### What changes were proposed in this pull request?
Use `dagScheduler.taskSetFailed` to abort a barrier stage instead of throwing exception within `resourceOffers`.
### Why are the changes needed?
Any non fatal exception thrown within Spark RPC framework can be swallowed:
100fc58da5/core/src/main/scala/org/apache/spark/rpc/netty/Inbox.scala (L202-L211)
The method `TaskSchedulerImpl.resourceOffers` is also within the scope of Spark RPC framework. Thus, throw exception inside `resourceOffers` won't fail the application.
As a result, if a barrier stage fail the require check at `require(addressesWithDescs.size == taskSet.numTasks, ...)`, the barrier stage will fail the check again and again util all tasks from `TaskSetManager` dequeued. But since the barrier stage isn't really executed, the application will hang.
The issue can be reproduced by the following test:
```scala
initLocalClusterSparkContext(2)
val rdd0 = sc.parallelize(Seq(0, 1, 2, 3), 2)
val dep = new OneToOneDependency[Int](rdd0)
val rdd = new MyRDD(sc, 2, List(dep), Seq(Seq("executor_h_0"),Seq("executor_h_0")))
rdd.barrier().mapPartitions { iter =>
BarrierTaskContext.get().barrier()
iter
}.collect()
```
### Does this PR introduce any user-facing change?
Yes, application hang previously but fail-fast after this fix.
### How was this patch tested?
Added a regression test.
Closes#28257 from Ngone51/fix_barrier_abort.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This exposes the `filterByRange` method from `OrderedRDDFunctions` in the Java API (as a method of JavaPairRDD).
This is the only method of `OrderedRDDFunctions` which is not exposed in the Java API so far.
### Why are the changes needed?
This improves the consistency between the Scala and Java APIs. Calling the Scala method manually from a Java context is cumbersome as it requires passing many ClassTags.
### Does this PR introduce any user-facing change?
Yes, a new method in the Java API.
### How was this patch tested?
With unit tests. The implementation of the Scala method is already tested independently and it was not touched in this PR.
Suggesting srowen as a reviewer.
Closes#28293 from wetneb/SPARK-31518.
Authored-by: Antonin Delpeuch <antonin@delpeuch.eu>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
As part of the Stage level scheduling features, add the Python api's to set resource profiles.
This also adds the functionality to properly apply the pyspark memory configuration when specified in the ResourceProfile. The pyspark memory configuration is being passed in the task local properties. This was an easy way to get it to the PythonRunner that needs it. I modeled this off how the barrier task scheduling is passing the addresses. As part of this I added in the JavaRDD api's because those are needed by python.
### Why are the changes needed?
python api for this feature
### Does this PR introduce any user-facing change?
Yes adds the java and python apis for user to specify a ResourceProfile to use stage level scheduling.
### How was this patch tested?
unit tests and manually tested on yarn. Tests also run to verify it errors properly on standalone and local mode where its not yet supported.
Closes#28085 from tgravescs/SPARK-29641-pr-base.
Lead-authored-by: Thomas Graves <tgraves@nvidia.com>
Co-authored-by: Thomas Graves <tgraves@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Remove the requirement to launch a task in order to reset locality wait timer.
### Why are the changes needed?
Recently https://github.com/apache/spark/pull/27207 was merged, but contained a bug which leads to undesirable behavior.
The crux of the issue is that single resource offers couldn't reset the timer, if there had been a previous reject followed by an allResourceOffer with no available resources.
This lead to a problem where once locality level reached ANY, single resource offers are all accepted, leading allResourceOffers to be left with no resources to utilize (hence no task being launched on an all resource offer -> no timer reset). The task manager would be stuck in ANY locality level.
Noting down here the downsides of using below reset conditions, in case we want to follow up.
As this is quite complex, I could easily be missing something, so please comment/respond if you have more bad behavior scenarios or find something wrong here:
The format is:
> **Reset condition**
> - the unwanted side effect
> - the cause/use case
Below references to locality increase/decrease mean:
```
PROCESS_LOCAL, NODE_LOCAL ... .. ANY
------ locality decrease --->
<----- locality increase -----
```
**Task launch:**
- locality decrease:
- Blacklisting, FAIR/FIFO scheduling, or task resource requirements can minimize tasks launched
- locality increase:
- single task launch decreases locality despite many tasks remaining
**No delay schedule reject since last allFreeResource offer**
- locality decrease:
- locality wait less than allFreeResource offer frequency, which occurs at least 1 per second
- locality increase:
- single resource (or none) not rejected despite many tasks remaining (other lower priority tasks utilizing resources)
**Current impl - No delay schedule reject since last (allFreeResource offer + task launch)**
- locality decrease:
- all from above
- locality increase:
- single resource accepted and task launched despite many tasks remaining
The current impl is an improvement on the legacy (task launch) in that unintended locality decrease case is similar and the unintended locality increase case only occurs when the cluster is fully utilized.
For the locality increase cases, perhaps a config which specifies a certain % of tasks in a taskset to finish before resetting locality levels would be helpful.
**If** that was considered a good approach then perhaps removing the task launch as a requirement would eliminate most of downsides listed above.
Lemme know if you have more ideas for eliminating locality increase downside of **No delay schedule reject since last allFreeResource offer**
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
TaskSchedulerImplSuite
Also manually tested similar to how I tested in https://github.com/apache/spark/pull/27207 using [this simple app](https://github.com/bmarcott/spark-test-apps/blob/master/src/main/scala/TestLocalityWait.scala).
With the new changes, given locality wait of 10s the behavior is generally:
10 seconds of locality being respected, followed by a single full utilization of resources using ANY locality level, followed by 10 seconds of locality being respected, and so on
If the legacy flag is enabled (spark.locality.wait.legacyResetOnTaskLaunch=true), the behavior is only scheduling PROCESS_LOCAL tasks (only utilizing a single executor)
cloud-fan
tgravescs
Closes#28188 from bmarcott/nmarcott-locality-fix.
Authored-by: Nicholas Marcott <481161+bmarcott@users.noreply.github.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
As suggested by https://github.com/apache/spark/pull/28255#discussion_r412619438, this patch proposes to use taskAttemptId in checkpoint filename, instead of stageAttemptNumber + attemptNumber.
### Why are the changes needed?
To simplify checkpoint simplified and unique.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#28289 from viirya/SPARK-31484-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR increases the thread safety of the `BytesToBytesMap`:
- It makes the `iterator()` and `destructiveIterator()` methods used their own `Location` object. This used to be shared, and this was causing issues when the map was being iterated over in two threads by two different iterators.
