to configuration. ## What changes were proposed in this pull request? Spark provides four codecs: `lz4`, `lzf`, `snappy`, and `zstd`. This pr add missing shortCompressionCodecNames to configuration. ## How was this patch tested? manually tested Author: Yuming Wang <yumwang@ebay.com> Closes #21431 from wangyum/SPARK-19112.
… values ## What changes were proposed in this pull request? This PR adds several unit tests along the `cols NOT IN (subquery)` pathway. There are a scattering of tests here and there which cover this codepath, but there doesn't seem to be a unified unit test of the correctness of null-aware anti joins anywhere. I have also added a brief explanation of how this expression behaves in SubquerySuite. Lastly, I made some clarifying changes in the NOT IN pathway in RewritePredicateSubquery. ## How was this patch tested? Added unit tests! There should be no behavioral change in this PR. Author: Miles Yucht <miles@databricks.com> Closes #21425 from mgyucht/spark-24381.
…shutdown of Arrow memory allocator
## What changes were proposed in this pull request?
There is a race condition of closing Arrow VectorSchemaRoot and Allocator in the writer thread of ArrowPythonRunner.
The race results in memory leak exception when closing the allocator. This patch removes the closing routine from the TaskCompletionListener and make the writer thread responsible for cleaning up the Arrow memory.
This issue be reproduced by this test:
```
def test_memory_leak(self):
from pyspark.sql.functions import pandas_udf, col, PandasUDFType, array, lit, explode
# Have all data in a single executor thread so it can trigger the race condition easier
with self.sql_conf({'spark.sql.shuffle.partitions': 1}):
df = self.spark.range(0, 1000)
df = df.withColumn('id', array([lit(i) for i in range(0, 300)])) \
.withColumn('id', explode(col('id'))) \
.withColumn('v', array([lit(i) for i in range(0, 1000)]))
pandas_udf(df.schema, PandasUDFType.GROUPED_MAP)
def foo(pdf):
xxx
return pdf
result = df.groupby('id').apply(foo)
with QuietTest(self.sc):
with self.assertRaises(py4j.protocol.Py4JJavaError) as context:
result.count()
self.assertTrue('Memory leaked' not in str(context.exception))
```
Note: Because of the race condition, the test case cannot reproduce the issue reliably so it's not added to test cases.
## How was this patch tested?
Because of the race condition, the bug cannot be unit test easily. So far it has only happens on large amount of data. This is currently tested manually.
Author: Li Jin <ice.xelloss@gmail.com>
Closes #21397 from icexelloss/SPARK-24334-arrow-memory-leak.
…and KeyValueGroupedDataset's child ## What changes were proposed in this pull request? When we create a `RelationalGroupedDataset` or a `KeyValueGroupedDataset` we set its child to the `logicalPlan` of the `DataFrame` we need to aggregate. Since the `logicalPlan` is already analyzed, we should not analyze it again. But this happens when the new plan of the aggregate is analyzed. The current behavior in most of the cases is likely to produce no harm, but in other cases re-analyzing an analyzed plan can change it, since the analysis is not idempotent. This can cause issues like the one described in the JIRA (missing to find a cached plan). The PR adds an `AnalysisBarrier` to the `logicalPlan` which is used as child of `RelationalGroupedDataset` or a `KeyValueGroupedDataset`. ## How was this patch tested? added UT Author: Marco Gaido <marcogaido91@gmail.com> Closes #21432 from mgaido91/SPARK-24373.
## What changes were proposed in this pull request? The pandas_udf functionality was introduced in 2.3.0, but is not completely stable and still evolving. This adds a label to indicate it is still an experimental API. ## How was this patch tested? NA Author: Bryan Cutler <cutlerb@gmail.com> Closes #21435 from BryanCutler/arrow-pandas_udf-experimental-SPARK-24392.
