[SPARK-47518][CORE] Skip transfer the last spilled shuffle data #45661

wankunde · 2024-03-22T07:30:42Z

What changes were proposed in this pull request?

If there is only one spill data file, spark will transfer that spill file to the final data file.

@Override
  public void transferMapSpillFile(
      File mapSpillFile,
      long[] partitionLengths,
      long[] checksums) throws IOException {
    // The map spill file already has the proper format, and it contains all of the partition data.
    // So just transfer it directly to the destination without any merging.
    File outputFile = blockResolver.getDataFile(shuffleId, mapId);
    File tempFile = Utils.tempFileWith(outputFile);
    Files.move(mapSpillFile.toPath(), tempFile.toPath());
    blockResolver
      .writeMetadataFileAndCommit(shuffleId, mapId, partitionLengths, checksums, tempFile);
  }

But if that spill file and the final data file are on different disks, there will still be a heavy data transfer.

sun.nio.fs.UnixCopyFile.transfer(Native Method)
sun.nio.fs.UnixCopyFile.copyFile(UnixCopyFile.java:251)
sun.nio.fs.UnixCopyFile.move(UnixCopyFile.java:471)
sun.nio.fs.UnixFileSystemProvider.move(UnixFileSystemProvider.java:262)
java.nio.file.Files.move(Files.java:1395)
org.apache.spark.shuffle.sort.io.LocalDiskSingleSpillMapOutputWriter.transferMapSpillFile(LocalDiskSingleSpillMapOutputWriter.java:52)
org.apache.spark.shuffle.sort.UnsafeShuffleWriter.mergeSpills(UnsafeShuffleWriter.java:280)
org.apache.spark.shuffle.sort.UnsafeShuffleWriter.closeAndWriteOutput(UnsafeShuffleWriter.java:224)
org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:180)
org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)
org.apache.spark.scheduler.Task.run(Task.scala:131)
org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:498)
org.apache.spark.executor.Executor$TaskRunner$$Lambda$453/980524593.apply(Unknown Source)
org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1465)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:501)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
java.lang.Thread.run(Thread.java:748)

We can get the disk where the final shuffle data file is located, and spill the final temp shuffle block in that disk to avoid unnecessary data copying.

Why are the changes needed?

Optimize spark shuffle performance.

Test mv a 2.6 G file between HDD disks on a node which has a high iowait :

$ time mv /hadoop/2/yarn/local/yarn-yarn-nodemanager.log.10 /hadoop/3/yarn/local/yarn-yarn-nodemanager.log.10

real	2m19.694s
user	0m0.029s
sys	0m6.120s
$ time mv /hadoop/3/yarn/local/yarn-yarn-nodemanager.log.10 /hadoop/3/yarn/local/yarn-yarn-nodemanager.log.11

real	0m0.003s
user	0m0.001s
sys	0m0.001s

When mv file to another disk which has a high iowait is very slow while mv file to the same disk is quite fast.

Does this PR introduce any user-facing change?

No

How was this patch tested?

Exists UT

Was this patch authored or co-authored using generative AI tooling?

No

dongjoon-hyun

According to the PR description, are you suggesting the linux file move is slow, @wankunde ? Could you elaborate your experience with numbers?

there will still be a heavy data transfer.

java.nio.file.Files.move(Files.java:1395)

wankunde · 2024-03-22T16:19:35Z

Test mv a 2.6 G file on a node which has a high iowait :

$ time mv /hadoop/2/yarn/local/yarn-yarn-nodemanager.log.10 /hadoop/3/yarn/local/yarn-yarn-nodemanager.log.10

real	2m19.694s
user	0m0.029s
sys	0m6.120s
$ time mv /hadoop/3/yarn/local/yarn-yarn-nodemanager.log.10 /hadoop/3/yarn/local/yarn-yarn-nodemanager.log.11

real	0m0.003s
user	0m0.001s
sys	0m0.001s

When mv file to another disk which has a high iowait is very slow while mv file to the same disk is quite fast.

dongjoon-hyun · 2024-03-22T16:20:28Z

Thanks. Could you put that into the PR description, @wankunde .

dongjoon-hyun

cc @cloud-fan , @mridulm , @tgravescs , @viirya too

Stale.

dongjoon-hyun · 2024-03-22T16:27:40Z

core/src/main/java/org/apache/spark/shuffle/sort/UnsafeShuffleWriter.java

+    Optional<File> finalDataFileDir;
+    if (shuffleExecutorComponents instanceof LocalDiskShuffleExecutorComponents) {
+      File dataFile =
+        new IndexShuffleBlockResolver(sparkConf, blockManager).getDataFile(shuffleId, mapId);


Is this only used to invoke getParentFile?

dongjoon-hyun · 2024-03-22T16:30:17Z

core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala

@@ -226,6 +226,24 @@ private[spark] class DiskBlockManager(
    (blockId, getFile(blockId))
  }

+  /** Produces a unique block id and File suitable for storing shuffled intermediate results
+   * in the input directory. */


nit.