- Removes the `safeIterator()` function. This is not needed anymore.
- Improves the documentation of a couple of methods w.r.t. thread-safety.
### Why are the changes needed?
It is unexpected an iterator shares the object it is returning with all other iterators. This is a violation of the iterator contract, and it causes issues with iterators over a map that are consumed in different threads.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#28286 from hvanhovell/SPARK-31511.
Authored-by: herman <herman@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
askAbortable method throw TimeoutException while it does no complete in time. Currently, the error message contains null as remoteAddr when receiver is in client mode.
This change is to print out correct rpcAddress instead of null in the error message.
### Why are the changes needed?
It provides the address of an endpoint which does not reply in time. It helps users to find slow executors when timeout happens.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Add a unit test.
Closes#28002 from Huang-yi-3456/SPARK-31233-enhance-rpctimeoutexception-log.
Authored-by: Huang-Yi <huang.yi.3456@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Rewrite the periodically check logic of `abortableRpcFuture` to make sure that barrier task would always return either desired messages or expected exception.
This PR also simplify a bit around `AbortableRpcFuture`.
### Why are the changes needed?
Currently, the periodically check logic of `abortableRpcFuture` is done by following:
```scala
...
var messages: Array[String] = null
while (!abortableRpcFuture.toFuture.isCompleted) {
messages = ThreadUtils.awaitResult(abortableRpcFuture.toFuture, 1.second)
...
}
return messages
```
It's possible that `abortableRpcFuture` complete before next invocation on `messages = ...`. In this case, the task may return null messages or execute successfully while it should throw exception(e.g. `SparkException` from `BarrierCoordinator`).
And here's a flaky test which caused by this bug:
```
[info] BarrierTaskContextSuite:
[info] - share messages with allGather() call *** FAILED *** (18 seconds, 705 milliseconds)
[info] org.apache.spark.SparkException: Job aborted due to stage failure: Could not recover from a failed barrier ResultStage. Most recent failure reason: Stage failed because barrier task ResultTask(0, 2) finished unsuccessfully.
[info] java.lang.NullPointerException
[info] at scala.collection.mutable.ArrayOps$ofRef$.length$extension(ArrayOps.scala:204)
[info] at scala.collection.mutable.ArrayOps$ofRef.length(ArrayOps.scala:204)
[info] at scala.collection.IndexedSeqOptimized.toList(IndexedSeqOptimized.scala:285)
[info] at scala.collection.IndexedSeqOptimized.toList$(IndexedSeqOptimized.scala:284)
[info] at scala.collection.mutable.ArrayOps$ofRef.toList(ArrayOps.scala:198)
[info] at org.apache.spark.scheduler.BarrierTaskContextSuite.$anonfun$new$4(BarrierTaskContextSuite.scala:68)
...
```
The test exception can be reproduced by changing the line `messages = ...` to the following:
```scala
messages = ThreadUtils.awaitResult(abortableRpcFuture.toFuture, 10.micros)
Thread.sleep(5000)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually test and update some unit tests.
Closes#28245 from Ngone51/fix_barrier.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`spark.driver.cores` configuration is used to set the amount of parallelism in kubernetes cluster mode drivers. Previously the amount of parallelism in the drivers were the number of cores in the host when running on JDK 8u120 or older, or the maximum of driver containers resource requests and limits when running on [JDK 8u121 or newer](https://bugs.openjdk.java.net/browse/JDK-8173345). This will enable users to specify `spark.driver.cores` to set parallelism, and specify `spark.kubernetes.driver.requests.cores` to limit the resource requests of the driver container, effectively decoupling the two
### Why are the changes needed?
Drivers submitted in kubernetes cluster mode set the parallelism of various components like `RpcEnv`, `MemoryManager`, `BlockManager` from inferring the number of available cores by calling `Runtime.getRuntime().availableProcessors()`. By using this, spark applications running on JDK 8u120 or older incorrectly get the total number of cores in the host, [ignoring the cgroup limits set by kubernetes](https://bugs.openjdk.java.net/browse/JDK-6515172). JDK 8u121 and newer runtimes do not have this problem.
Orthogonal to this, it is currently not possible to decouple resource limits on the driver container with the amount of parallelism of the various network and memory components listed above.
### Does this PR introduce any user-facing change?
Yes. Previously the amount of parallelism in kubernetes cluster mode submitted drivers were the number of cores in the host when running on JDK 8u120 or older, or the maximum of driver containers resource requests and limits when running on JDK 8u121 or newer. Now the value of `spark.driver.cores` is used.
### How was this patch tested?
happy to add tests if my proposal looks reasonable
Closes#27695 from onursatici/os/decouple-requests-and-parallelism.
Authored-by: Onur Satici <onursatici@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
To avoid file already existing exception when creating checkpoint file, this PR proposes to add stage attempt number to temporary checkpoint file.
### Why are the changes needed?
On our production clusters, we have seen checkpointing failure. The failed stage can possibly leave partial written checkpoint file, the task of retried stage to write checkpoint file could fail due to`FileAlreadyExistsException` when creating the same file, like
```
org.apache.hadoop.fs.FileAlreadyExistsException: /path_to_checkpoint/rdd-114/.part-03154-attempt-0 for client xxx.xxx.xxx.xxx already exists
org.apache.hadoop.hdfs.server.namenode.FSDirWriteFileOp.startFile(FSDirWriteFileOp.java:359)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.startFileInt(FSNamesystem.java:2353)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.startFile(FSNamesystem.java:2273)
at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.create(NameNodeRpcServer.java:728)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.create(ClientNamenodeProtocolServerSideTranslatorPB.java:413)
at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:447)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:989)
at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:851)
at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:794)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1836)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2490)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.hadoop.ipc.RemoteException.instantiateException(RemoteException.java:121)
at org.apache.hadoop.ipc.RemoteException.unwrapRemoteException(RemoteException.java:88)
at org.apache.hadoop.hdfs.DFSOutputStream.newStreamForCreate(DFSOutputStream.java:270)
at org.apache.hadoop.hdfs.DFSClient.create(DFSClient.java:1263)
at org.apache.hadoop.hdfs.DFSClient.create(DFSClient.java:1205)
at org.apache.hadoop.hdfs.DistributedFileSystem$8.doCall(DistributedFileSystem.java:473)
at org.apache.hadoop.hdfs.DistributedFileSystem$8.doCall(DistributedFileSystem.java:470)
at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
at org.apache.hadoop.hdfs.DistributedFileSystem.create(DistributedFileSystem.java:470)
at org.apache.hadoop.hdfs.DistributedFileSystem.create(DistributedFileSystem.java:411)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:929)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:910)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:872)
at org.apache.spark.rdd.ReliableCheckpointRDD$.writePartitionToCheckpointFile(ReliableCheckpointRDD.scala:204)
```
### Does this PR introduce any user-facing change?
Yes. Users won't see checkpoint file already existing exception after this PR.