…amePrefix ## What changes were proposed in this pull request? `Random.nextString` is good for generating random string data, but it's not proper for directory name prefix in `Utils.createDirectory(tempDir, Random.nextString(10))`. This PR uses more safe directory namePrefix. ```scala scala> scala.util.Random.nextString(10) res0: String = 馨쭔ᎰႻ穚䃈兩㻞藑並 ``` ```scala StateStoreRDDSuite: - versioning and immutability - recovering from files - usage with iterators - only gets and only puts - preferred locations using StateStoreCoordinator *** FAILED *** java.io.IOException: Failed to create a temp directory (under /.../spark/sql/core/target/tmp/StateStoreRDDSuite8712796397908632676) after 10 attempts! at org.apache.spark.util.Utils$.createDirectory(Utils.scala:295) at org.apache.spark.sql.execution.streaming.state.StateStoreRDDSuite$$anonfun$13$$anonfun$apply$6.apply(StateStoreRDDSuite.scala:152) at org.apache.spark.sql.execution.streaming.state.StateStoreRDDSuite$$anonfun$13$$anonfun$apply$6.apply(StateStoreRDDSuite.scala:149) at org.apache.spark.sql.catalyst.util.package$.quietly(package.scala:42) at org.apache.spark.sql.execution.streaming.state.StateStoreRDDSuite$$anonfun$13.apply(StateStoreRDDSuite.scala:149) at org.apache.spark.sql.execution.streaming.state.StateStoreRDDSuite$$anonfun$13.apply(StateStoreRDDSuite.scala:149) ... - distributed test *** FAILED *** java.io.IOException: Failed to create a temp directory (under /.../spark/sql/core/target/tmp/StateStoreRDDSuite8712796397908632676) after 10 attempts! at org.apache.spark.util.Utils$.createDirectory(Utils.scala:295) ``` ## How was this patch tested? Pass the existing tests.StateStoreRDDSuite: Author: Dongjoon Hyun <dongjoon@apache.org> Closes #21446 from dongjoon-hyun/SPARK-19613.
…cation ## What changes were proposed in this pull request? For some Spark applications, though they're a java program, they require not only jar dependencies, but also python dependencies. One example is Livy remote SparkContext application, this application is actually an embedded REPL for Scala/Python/R, it will not only load in jar dependencies, but also python and R deps, so we should specify not only "--jars", but also "--py-files". Currently for a Spark application, --py-files can only be worked for a pyspark application, so it will not be worked in the above case. So here propose to remove such restriction. Also we tested that "spark.submit.pyFiles" only supports quite limited scenario (client mode with local deps), so here also expand the usage of "spark.submit.pyFiles" to be alternative of --py-files. ## How was this patch tested? UT added. Author: jerryshao <sshao@hortonworks.com> Closes #21420 from jerryshao/SPARK-24377.
## What changes were proposed in this pull request? We should not stop users from calling `getActiveSession` and `getDefaultSession` in executors. To not break the existing behaviors, we should simply return None. ## How was this patch tested? N/A Author: Xiao Li <gatorsmile@gmail.com> Closes #21436 from gatorsmile/followUpSPARK-24250.
…eBlocksToBatch When blocks tried to get allocated to a batch and WAL write fails then the blocks will be removed from the received block queue. This fact simply produces data loss because the next allocation will not find the mentioned blocks in the queue. In this PR blocks will be removed from the received queue only if WAL write succeded. Additional unit test. Author: Gabor Somogyi <gabor.g.somogyi@gmail.com> Closes #21430 from gaborgsomogyi/SPARK-23991. Change-Id: I5ead84f0233f0c95e6d9f2854ac2ff6906f6b341
…d Java.
## What changes were proposed in this pull request?
Implemented **`isInCollection `** in DataFrame API for both Scala and Java, so users can do
```scala
val profileDF = Seq(
Some(1), Some(2), Some(3), Some(4),
Some(5), Some(6), Some(7), None
).toDF("profileID")
val validUsers: Seq[Any] = Seq(6, 7.toShort, 8L, "3")
val result = profileDF.withColumn("isValid", $"profileID". isInCollection(validUsers))
result.show(10)
"""
+---------+-------+
|profileID|isValid|
+---------+-------+
| 1| false|
| 2| false|
| 3| true|
| 4| false|
| 5| false|
| 6| true|
| 7| true|
| null| null|
+---------+-------+
""".stripMargin
```
## How was this patch tested?
Several unit tests are added.