/** * Produces a unique block id and File suitable for storing shuffled intermediate results * in the input directory. */

dongjoon-hyun · 2024-03-22T16:39:14Z

core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala

+    var tmpFile = new File(fileDir, blockId.name)
+    while (tmpFile.exists()) {
+      blockId = TempShuffleBlockId(UUID.randomUUID())
+      tmpFile = new File(fileDir, blockId.name)


If fileDir is invalid, we are in the Infinite loop, aren't we? It seems that we need a safe guard to avoid the infinite loop, @wankunde . Also, please add a unit test case for the invalid fileDir (maybe, nonExist)

Added UT, if fileDir is invalid, tmpFile will not exists, exit this loop.
Generate new block only when the fileDir is valid and the first TempShuffleBlockId file already created by some other task.

viirya · 2024-03-22T17:54:46Z

core/src/main/java/org/apache/spark/shuffle/sort/UnsafeShuffleWriter.java

@@ -219,7 +221,15 @@ void closeAndWriteOutput() throws IOException {
    updatePeakMemoryUsed();
    serBuffer = null;
    serOutputStream = null;
-    final SpillInfo[] spills = sorter.closeAndGetSpills();
+    Optional<File> finalDataFileDir;
+    if (shuffleExecutorComponents instanceof LocalDiskShuffleExecutorComponents) {


Hmm, it looks a bit hacky to handle local disk shuffle specially here.

Sorry, I'm not familiar with the block storage in KubernetesLocalDiskShuffleExecutorComponents, so only handle LocalDiskShuffleExecutorComponents here.

Or should I add a new method getDataFile() in trait ShuffleExecutorComponents ?

wankunde · 2024-03-24T10:42:54Z

UT org.apache.spark.util.collection.SorterSuite failed:

2024-03-23T03:43:57.7101735Z �[0m[�[0m�[0minfo�[0m] �[0m�[0m�[32m- SPARK-5984 TimSort bug (32 seconds, 966 milliseconds)�[0m�[0m
2024-03-23T03:43:58.5733341Z �[0m[�[0m�[0minfo�[0m] �[0m�[0m�[31morg.apache.spark.util.collection.SorterSuite *** ABORTED *** (33 seconds, 954 milliseconds)�[0m�[0m
2024-03-23T03:43:58.5736160Z �[0m[�[0m�[0minfo�[0m] �[0m�[0m�[31m  java.lang.OutOfMemoryError: Java heap space�[0m�[0m
2024-03-23T03:43:58.5739209Z �[0m[�[0m�[0minfo�[0m] �[0m�[0m�[31m  at org.apache.spark.util.collection.SorterSuite.$anonfun$new$11(SorterSuite.scala:145)�[0m�[0m
2024-03-23T03:43:58.5747591Z �[0m[�[0m�[0minfo�[0m] �[0m�[0m�[31m  at org.apache.spark.util.collection.SorterSuite$$Lambda$17217/0x00007f618e8d2610.apply$mcV$sp(Unknown Source)�[0m�[0m
2024-03-23T03:43:58.5751054Z �[0m[�[0m�[0minfo�[0m] �[0m�[0m�[31m  at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)�[0m�[0m

wankunde · 2024-03-24T10:43:03Z

Retest this please

dongjoon-hyun

To @wankunde , to be clear, the below two comments are critical to this PR. You had better answer to resolve them.

wankunde · 2024-03-25T01:46:36Z

To @wankunde , to be clear, the below two comments are critical to this PR. You had better answer to resolve them.

https://github.com/apache/spark/pull/45661/files#r1535871592

https://github.com/apache/spark/pull/45661/files#r1535982390

My replies were not submitted , sorry about that.

cloud-fan · 2024-03-25T02:01:02Z

I'm a bit worried about this change, as it changes the assumption of always having two phases during shuffle: first phase only write to temp files, the second phase "commit" it to the final destination in a short time. The shuffle and task scheduling process is quite convoluted in Spark and I can't be 100% sure that this is a safe change.

wankunde · 2024-03-25T02:38:46Z

I'm a bit worried about this change, as it changes the assumption of always having two phases during shuffle: first phase only write to temp files, the second phase "commit" it to the final destination in a short time. The shuffle and task scheduling process is quite convoluted in Spark and I can't be 100% sure that this is a safe change.