### How was this patch tested?
Add unit test.
Closes#28255 from viirya/delete-temp-checkpoint.
Authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
I checked all the config of Spark again. find some new commit not add version information.
**Test.scala**
Item name | Since version | JIRA ID | Commit ID | Note
-- | -- | -- | -- | --
spark.testing.skipValidateCores | 3.1.0 | SPARK-29154 | 474b1bb5c2bce2f83c4dd8e19b9b7c5b3aebd6c4#diff-8b4ea8f3b0cc1e7ce7e943de1abbb165 |
**SQL**
Item name | Since version | JIRA ID | Commit ID | Note
-- | -- | -- | -- | --
spark.sql.legacy.integerGroupingId | 3.1.0 | SPARK-30279 | 71c73d58f6e88d2558ed2e696897767d93bac60f#diff-9a6b543db706f1a90f790783d6930a13 |
The two config only exists in branch master.
### Why are the changes needed?
Supplement version information.
### Does this PR introduce any user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#28233 from beliefer/sql-conf-version-legacy-integerGroupingId.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Make `UnsafeKVExternalSorter` / `VariableLengthRowBasedKeyValueBatch ` also respect `UnsafeAlignedOffset` when reading the record and update some out of date comemnts.
### Why are the changes needed?
Since `BytesToBytesMap` respects `UnsafeAlignedOffset` when writing the record, `UnsafeKVExternalSorter` should also respect `UnsafeAlignedOffset` when reading the record from `BytesToBytesMap` otherwise it will causes data correctness issue.
Unlike `UnsafeKVExternalSorter` may reading records from `BytesToBytesMap`, `VariableLengthRowBasedKeyValueBatch` writes and reads records by itself. Thus, similar to #22053 and [comment](https://github.com/apache/spark/pull/22053#issuecomment-411975239) there, fix for `VariableLengthRowBasedKeyValueBatch` more likely an improvement for the support of SPARC platform.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually tested `HashAggregationQueryWithControlledFallbackSuite` with `UAO_SIZE=8` to simulate SPARC platform. And tests only pass with this fix.
Closes#28195 from Ngone51/fix_uao.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Combine `BarrierRequestToSync` and `AllGatherRequestToSync` into `RequestToSync`, which is distinguished by `RequestMethod` type.
2. Remove unnecessary Json serialization/deserialization
3. Clean up some codes to make runBarrier() and `BarrierCoordinator` more general
4. Remove unused imports.
### Why are the changes needed?
To make codes simpler for better maintain in the future.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This is pure code refactor, so should be covered by existed tests.
Closes#28117 from Ngone51/refactor_barrier.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
This PR makes each id attribute for page navigations in a page unique.
`PagedTable#pageNavigation` returns HTML elements representing a page navigation for a paged table.
In the current implementation, the method generates an id and it's used for id attribute for a set of elements for the page navigation.
But some pages have two page navigations so there are two set of elements where corresponding elements have the same id.
For example, there are two `form-completedJob-table-page` id in JobsPage.
### Why are the changes needed?
Each id attribute should be unique in a page.
The following is a screenshot of warning messages shown with Chrome when I visit JobsPage (Firefox doesn't show in my environment).
<img width="1440" alt="warning-jobspage" src="https://user-images.githubusercontent.com/4736016/79261523-f3fa9280-7eca-11ea-861d-d54f04f1b0bc.png">
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
I added a test case for `pageNavigation` extended.
I also manually tested that there were no warning messages for the uniqueness in JobsPage and JobPage.
Closes#28217 from sarutak/unique-form-id.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
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### What changes were proposed in this pull request?
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This change explicitly set locale of timeline view to 'en' to be the same appearance as before upgrading vis-timeline.
### Why are the changes needed?
<!--
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1. If you propose a new API, clarify the use case for a new API.
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We upgraded vis-timeline in #28192 and the upgraded version is different from before we used in the notation of dates.
The notation seems to be dependent on locale. The following is appearance in my Japanese environment.
<img width="557" alt="locale-changed" src="https://user-images.githubusercontent.com/4736016/79265314-de886700-7ed0-11ea-8641-fa76b993c0d9.png">
Although the notation is in Japanese, the default format is a little bit unnatural (e.g. 4月9日 05:39 is natural rather than 9 四月 05:39).
I found we can get the same appearance as before by explicitly set locale to 'en'.
### Does this PR introduce any user-facing change?
<!--
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.
If no, write 'No'.
-->
No.
### How was this patch tested?
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I visited JobsPage, JobPage and StagePage and confirm that timeline view shows dates with 'en' locale.
<img width="735" alt="fix-date-appearance" src="https://user-images.githubusercontent.com/4736016/79267107-8bfc7a00-7ed3-11ea-8a25-f6681d04a83c.png">
NOTE: #28192 will be backported to branch-2.4 and branch-3.0 so this PR should be follow #28214 and #28213 .
Closes#28218 from sarutak/fix-locale-issue.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
### What changes were proposed in this pull request?
Upgrade vis.js to fix an infinite re-drawing issue.
As reported here, old releases of vis.js have that issue.
Fortunately, the latest version seems to resolve the issue.
With the latest release of vis.js, there are some performance issues with the original `timeline-view.js` and `timeline-view.css` so I also changed them.
### Why are the changes needed?
For better UX.
### Does this PR introduce any user-facing change?
No. Appearance and functionalities are not changed.
### How was this patch tested?
I confirmed infinite redrawing doesn't happen with a JobPage which I had reproduced the issue.
With the original version of vis.js, I reproduced the issue with the following conditions.
* Use history server and load core/src/test/resources/spark-events.
* Visit the JobPage for job2 in application_1553914137147_0018.
* Zoom out to 80% on Safari / Chrome / Firefox.
Maybe, it depends on OS and the version of browsers.
Closes#28192 from sarutak/upgrade-visjs.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
Show submitted time and duration of a job in its details page
### Why are the changes needed?
When we check job details from the SQL execution page, it will be more convenient if we can get the submission time and duration from the job page, instead of finding the info from job list page.
