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## What changes were proposed in this pull request? Fixes current failures in dev/lint-java ## How was this patch tested? Existing linter, tests. Author: Sean Owen <sowen@cloudera.com> Closes #18757 from srowen/LintJava.
…f master parameter in the spark-shell When I type spark-shell --help, I find that the default value description for the master parameter is missing. The user does not know what the default value is when the master parameter is not included, so we need to add the master parameter default description to the help information. [https://issues.apache.org/jira/browse/SPARK-21553](https://issues.apache.org/jira/browse/SPARK-21553) Author: davidxdh <xu.donghui@zte.com.cn> Author: Donghui Xu <xu.donghui@zte.com.cn> Closes #18755 from davidxdh/dev_0728.
…at does not create SparkContext If you run a spark job without creating the SparkSession or SparkContext, the spark job logs says it succeeded but yarn says it fails and retries 3 times. Also, since, Application Master unregisters with Resource Manager and exits successfully, it deletes the spark staging directory, so when yarn makes subsequent retries, it fails to find the staging directory and thus, the retries fail. Added a flag to check whether user has initialized SparkContext. If it is true, we let Application Master unregister with Resource Manager else, we do not let AM unregister with RM. ## How was this patch tested? Manually tested the fix. Before: <img width="1253" alt="screen shot-before" src="https://user-images.githubusercontent.com/22228190/28647214-69bf81e2-722b-11e7-9ed0-d416d2bf23be.png"> After: <img width="1319" alt="screen shot-after" src="https://user-images.githubusercontent.com/22228190/28647220-70f9eea2-722b-11e7-85c6-e56276b15614.png"> Please review http://spark.apache.org/contributing.html before opening a pull request. Author: pgandhi <pgandhi@yahoo-inc.com> Author: pgandhi999 <parthkgandhi9@gmail.com> Closes #18741 from pgandhi999/SPARK-21541.
- Author: Johan Grande <nahoj@crans.org> Closes #18738 from nahoj/patch-1.
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### What changes were proposed in this pull request? Updated kubernetes client. ### Why are the changes needed? https://issues.apache.org/jira/browse/SPARK-27812 https://issues.apache.org/jira/browse/SPARK-27927 We need this fix fabric8io/kubernetes-client#1768 that was released on version 4.6 of the client. The root cause of the problem is better explained in apache#25785 ### Does this PR introduce any user-facing change? Nope, it should be transparent to users ### How was this patch tested? This patch was tested manually using a simple pyspark job ```python from pyspark.sql import SparkSession if __name__ == '__main__': spark = SparkSession.builder.getOrCreate() ``` The expected behaviour of this "job" is that both python's and jvm's process exit automatically after the main runs. This is the case for spark versions <= 2.4. On version 2.4.3, the jvm process hangs because there's a non daemon thread running ``` "OkHttp WebSocket https://10.96.0.1/..." #121 prio=5 os_prio=0 tid=0x00007fb27c005800 nid=0x24b waiting on condition [0x00007fb300847000] "OkHttp WebSocket https://10.96.0.1/..." #117 prio=5 os_prio=0 tid=0x00007fb28c004000 nid=0x247 waiting on condition [0x00007fb300e4b000] ``` This is caused by a bug on `kubernetes-client` library, which is fixed on the version that we are upgrading to. When the mentioned job is run with this patch applied, the behaviour from spark <= 2.4.3 is restored and both processes terminate successfully Closes apache#26093 from igorcalabria/k8s-client-update. Authored-by: igor.calabria <igor.calabria@ubee.in> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
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### What changes were proposed in this pull request?
Currently `ShuffledHashJoin.outputPartitioning` inherits from `HashJoin.outputPartitioning`, which only preserves stream side partitioning (`HashJoin.scala`):
```
override def outputPartitioning: Partitioning = streamedPlan.outputPartitioning
```
This loses build side partitioning information, and causes extra shuffle if there's another join / group-by after this join.
Example:
```
withSQLConf(
SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "50",
SQLConf.SHUFFLE_PARTITIONS.key -> "2",
SQLConf.PREFER_SORTMERGEJOIN.key -> "false") {
val df1 = spark.range(10).select($"id".as("k1"))
val df2 = spark.range(30).select($"id".as("k2"))
Seq("inner", "cross").foreach(joinType => {
val plan = df1.join(df2, $"k1" === $"k2", joinType).groupBy($"k1").count()
.queryExecution.executedPlan
assert(plan.collect { case _: ShuffledHashJoinExec => true }.size === 1)
// No extra shuffle before aggregate
assert(plan.collect { case _: ShuffleExchangeExec => true }.size === 2)
})
}
```
Current physical plan (having an extra shuffle on `k1` before aggregate)
```
*(4) HashAggregate(keys=[k1#220L], functions=[count(1)], output=[k1#220L, count#235L])
+- Exchange hashpartitioning(k1#220L, 2), true, [id=#117]
+- *(3) HashAggregate(keys=[k1#220L], functions=[partial_count(1)], output=[k1#220L, count#239L])
+- *(3) Project [k1#220L]
+- ShuffledHashJoin [k1#220L], [k2#224L], Inner, BuildLeft
:- Exchange hashpartitioning(k1#220L, 2), true, [id=#109]
: +- *(1) Project [id#218L AS k1#220L]
: +- *(1) Range (0, 10, step=1, splits=2)
+- Exchange hashpartitioning(k2#224L, 2), true, [id=#111]
+- *(2) Project [id#222L AS k2#224L]
+- *(2) Range (0, 30, step=1, splits=2)
```
Ideal physical plan (no shuffle on `k1` before aggregate)
```
*(3) HashAggregate(keys=[k1#220L], functions=[count(1)], output=[k1#220L, count#235L])
+- *(3) HashAggregate(keys=[k1#220L], functions=[partial_count(1)], output=[k1#220L, count#239L])
+- *(3) Project [k1#220L]
+- ShuffledHashJoin [k1#220L], [k2#224L], Inner, BuildLeft
:- Exchange hashpartitioning(k1#220L, 2), true, [id=#107]
: +- *(1) Project [id#218L AS k1#220L]
: +- *(1) Range (0, 10, step=1, splits=2)
+- Exchange hashpartitioning(k2#224L, 2), true, [id=#109]
+- *(2) Project [id#222L AS k2#224L]
+- *(2) Range (0, 30, step=1, splits=2)
```
This can be fixed by overriding `outputPartitioning` method in `ShuffledHashJoinExec`, similar to `SortMergeJoinExec`.
In addition, also fix one typo in `HashJoin`, as that code path is shared between broadcast hash join and shuffled hash join.
### Why are the changes needed?
To avoid shuffle (for queries having multiple joins or group-by), for saving CPU and IO.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit test in `JoinSuite`.
Closes apache#29130 from c21/shj.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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Please review http://spark.apache.org/contributing.html before opening a pull request.