Hi, @cloud-fan , the shuffle in this PR still need two phases during shuffle:

Stage 1 will write to a TempShuffleBlockId file in the final shuffle data file directory, and will make sure no conflict.
Stage 2 is "commit phase", and will just rename the TempShuffleBlockId file to the final shuffle data file if there is only one shuffle spill file.

ulysses-you · 2024-03-25T02:48:02Z

Test mv a 2.6 G file on a node which has a high iowait :

@wankunde what kind of disk did you test ? Have you tried ssd ?

wankunde · 2024-03-25T03:06:58Z

Test mv a 2.6 G file on a node which has a high iowait :

@wankunde what kind of disk did you test ? Have you tried ssd ?

This test is on HDD disks, copy 2.6 file on NVMe disks is 10x times faster than HDD.

cloud-fan · 2024-03-25T07:25:35Z

Before this PR, will Spark write any temp files to the final shuffle file directory?

wankunde · 2024-03-25T10:54:02Z

Before this PR, will Spark write any temp files to the final shuffle file directory?

In most cases, spark will manage its internal files as block files, including ShuffleDataBlockId, ShuffleIndexBlockId, TempShuffleBlockId and some other blocks. These block files are located by hash(block name) and the full block file path will be localDirs(hash % localDirs.length) / (hash / localDirs.length) % subDirsPerLocalDir / filename

If the block files have the same hash code, then will be in the same directory.

cloud-fan · 2024-03-25T16:26:07Z

To confirm: is the goal to write the last spill file to the same directory as the final shuffle file, so that the following transfer operation can be cheap?

mridulm · 2024-03-26T07:22:13Z

A move between disks requires actual data copy, while a mv within the same disk is simply a metadata operation.
Having said that, if the disk doing < 20 MBps transfer rates, there are some severe infra issues to be sorted out.

I understand the motivation for the proposed fix here, but I agree with @cloud-fan - I am not in favor of complicating spark code for handling extremely degenerate cases like this.

wankunde · 2024-03-26T08:04:27Z

To confirm: is the goal to write the last spill file to the same directory as the final shuffle file, so that the following transfer operation can be cheap?

Yes

cloud-fan · 2024-03-26T08:54:01Z

I think a more general approach is, DiskBlockManager#createTempShuffleBlock should co-locate temp shuffle files and final shuffle files (with the same shuffle id and map id). This benefits more than one spill files as well.

wankunde · 2024-03-26T09:58:50Z

I think a more general approach is, DiskBlockManager#createTempShuffleBlock should co-locate temp shuffle files and final shuffle files (with the same shuffle id and map id). This benefits more than one spill files as well.

Spark will rename the TempShuffleBlock file to the final data file when there is only one TempShuffleBlock file.

spark/core/src/main/java/org/apache/spark/shuffle/sort/UnsafeShuffleWriter.java

Lines 277 to 290 in 0bbef20

    
           } else if (spills.length == 1) { 
        
             Optional<SingleSpillShuffleMapOutputWriter> maybeSingleFileWriter = 
        
                 shuffleExecutorComponents.createSingleFileMapOutputWriter(shuffleId, mapId); 
        
             if (maybeSingleFileWriter.isPresent()) { 
        
               // Here, we don't need to perform any metrics updates because the bytes written to this 
        
               // output file would have already been counted as shuffle bytes written. 
        
               partitionLengths = spills[0].partitionLengths; 
        
               logger.debug("Merge shuffle spills for mapId {} with length {}", mapId, 
        
                   partitionLengths.length); 
        
               maybeSingleFileWriter.get() 
        
                 .transferMapSpillFile(spills[0].file, partitionLengths, sorter.getChecksums()); 
        
             } else { 
        
               partitionLengths = mergeSpillsUsingStandardWriter(spills); 
        
             }

If there are multiple TempShuffleBlock files, spark will always read all the shuffle data into the final shuffle data file.
At this time, the workload of reading and writing is unavoidable.

cloud-fan · 2024-03-26T12:53:44Z

I think renaming and reading/writing should be faster if the files are co-located on the same disk?

mridulm · 2024-03-26T13:35:30Z

I think renaming and reading/writing should be faster if the files are co-located on the same disk?