### Does this PR introduce any user-facing change?
Yes. After changes, the job details page shows the submitted time and duration.
### How was this patch tested?
Manual check
Closes#28179 from gengliangwang/addSubmittedTimeAndDuration.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
This PR (SPARK-31422) aims to return empty result in order to avoid `NullPointerException` at `getStorageStatus` and `getMemoryStatus` which happens after `BlockManagerMaster` stops. The empty result is consistent with the current status of `SparkContext` because `BlockManager` and `BlockManagerMaster` is already stopped.
### Why are the changes needed?
In `SparkEnv.stop`, the following stop sequence is used and `metricsSystem.stop` invokes `sink.stop`.
```
blockManager.master.stop()
metricsSystem.stop() --> sinks.foreach(_.stop)
```
However, some sink can invoke `BlockManagerSource` and ends up with `NullPointerException` because `BlockManagerMaster` is already stopped and `driverEndpoint` became `null`.
```
java.lang.NullPointerException
at org.apache.spark.storage.BlockManagerMaster.getStorageStatus(BlockManagerMaster.scala:170)
at org.apache.spark.storage.BlockManagerSource$$anonfun$10.apply(BlockManagerSource.scala:63)
at org.apache.spark.storage.BlockManagerSource$$anonfun$10.apply(BlockManagerSource.scala:63)
at org.apache.spark.storage.BlockManagerSource$$anon$1.getValue(BlockManagerSource.scala:31)
at org.apache.spark.storage.BlockManagerSource$$anon$1.getValue(BlockManagerSource.scala:30)
```
Since `SparkContext` registers and forgets `BlockManagerSource` without deregistering, we had better avoid `NullPointerException` inside `BlockManagerMaster` preventively.
```scala
_env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager))
```
### Does this PR introduce any user-facing change?
Yes. This will remove NPE for the users who uses `BlockManagerSource`.
### How was this patch tested?
Pass the Jenkins with the newly added test cases.
Closes#28187 from dongjoon-hyun/SPARK-31422.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This fixes one UT failure.
```
[info] - extra resources from executor *** FAILED *** (218 milliseconds)
[info] org.mockito.exceptions.misusing.InvalidUseOfMatchersException: Invalid use of argument matchers!
[info] 0 matchers expected, 1 recorded:
```
### Why are the changes needed?
The original PR was merged with an outdated Jenkins result (7 days before the merging).
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass the Jenkins or manually do the following.
```
$ build/sbt "core/testOnly *.CoarseGrainedSchedulerBackendSuite"
```
Closes#28174 from dongjoon-hyun/SPARK-18886.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
[Delay scheduling](http://elmeleegy.com/khaled/papers/delay_scheduling.pdf) is an optimization that sacrifices fairness for data locality in order to improve cluster and workload throughput.
One useful definition of "delay" here is how much time has passed since the TaskSet was using its fair share of resources.
However it is impractical to calculate this delay, as it would require running simulations assuming no delay scheduling. Tasks would be run in different orders with different run times.
Currently the heuristic used to estimate this delay is the time since a task was last launched for a TaskSet. The problem is that it essentially does not account for resource utilization, potentially leaving the cluster heavily underutilized.
This PR modifies the heuristic in an attempt to move closer to the useful definition of delay above.
The newly proposed delay is the time since a TasksSet last launched a task **and** did not reject any resources due to delay scheduling when offered its "fair share".
See the last comments of #26696 for more discussion.
### Why are the changes needed?
cluster can become heavily underutilized as described in [SPARK-18886](https://issues.apache.org/jira/browse/SPARK-18886?jql=project%20%3D%20SPARK%20AND%20text%20~%20delay)
### How was this patch tested?
TaskSchedulerImplSuite
cloud-fan
tgravescs
squito
Closes#27207 from bmarcott/nmarcott-fulfill-slots-2.
Authored-by: Nicholas Marcott <481161+bmarcott@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR (SPARK-31379) adds one line `when(ts.resourceOffers(any[IndexedSeq[WorkerOffer]])).thenReturn(Seq.empty)` to avoid allocating resources.
### Why are the changes needed?
The test is flaky and here's part of error stack:
```
sbt.ForkMain$ForkError: org.scalatest.exceptions.TestFailedDueToTimeoutException:
The code passed to eventually never returned normally. Attempted 325 times over 5.01070979 seconds.
Last failure message: ArrayBuffer("1", "3") did not equal Array("0", "1", "3").
...
org.apache.spark.scheduler.CoarseGrainedSchedulerBackendSuite.eventually(CoarseGrainedSchedulerBackendSuite.scala:45)
```
You can check [here](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/120786/testReport/org.apache.spark.scheduler/CoarseGrainedSchedulerBackendSuite/extra_resources_from_executor/) for details.
And it is flaky because: after sending `StatusUpdate` to `CoarseGrainedSchedulerBackend`, `CoarseGrainedSchedulerBackend` will call `makeOffer` immediately once releasing the resources. So, it's possible that `availableAddrs` has allocated again before we assert `execResources(GPU).availableAddrs.sorted === Array("0", "1", "3")`.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
The issue can be stably reproduced by inserting `Thread.sleep(3000)` after the line of sending `StatusUpdate`. After applying this fix, the issue is gone.
Closes#28145 from Ngone51/fix_flaky.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
I found a bug in the stage level scheduling dynamic allocation code when you have a non default profile and it has an initial number of executors the same as what the number of executors needed for the first job, then we don't properly request the executors. This causes a hang.
The issue is that when a new stage is added and the initial number of executors is set, we set the target to be the initial number. Unfortunately that makes the code in the update and sync function think it has already requested that number. So to fix this, when there is an initial number we just go ahead and request executors at that point. This is basically what happens on startup to handle the case with the default profile.
### Why are the changes needed?
bug
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
unit test and manually test on yarn cluster. Went though multiple scenarios initial numbers, minimum number and number executor required by the first stage.
Closes#28146 from tgravescs/SPARK-31378.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add a cleanShuffleDependencies as an experimental developer feature to allow folks to clean up shuffle files more aggressively than we currently do.
### Why are the changes needed?
Dynamic scaling on Kubernetes (introduced in Spark 3) depends on only shutting down executors without shuffle files. However Spark does not aggressively clean up shuffle files (see SPARK-5836) and instead depends on JVM GC on the driver to trigger deletes. We already have a mechanism to explicitly clean up shuffle files from the ALS algorithm where we create a lot of quickly orphaned shuffle files. We should expose this as an advanced developer feature to enable people to better clean-up shuffle files improving dynamic scaling of their jobs on Kubernetes.