Author: DB Tsai <d_tsai@apple.com>
Closes #21416 from dbtsai/optimize-set.
## What changes were proposed in this pull request? Add Data Source write benchmark. So that it would be easier to measure the writer performance. Author: Gengliang Wang <gengliang.wang@databricks.com> Closes #21409 from gengliangwang/parquetWriteBenchmark.
…ntries to SparkR
## What changes were proposed in this pull request?
The PR adds functions `arrays_overlap`, `array_repeat`, `map_entries` to SparkR.
## How was this patch tested?
Tests added into R/pkg/tests/fulltests/test_sparkSQL.R
## Examples
### arrays_overlap
```
df <- createDataFrame(list(list(list(1L, 2L), list(3L, 1L)),
list(list(1L, 2L), list(3L, 4L)),
list(list(1L, NA), list(3L, 4L))))
collect(select(df, arrays_overlap(df[[1]], df[[2]])))
```
```
arrays_overlap(_1, _2)
1 TRUE
2 FALSE
3 NA
```
### array_repeat
```
df <- createDataFrame(list(list("a", 3L), list("b", 2L)))
collect(select(df, array_repeat(df[[1]], df[[2]])))
```
```
array_repeat(_1, _2)
1 a, a, a
2 b, b
```
```
collect(select(df, array_repeat(df[[1]], 2L)))
```
```
array_repeat(_1, 2)
1 a, a
2 b, b
```
### map_entries
```
df <- createDataFrame(list(list(map = as.environment(list(x = 1, y = 2)))))
collect(select(df, map_entries(df$map)))
```
```
map_entries(map)
1 x, 1, y, 2
```
Author: Marek Novotny <mn.mikke@gmail.com>
Closes #21434 from mn-mikke/SPARK-24331.
## What changes were proposed in this pull request? Make sure that `StopIteration`s raised in users' code do not silently interrupt processing by spark, but are raised as exceptions to the users. The users' functions are wrapped in `safe_iter` (in `shuffle.py`), which re-raises `StopIteration`s as `RuntimeError`s ## How was this patch tested? Unit tests, making sure that the exceptions are indeed raised. I am not sure how to check whether a `Py4JJavaError` contains my exception, so I simply looked for the exception message in the java exception's `toString`. Can you propose a better way? ## License This is my original work, licensed in the same way as spark Author: e-dorigatti <emilio.dorigatti@gmail.com> Author: edorigatti <emilio.dorigatti@gmail.com> Closes #21383 from e-dorigatti/fix_spark_23754.
## What changes were proposed in this pull request? Upgrade SBT to 0.13.17 with Scala 2.10.7 for JDK9+ ## How was this patch tested? Existing tests Author: DB Tsai <d_tsai@apple.com> Closes #21458 from dbtsai/sbt.
…s having the same argument set
## What changes were proposed in this pull request?
This pr fixed an issue when having multiple distinct aggregations having the same argument set, e.g.,
```
scala>: paste
val df = sql(
s"""SELECT corr(DISTINCT x, y), corr(DISTINCT y, x), count(*)
| FROM (VALUES (1, 1), (2, 2), (2, 2)) t(x, y)
""".stripMargin)
java.lang.RuntimeException
You hit a query analyzer bug. Please report your query to Spark user mailing list.
```
The root cause is that `RewriteDistinctAggregates` can't detect multiple distinct aggregations if they have the same argument set. This pr modified code so that `RewriteDistinctAggregates` could count the number of aggregate expressions with `isDistinct=true`.
## How was this patch tested?
Added tests in `DataFrameAggregateSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes #21443 from maropu/SPARK-24369.
…onRunner in submit with client mode in spark-submit
## What changes were proposed in this pull request?
In client side before context initialization specifically, .py file doesn't work in client side before context initialization when the application is a Python file. See below:
```
$ cat /home/spark/tmp.py
def testtest():
return 1
```
This works:
```
$ cat app.py
import pyspark
pyspark.sql.SparkSession.builder.getOrCreate()
import tmp
print("************************%s" % tmp.testtest())
$ ./bin/spark-submit --master yarn --deploy-mode client --py-files /home/spark/tmp.py app.py
...