### Does this PR introduce any user-facing change?
This adds a new experimental API.
### How was this patch tested?
ALS already used a mechanism like this, re-targets the ALS code to the new interface, tested with existing ALS tests.
Closes#28038 from holdenk/SPARK-31208-allow-users-to-cleanup-shuffle-files.
Authored-by: Holden Karau <hkarau@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This change (SPARK-31360) fixes a hung-up issue in StagePage.
StagePage will be hung-up with following operations.
1. Run a job with shuffle.
`scala> sc.parallelize(1 to 10).map(x => (x, x)).reduceByKey(_ + _).collect`
2. Visit StagePage for the stage writing shuffle data and check `Shuffle Write Time`.
<img width="401" alt="check-shuffle-write-time" src="https://user-images.githubusercontent.com/4736016/78557730-4513e200-784c-11ea-8b42-a5053b9489a5.png">
3. Run a job with no shuffle.
`scala> sc.parallelize(1 to 10).collect`
4. Visit StagePage for the last stage.
<img width="956" alt="hungup" src="https://user-images.githubusercontent.com/4736016/78557746-4f35e080-784c-11ea-83e8-5db745b88535.png">
This issue is caused by following reason.
In stagepage.js, an array `optionalColumns` has indices for columns for optional metrics.
If a stage doesn't perform shuffle read or write, the corresponding indices are removed from the array.
StagePage doesn't try to create column for such metrics, even if the state of corresponding optional metrics are preserved as "visible".
But, if a stage doesn't perform both shuffle read and write, the index for `Shuffle Write Time` isn't removed.
In that case, StagePage tries to create a column for `Shuffle Write Time` even though there are no metrics for shuffle write, leading hungup.
### Why are the changes needed?
This is a bug.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
I tested with operations I explained above and confirmed that StagePage won't be hung-up.
Closes#28136 from sarutak/fix-ui-hungup.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Mark `RDD.resourceProfile` as `transient`.
### Why are the changes needed?
`RDD.resourceProfile` should only be used at driver side.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass Jenkins.
Closes#28108 from Ngone51/spark_29154_followup.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
In `CoarseGrainedSchedulerBackendSuite.RegisterExecutor`, change it to post `SparkListenerExecutorAdded` before `context.reply(true)`.
### Why are the changes needed?
To fix flaky `CoarseGrainedSchedulerBackendSuite.custom log url for Spark UI is applied`.
In this test, though we use `askSync` to register executor but `askSync` could be finished before we posting the 3rd `SparkListenerExecutorAdded` event to the listener bus due to the reason that `context.reply(true)` comes before `listenerBus.post`.
The error can be reproduced if you:
- loop it for 500 times in one turn
- or, insert a `Thread.sleep(1000)` between `post` and `reply`.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Loop the flaky tests for 1000 times without any error.
Closes#28100 from Ngone51/fix_spark_31249.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
For the stage level scheduling feature, add the ability to optionally merged resource profiles if they were specified on multiple RDD within a stage. There is a config to enable this feature, its off by default (spark.scheduler.resourceProfile.mergeConflicts). When the config is set to true, Spark will merge the profiles selecting the max value of each resource (cores, memory, gpu, etc). further documentation will be added with SPARK-30322.
This also added in the ability to check if an equivalent resource profile already exists. This is so that if a user is running stages and combining the same profiles over and over again we don't get an explosion in the number of profiles.
### Why are the changes needed?
To allow users to specify resource on multiple RDD and not worry as much about if they go into the same stage and fail.
### Does this PR introduce any user-facing change?
Yes, when the config is turned on it now merges the profiles instead of errorring out.
### How was this patch tested?
Unit tests
Closes#28053 from tgravescs/SPARK-29153.
Lead-authored-by: Thomas Graves <tgraves@apache.org>
Co-authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
For TransportFactory, the requests sent to the same address share a clientPool.
Specially, when the io.numConnectionPerPeer is 1, these requests would share a same client.
When this address is unreachable, the createClient operation would be still timeout.
And these requests would block each other during createClient, because there is a lock for this shared client.
It would cost connectionNum \* connectionTimeOut \* maxRetry to retry, and then fail the task.
It fact, it is expected that this task could fail in connectionTimeOut * maxRetry.
In this PR, I set a fastFail time window for the clientPool, if the last connection failed in this time window, the new connection would fast fail.
## Why are the changes needed?
It can save time for some cases.
## Does this PR introduce any user-facing change?
No.
## How was this patch tested?
Existing UT.
Closes#27943 from turboFei/SPARK-31179-fast-fail-connection.
Authored-by: turbofei <fwang12@ebay.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
In `TaskSchedulerImpl`, Spark will upper schedule mode `SchedulingMode.withName(schedulingModeConf.toUpperCase(Locale.ROOT))`.
But at other place, Spark does not. Such as [AllJobsPage](5945d46c11/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala (L304)).
We should have the same behavior and uppercase schema mode string at config.
### Why are the changes needed?
Before this pr, it's ok to set `spark.scheduler.mode=fair` logically.
But Spark will throw warn log
```
java.util.NoSuchElementException: No value found for 'fair'
at scala.Enumeration.withName(Enumeration.scala:124)
at org.apache.spark.ui.jobs.AllJobsPage$$anonfun$22.apply(AllJobsPage.scala:314)
at org.apache.spark.ui.jobs.AllJobsPage$$anonfun$22.apply(AllJobsPage.scala:314)
at scala.Option.map(Option.scala:146)
at org.apache.spark.ui.jobs.AllJobsPage.render(AllJobsPage.scala:314)
at org.apache.spark.ui.WebUI$$anonfun$2.apply(WebUI.scala:90)
at org.apache.spark.ui.WebUI$$anonfun$2.apply(WebUI.scala:90)
at org.apache.spark.ui.JettyUtils$$anon$3.doGet(JettyUtils.scala:90)
```
### Does this PR introduce any user-facing change?
Almost no.
### How was this patch tested?
Exists Test.
Closes#28049 from ulysses-you/SPARK-31285.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR (SPARK-31308) proposed to add python dependencies even it is not Python applications.
### Why are the changes needed?
For now, we add `pyFiles` argument to `files` argument only for Python applications, in SparkSubmit. Like the reason in #21420, "for some Spark applications, though they're a java program, they require not only jar dependencies, but also python dependencies.", we need to add `pyFiles` to `files` even it is not Python applications.
### Does this PR introduce any user-facing change?
Yes. After this change, for non-PySpark applications, the Python files specified by `pyFiles` are also added to `files` like PySpark applications.
### How was this patch tested?
Manually test on jupyter notebook or do `spark-submit` with `--verbose`.
```
Spark config:
...
(spark.files,file:/Users/dongjoon/PRS/SPARK-PR-28077/a.py)
(spark.submit.deployMode,client)
(spark.master,local[*])
```
Closes#28077 from viirya/pyfile.
Lead-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Co-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR (SPARK-31248) uses `ManualClock` to disable `ExecutorAllocationManager.schedule()` in order to avoid unexpected update of target executors.
### Why are the changes needed?
`ExecutorAllocationManager` will call `schedule` periodically, which may update target executors before we checking 496f6ac860/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala (L864)
And fail the check:
```
sbt.ForkMain$ForkError: org.scalatest.exceptions.TestFailedException: 12 did not equal 8
at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:530)
at org.scalatest.Assertions.newAssertionFailedException$(Assertions.scala:529)
at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1560)
at org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:503)
at org.apache.spark.ExecutorAllocationManagerSuite.$anonfun$new$51(ExecutorAllocationManagerSuite.scala:864)
at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:186)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:151)
at org.scalatest.FunSuiteLike.invokeWithFixture$1(FunSuiteLike.scala:184)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Update test.
Closes#28084 from Ngone51/spark_31248.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This reverts commit 8cf76f8d61. #25962
### Why are the changes needed?
In SPARK-29285, we change to create shuffle temporary eagerly. This is helpful for not to fail the entire task in the scenario of occasional disk failure. But for the applications that many tasks don't actually create shuffle files, it caused overhead. See the below benchmark:
Env: Spark local-cluster[2, 4, 19968], each queries run 5 round, each round 5 times.
Data: TPC-DS scale=99 generate by spark-tpcds-datagen
Results:
| | Base | Revert |
|-----|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|
| Q20 | Vector(4.096865667, 2.76231748, 2.722007606, 2.514433591, 2.400373579) Median 2.722007606 | Vector(3.763185446, 2.586498463, 2.593472842, 2.320522846, 2.224627274) Median 2.586498463 |
| Q33 | Vector(5.872176321, 4.854397586, 4.568787136, 4.393378146, 4.423996818) Median 4.568787136 | Vector(5.38746785, 4.361236877, 4.082311276, 3.867206824, 3.783188024) Median 4.082311276 |
| Q52 | Vector(3.978870321, 3.225437871, 3.282411608, 2.869674887, 2.644490664) Median 3.225437871 | Vector(4.000381522, 3.196025108, 3.248787619, 2.767444508, 2.606163423) Median 3.196025108 |
| Q56 | Vector(6.238045133, 4.820535173, 4.609965579, 4.313509894, 4.221256227) Median 4.609965579 | Vector(6.241611339, 4.225592467, 4.195202502, 3.757085755, 3.657525982) Median 4.195202502 |
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#28072 from xuanyuanking/SPARK-29285-revert.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR replaces the method calls of `toSet.toSeq` with `distinct`.
### Why are the changes needed?
`toSet.toSeq` is intended to make its elements unique but a bit verbose. Using `distinct` instead is easier to understand and improves readability.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Tested with the existing unit tests and found no problem.
Closes#28062 from sekikn/SPARK-31292.
Authored-by: Kengo Seki <sekikn@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Fix incorrect log of `cureRequestSize`.
### Why are the changes needed?
In batch mode, `curRequestSize` can be the total size of several block groups. And each group should have its own request size instead of using the total size.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
It's only affect log.
Closes#28028 from Ngone51/fix_curRequestSize.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This is the core scheduler changes to support Stage level scheduling.
The main changes here include modification to the DAGScheduler to look at the ResourceProfiles associated with an RDD and have those applied inside the scheduler.
Currently if multiple RDD's in a stage have conflicting ResourceProfiles we throw an error. logic to allow this will happen in SPARK-29153. I added the interfaces to RDD to add and get the REsourceProfile so that I could add unit tests for the scheduler. These are marked as private for now until we finish the feature and will be exposed in SPARK-29150. If you think this is confusing I can remove those and remove the tests and add them back later.
I modified the task scheduler to make sure to only schedule on executor that exactly match the resource profile. It will then check those executors to make sure the current resources meet the task needs before assigning it. In here I changed the way we do the custom resource assignment.
Other changes here include having the cpus per task passed around so that we can properly account for them. Previously we just used the one global config, but now it can change based on the ResourceProfile.
I removed the exceptions that require the cores to be the limiting resource. With this change all the places I found that used executor cores /task cpus as slots has been updated to use the ResourceProfile logic and look to see what resource is limiting.
### Why are the changes needed?
Stage level sheduling feature
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
unit tests and lots of manual testing
Closes#27773 from tgravescs/SPARK-29154.
Lead-authored-by: Thomas Graves <tgraves@nvidia.com>
Co-authored-by: Thomas Graves <tgraves@apache.org>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Assert the number of blocks to fetch equals the number of local blocks + the number of hostLocal blocks + the number of remote blocks in ShuffleBlockFetcherIterator. Also refactor the code a bit to make it easier to follow.
### Why are the changes needed?
When the numbers don't match it means something is going wrong, we should fail fast.
### Does this PR introduce any user-facing change?
No. This is basically code refactoring.
### How was this patch tested?
Tested with existing test suites.
Closes#27972 from jiangxb1987/BlockFetcher.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The test case has been flaky because the execution time sometimes exceeds the await duration. Increase the await duration to avoid flakiness.
### How was this patch tested?
Tested locally and it didn't fail anymore.
Closes#28007 from jiangxb1987/DecomTest.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Refactor `streaming-page.js` by making on-click timeline action customizable.
### Why are the changes needed?
In the current implementation, `streaming-page.js` is used from Streaming page and Structured Streaming page but the implementation of the on-click timeline action is strongly dependent on Streamng page.
Structured Streaming page doesn't define the on-click action for now but it's better to remove the dependncy for the future.
Originally, I make this change to fix `SPARK-31128` but #27883 resolved it.
So, now this is just for refactoring.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual tests with following code and confirmed there are no regression and no error in the debug console in Firefox.