************************1
```
but this doesn't:
```
$ cat app.py
import pyspark
import tmp
pyspark.sql.SparkSession.builder.getOrCreate()
print("************************%s" % tmp.testtest())
$ ./bin/spark-submit --master yarn --deploy-mode client --py-files /home/spark/tmp.py app.py
Traceback (most recent call last):
File "/home/spark/spark/app.py", line 2, in <module>
import tmp
ImportError: No module named tmp
```
### How did it happen?
In client mode specifically, the paths are being added into PythonRunner as are:
https://github.com/apache/spark/blob/628c7b517969c4a7ccb26ea67ab3dd61266073ca/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala#L430
https://github.com/apache/spark/blob/628c7b517969c4a7ccb26ea67ab3dd61266073ca/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala#L49-L88
The problem here is, .py file shouldn't be added as are since `PYTHONPATH` expects a directory or an archive like zip or egg.
### How does this PR fix?
We shouldn't simply just add its parent directory because other files in the parent directory could also be added into the `PYTHONPATH` in client mode before context initialization.
Therefore, we copy .py files into a temp directory for .py files and add it to `PYTHONPATH`.
## How was this patch tested?
Unit tests are added and manually tested in both standalond and yarn client modes with submit.
Author: hyukjinkwon <gurwls223@apache.org>
Closes #21426 from HyukjinKwon/SPARK-24384.
## What changes were proposed in this pull request? Add featureSubsetStrategy in GBTClassifier and GBTRegressor. Also make GBTClassificationModel inherit from JavaClassificationModel instead of prediction model so it will have numClasses. ## How was this patch tested? Add tests in doctest Author: Huaxin Gao <huaxing@us.ibm.com> Closes #21413 from huaxingao/spark-23161.
## What changes were proposed in this pull request? The PR adds the masking function as they are described in Hive's documentation: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-DataMaskingFunctions. This means that only `string`s are accepted as parameter for the masking functions. ## How was this patch tested? added UTs Author: Marco Gaido <marcogaido91@gmail.com> Closes #21246 from mgaido91/SPARK-23901.
… equality ## What changes were proposed in this pull request? When two `In` operators are created with the same list of values, but different order, we are considering them as semantically different. This is wrong, since they have the same semantic meaning. The PR adds a canonicalization rule which orders the literals in the `In` operator so the semantic equality works properly. ## How was this patch tested? added UT Author: Marco Gaido <marcogaido91@gmail.com> Closes #21331 from mgaido91/SPARK-24276.
## What changes were proposed in this pull request? Improve the exception messages when retrieving Spark conf values to include the key name when the value is invalid. ## How was this patch tested? Unit tests for all get* operations in SparkConf that require a specific value format Author: William Sheu <william.sheu@databricks.com> Closes #21454 from PenguinToast/SPARK-24337-spark-config-errors.
…ing - PrefixSpan: Python API ## What changes were proposed in this pull request? spark.ml parity for sequential pattern mining - PrefixSpan: Python API ## How was this patch tested? doctests Author: WeichenXu <weichen.xu@databricks.com> Closes #21265 from WeichenXu123/prefix_span_py.
As discussed separately, this avoids the possibility of XSS on certain request param keys. CC vanzin Author: Sean Owen <srowen@gmail.com> Closes #21464 from srowen/XSS2.
This change takes into account all non-pending tasks when calculating the number of tasks to be shown. This also means that when the stage is pending, the task table (or, in fact, most of the data in the stage page) will not be rendered. I also fixed the label when the known number of tasks is larger than the recorded number of tasks (it was inverted). Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #21457 from vanzin/SPARK-24414.
## What changes were proposed in this pull request? This adds a new API `TaskContext.getLocalProperty(key)` to the Python TaskContext. It mirrors the Java TaskContext API of returning a string value if the key exists, or None if the key does not exist. ## How was this patch tested? New test added. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #21437 from tdas/SPARK-24397.