For Structured Streaming:
```
spark.readStream.format("socket").options(Map("host"->"localhost", "port"->"8765")).load.writeStream.format("console").start
```
And then, visited Structured Streaming page and there were no error in the debug console when I clicked a point in the timeline.
For Spark Streaming:
```
import org.apache.spark.streaming._
val ssc = new StreamingContext(sc, Seconds(1))
ssc.socketTextStream("localhost", 8765)
dstream.foreachRDD(rdd => rdd.foreach(println))
ssc.start
```
And then, visited Streaming page and confirmed scrolling down and hilighting work well and there were no error in the debug console when I clicked a point in the timeline.
Closes#27921 from sarutak/followup-SPARK-29543-fix-oncick.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
This PR proposes that we change the return type of the `BarrierTaskContext.allGather` method to `Array[String]` instead of `ArrayBuffer[String]` since it is immutable. Based on discussion in #27640. cc zhengruifeng srowen
Closes#27951 from sarthfrey/all-gather-api.
Authored-by: sarthfrey-db <sarth.frey@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pull request fixes an issue with rolling event logs. The rolling event log directory is created ignoring the dfs umask setting. This allows the history server to prune old rolling logs when run as the group owner of the event log folder.
### Why are the changes needed?
For non-rolling event logs, log files are created ignoring the umask setting by calling setPermission after creating the file. The default umask of 022 currently causes rolling log directories to be created without group write permissions, preventing the history server from pruning logs of applications not run as the same user as the history server. This adds the same behavior for rolling event logs so users don't need to worry about the umask setting causing different behavior.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manually. The folder is created with the correct 770 permission. The status file is still affected by the umask setting, but that doesn't stop the folder from being deleted by the history server. I'm not sure if that causes any other issues. I'm not sure how to test something involving a Hadoop setting.
Closes#27764 from Kimahriman/bug/rolling-log-permissions.
Authored-by: Adam Binford <adam.binford@radiantsolutions.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR adds the proxy user on the spark-submit command to the childArgs, so the proxy user can be retrieved and used in the KubernetesAplication to add the proxy user in the driver container args
### Why are the changes needed?
The proxy user when used on the spark submit doesn't work on the Kubernetes environment since it doesn't add the `--proxy-user` argument on the driver container and when I added it manually to the Pod definition it worked just fine.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Tests were added
Closes#27422 from PedroRossi/SPARK-25355.
Authored-by: Pedro Rossi <pgrr@cin.ufpe.br>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Fix the IllegalArgumentException in broadcast exchange when numRows over 341 million but less than 512 million.
Since the maximum number of keys that `BytesToBytesMap` supports is 1 << 29, and only 70% of the slots can be used before growing in `HashedRelation`, So here the limitation should not be greater equal than 341 million (1 << 29 / 1.5(357913941)) instead of 512 million.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manually test.
Closes#27828 from LantaoJin/SPARK-31068.
Lead-authored-by: LantaoJin <jinlantao@gmail.com>
Co-authored-by: Alan Jin <jinlantao@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Spark's Web UI is using an older version of Bootstrap (v. 2.3.2) for the portal pages. Bootstrap 2.x was moved to EOL in Aug 2013 and Bootstrap 3.x was moved to EOL in July 2019 (https://github.com/twbs/release). Older versions of Bootstrap are also getting flagged in security scans for various CVEs:
https://snyk.io/vuln/SNYK-JS-BOOTSTRAP-72889https://snyk.io/vuln/SNYK-JS-BOOTSTRAP-173700https://snyk.io/vuln/npm:bootstrap:20180529https://snyk.io/vuln/npm:bootstrap:20160627
I haven't validated each CVE, but it would be nice to resolve any potential issues and get on a supported release.
The bad news is that there have been quite a few changes between Bootstrap 2 and Bootstrap 4. I've tried updating the library, refactoring/tweaking the CSS and JS to maintain a similar appearance and functionality, and testing the UI for functionality and appearance. This is a fairly large change so I'm sure additional testing and fixes will be needed.
### How was this patch tested?
This has been manually tested, but there is a ton of functionality and there are many pages and detail pages so it is very possible bugs introduced from the upgrade were missed. Additional testing and feedback is welcomed. If it appears a whole page was missed let me know and I'll take a pass at addressing that page/section.
Closes#27370 from clarkead/bootstrap4-core-upgrade.
Authored-by: Dale Clarke <a.dale.clarke@gmail.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
There is a minor issue in https://github.com/apache/spark/pull/26201
In the streaming statistics page, there is such error
```
streaming-page.js:211 Uncaught TypeError: Cannot read property 'top' of undefined
at SVGCircleElement.<anonymous> (streaming-page.js:211)
at SVGCircleElement.__onclick (d3.min.js:1)
```
in the console after clicking the timeline graph.
This PR is to fix it.
### Why are the changes needed?
Fix the error of javascript execution.
### Does this PR introduce any user-facing change?
No, the error shows up in the console.
### How was this patch tested?
Manual test.
Closes#27883 from gengliangwang/fixSelector.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
When loading DataFrames from JDBC datasource with Kerberos authentication, remote executors (yarn-client/cluster etc. modes) fail to establish a connection due to lack of Kerberos ticket or ability to generate it.
This is a real issue when trying to ingest data from kerberized data sources (SQL Server, Oracle) in enterprise environment where exposing simple authentication access is not an option due to IT policy issues.
In this PR I've added Postgres support (other supported databases will come in later PRs).
What this PR contains:
* Added `keytab` and `principal` JDBC options
* Added `ConnectionProvider` trait and it's impementations:
* `BasicConnectionProvider` => unsecure connection
* `PostgresConnectionProvider` => postgres secure connection
* Added `ConnectionProvider` tests
* Added `PostgresKrbIntegrationSuite` docker integration test
* Created `SecurityUtils` to concentrate re-usable security related functionalities
* Documentation
### Why are the changes needed?
Missing JDBC kerberos support.
### Does this PR introduce any user-facing change?
Yes, 2 additional JDBC options added:
* keytab
* principal
If both provided then Spark does kerberos authentication.
### How was this patch tested?
To demonstrate the functionality with a standalone application I've created this repository: https://github.com/gaborgsomogyi/docker-kerberos
* Additional + existing unit tests
* Additional docker integration test
* Test on cluster manually
* `SKIP_API=1 jekyll build`
Closes#27637 from gaborgsomogyi/SPARK-30874.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@apache.org>
### What changes were proposed in this pull request?
This patch changes to log better message (at least relevant to decommission) when registering signal handler for SIGPWR fails. SIGPWR is non-POSIX and not all unix-like OS support it; we can easily find the case, macOS.
### Why are the changes needed?
Spark already logs message on failing to register handler for SIGPWR, but the error message is too general which doesn't give the information of the impact. End users should be noticed that failing to register handler for SIGPWR effectively "disables" the feature of decommission.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually tested via running standalone master/worker in macOS 10.14.6, with `spark.worker.decommission.enabled= true`, and submit an example application to run executors.
(NOTE: the message may be different a bit, as the message can be updated in review phase.)
For worker log:
```
20/03/06 17:19:13 INFO Worker: Registering SIGPWR handler to trigger decommissioning.
20/03/06 17:19:13 INFO SignalUtils: Registering signal handler for PWR
20/03/06 17:19:13 WARN SignalUtils: Failed to register SIGPWR - disabling worker decommission.
java.lang.IllegalArgumentException: Unknown signal: PWR
at java.base/jdk.internal.misc.Signal.<init>(Signal.java:148)
at jdk.unsupported/sun.misc.Signal.<init>(Signal.java:139)
at org.apache.spark.util.SignalUtils$.$anonfun$registerSignal$1(SignalUtils.scala:95)
at scala.collection.mutable.HashMap.getOrElseUpdate(HashMap.scala:86)
at org.apache.spark.util.SignalUtils$.registerSignal(SignalUtils.scala:93)
at org.apache.spark.util.SignalUtils$.register(SignalUtils.scala:81)
at org.apache.spark.deploy.worker.Worker.<init>(Worker.scala:73)
at org.apache.spark.deploy.worker.Worker$.startRpcEnvAndEndpoint(Worker.scala:887)
at org.apache.spark.deploy.worker.Worker$.main(Worker.scala:855)
at org.apache.spark.deploy.worker.Worker.main(Worker.scala)
```
For executor:
```
20/03/06 17:21:52 INFO CoarseGrainedExecutorBackend: Registering PWR handler.
20/03/06 17:21:52 INFO SignalUtils: Registering signal handler for PWR
20/03/06 17:21:52 WARN SignalUtils: Failed to register SIGPWR - disabling decommission feature.
java.lang.IllegalArgumentException: Unknown signal: PWR
at java.base/jdk.internal.misc.Signal.<init>(Signal.java:148)
at jdk.unsupported/sun.misc.Signal.<init>(Signal.java:139)
at org.apache.spark.util.SignalUtils$.$anonfun$registerSignal$1(SignalUtils.scala:95)
at scala.collection.mutable.HashMap.getOrElseUpdate(HashMap.scala:86)
at org.apache.spark.util.SignalUtils$.registerSignal(SignalUtils.scala:93)
at org.apache.spark.util.SignalUtils$.register(SignalUtils.scala:81)
at org.apache.spark.executor.CoarseGrainedExecutorBackend.onStart(CoarseGrainedExecutorBackend.scala:86)
at org.apache.spark.rpc.netty.Inbox.$anonfun$process$1(Inbox.scala:120)
at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:203)
at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
at org.apache.spark.rpc.netty.MessageLoop.org$apache$spark$rpc$netty$MessageLoop$$receiveLoop(MessageLoop.scala:75)
at org.apache.spark.rpc.netty.MessageLoop$$anon$1.run(MessageLoop.scala:41)
at java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515)
at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:834)
```
Closes#27832 from HeartSaVioR/SPARK-31011.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
1.Add version information to the configuration of `Status`.
2.Update the docs of `Status`.
3.By the way supplementary documentation about https://github.com/apache/spark/pull/27847
I sorted out some information show below.
Item name | Since version | JIRA ID | Commit ID | Note
-- | -- | -- | -- | --
spark.appStateStore.asyncTracking.enable | 2.3.0 | SPARK-20653 | 772e4648d95bda3353723337723543c741ea8476#diff-9ab674b7af7b2097f7d28cb6f5fd1e8c |
spark.ui.liveUpdate.period | 2.3.0 | SPARK-20644 | c7f38e5adb88d43ef60662c5d6ff4e7a95bff580#diff-9ab674b7af7b2097f7d28cb6f5fd1e8c |
spark.ui.liveUpdate.minFlushPeriod | 2.4.2 | SPARK-27394 | a8a2ba11ac10051423e58920062b50f328b06421#diff-9ab674b7af7b2097f7d28cb6f5fd1e8c |
spark.ui.retainedJobs | 1.2.0 | SPARK-2321 | 9530316887612dca060a128fca34dd5a6ab2a9a9#diff-1f32bcb61f51133bd0959a4177a066a5 |
spark.ui.retainedStages | 0.9.0 | None | 112c0a1776bbc866a1026a9579c6f72f293414c4#diff-1f32bcb61f51133bd0959a4177a066a5 | 0.9.0-incubating-SNAPSHOT
spark.ui.retainedTasks | 2.0.1 | SPARK-15083 | 55db26245d69bb02b7d7d5f25029b1a1cd571644#diff-6bdad48cfc34314e89599655442ff210 |
spark.ui.retainedDeadExecutors | 2.0.0 | SPARK-7729 | 9f4263392e492b5bc0acecec2712438ff9a257b7#diff-a0ba36f9b1f9829bf3c4689b05ab6cf2 |
spark.ui.dagGraph.retainedRootRDDs | 2.1.0 | SPARK-17171 | cc87280fcd065b01667ca7a59a1a32c7ab757355#diff-3f492c527ea26679d4307041b28455b8 |
spark.metrics.appStatusSource.enabled | 3.0.0 | SPARK-30060 | 60f20e5ea2000ab8f4a593b5e4217fd5637c5e22#diff-9f796ae06b0272c1f0a012652a5b68d0 |
### Why are the changes needed?
Supplemental configuration version information.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Exists UT
Closes#27848 from beliefer/add-version-to-status-config.
Lead-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
yi.wu
bmarcott
Antonin Delpeuch
Thomas Graves
Liang-Chi Hsieh
herman
Huang-Yi
Onur Satici
Liang-Chi Hsieh
beliefer
Kousuke Saruta
Gengliang Wang
Dongjoon Hyun
Holden Karau
Thomas Graves
turbofei
ulysses
Yuanjian Li
Kengo Seki
Xingbo Jiang
sarthfrey-db
Adam Binford
Pedro Rossi
LantaoJin
Dale Clarke
Gabor Somogyi
Jungtaek Lim (HeartSaVioR)