…ment ## What changes were proposed in this pull request? `format_number` support user specifed format as argument. For example: ```sql spark-sql> SELECT format_number(12332.123456, '##################.###'); 12332.123 ``` ## How was this patch tested? unit test Author: Yuming Wang <yumwang@ebay.com> Closes #21010 from wangyum/SPARK-23900.
## What changes were proposed in this pull request?
* Allows to refer a secret as an env var.
* Introduces new config properties in the form: spark.kubernetes{driver,executor}.secretKeyRef.ENV_NAME=name:key
ENV_NAME is case sensitive.
* Updates docs.
* Adds required unit tests.
## How was this patch tested?
Manually tested and confirmed that the secrets exist in driver's and executor's container env.
Also job finished successfully.
First created a secret with the following yaml:
```
apiVersion: v1
kind: Secret
metadata:
name: test-secret
data:
username: c3RhdnJvcwo=
password: Mzk1MjgkdmRnN0pi
-------
$ echo -n 'stavros' | base64
c3RhdnJvcw==
$ echo -n '39528$vdg7Jb' | base64
MWYyZDFlMmU2N2Rm
```
Run a job as follows:
```./bin/spark-submit \
--master k8s://http://localhost:9000 \
--deploy-mode cluster \
--name spark-pi \
--class org.apache.spark.examples.SparkPi \
--conf spark.executor.instances=1 \
--conf spark.kubernetes.container.image=skonto/spark:k8envs3 \
--conf spark.kubernetes.driver.secretKeyRef.MY_USERNAME=test-secret:username \
--conf spark.kubernetes.driver.secretKeyRef.My_password=test-secret:password \
--conf spark.kubernetes.executor.secretKeyRef.MY_USERNAME=test-secret:username \
--conf spark.kubernetes.executor.secretKeyRef.My_password=test-secret:password \
local:///opt/spark/examples/jars/spark-examples_2.11-2.4.0-SNAPSHOT.jar 10000
```
Secret loaded correctly at the driver container:
Also if I log into the exec container:
kubectl exec -it spark-pi-1526555613156-exec-1 bash
bash-4.4# env
> SPARK_EXECUTOR_MEMORY=1g
> SPARK_EXECUTOR_CORES=1
> LANG=C.UTF-8
> HOSTNAME=spark-pi-1526555613156-exec-1
> SPARK_APPLICATION_ID=spark-application-1526555618626
> **MY_USERNAME=stavros**
>
> JAVA_HOME=/usr/lib/jvm/java-1.8-openjdk
> KUBERNETES_PORT_443_TCP_PROTO=tcp
> KUBERNETES_PORT_443_TCP_ADDR=10.100.0.1
> JAVA_VERSION=8u151
> KUBERNETES_PORT=tcp://10.100.0.1:443
> PWD=/opt/spark/work-dir
> HOME=/root
> SPARK_LOCAL_DIRS=/var/data/spark-b569b0ae-b7ef-4f91-bcd5-0f55535d3564
> KUBERNETES_SERVICE_PORT_HTTPS=443
> KUBERNETES_PORT_443_TCP_PORT=443
> SPARK_HOME=/opt/spark
> SPARK_DRIVER_URL=spark://CoarseGrainedSchedulerspark-pi-1526555613156-driver-svc.default.svc:7078
> KUBERNETES_PORT_443_TCP=tcp://10.100.0.1:443
> SPARK_EXECUTOR_POD_IP=9.0.9.77
> TERM=xterm
> SPARK_EXECUTOR_ID=1
> SHLVL=1
> KUBERNETES_SERVICE_PORT=443
> SPARK_CONF_DIR=/opt/spark/conf
> PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/lib/jvm/java-1.8-openjdk/jre/bin:/usr/lib/jvm/java-1.8-openjdk/bin
> JAVA_ALPINE_VERSION=8.151.12-r0
> KUBERNETES_SERVICE_HOST=10.100.0.1
> **My_password=39528$vdg7Jb**
> _=/usr/bin/env
>
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes #21317 from skonto/k8s-fix-env-secrets.
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What changes were proposed in this pull request?
apache spark latest pull
How was this patch tested?
Please review http://spark.apache.org/contributing.html before opening a pull request.