At these disk speeds, other than rename, it will be slower if they are on same disk.
In general though, we can run out of disk space in a single disk as well - which is why we spread it across disks.

cloud-fan · 2024-03-26T13:51:52Z

core/src/main/java/org/apache/spark/shuffle/sort/ShuffleExternalSorter.java

@@ -198,7 +201,8 @@ private void writeSortedFile(boolean isFinalFile) {
    // spark.shuffle.compress instead of spark.shuffle.spill.compress, so we need to use
    // createTempShuffleBlock here; see SPARK-3426 for more details.
    final Tuple2<TempShuffleBlockId, File> spilledFileInfo =
-      blockManager.diskBlockManager().createTempShuffleBlock();


one idea: if isFinalFile is true, then we call a special version of createTempShuffleBlock that takes shuffle & map id, and returns a file path under the same directory of the final shuffle file. Then we don't need to change other places?

Do you mean change the parameters of createTempShuffleBlockInDir from finalDataFileDir to Tuple2<ShuffleId, MapId> ?

That would assume the final output has to go to blockResolver.getDataFile(shuffleId, mapId) right @cloud-fan ?
Currently at this layer we do not make that assumption ...

I was initially toying with the idea of passing mapId and shuffleId as constructor params ... and do something similar when I realized this would make assumptions that the code currently does not make - and so why the base directory is being passed around.

(And then ofcourse I thought we could solve it in LocalDiskSingleSpillMapOutputWriter here, but was completely wrong :-( ).

mridulm · 2024-03-26T14:07:37Z

ExecutorDiskUtils.getFilePath and DiskBlockManager.getFile determines the layout of the files ...
and given this is specific to an optimization in LocalDiskShuffleExecutorComponents (which determines the output file), why not simply modify LocalDiskSingleSpillMapOutputWriter.transferMapSpillFile to leverage this layout information and 'host' the final output in the same disk ?

KubernetesLocalDiskShuffleExecutorComponents.recoverDiskStore for example, does something similar as well (using the layout info to recover - not this specific layout).


  public void transferMapSpillFile(
      File mapSpillFile,
      long[] partitionLengths,
      long[] checksums) throws IOException {
    // The map spill file already has the proper format, and it contains all of the partition data.
    // So just transfer it directly to the destination without any merging.
    File parent = ExecutorDiskUtils.getLocalDir(mapSpillFile);
    File outputFile = blockResolver.getDataFile(shuffleId, mapId, Some(Array(parent.getAbsolutePath)));
    File tempFile = Utils.tempFileWith(outputFile);
    Files.move(mapSpillFile.toPath(), tempFile.toPath());
    blockResolver
      .writeMetadataFileAndCommit(shuffleId, mapId, partitionLengths, checksums, tempFile);
  }

This is only going to marginally alleviate the issues though, the slow disks are the RC here - and their impact will manifest in a lot of other ways as well. (We built push based shuffle when we started seeing very high disk issues of this sort - though unfortunately it is only supported in yarn right now)

Stale.

wankunde · 2024-03-27T00:49:20Z

ExecutorDiskUtils.getFilePath and DiskBlockManager.getFile determines the layout of the files ... and given this is specific to an optimization in LocalDiskShuffleExecutorComponents (which determines the output file), why not simply modify LocalDiskSingleSpillMapOutputWriter.transferMapSpillFile to leverage this layout information and 'host' the final output in the same disk ?

KubernetesLocalDiskShuffleExecutorComponents.recoverDiskStore for example, does something similar as well (using the layout info to recover - not this specific layout).
  public void transferMapSpillFile(
      File mapSpillFile,
      long[] partitionLengths,
      long[] checksums) throws IOException {
    // The map spill file already has the proper format, and it contains all of the partition data.
    // So just transfer it directly to the destination without any merging.
    File parent = ExecutorDiskUtils.getLocalDir(mapSpillFile);
    File outputFile = blockResolver.getDataFile(shuffleId, mapId, Some(Array(parent.getAbsolutePath)));
    File tempFile = Utils.tempFileWith(outputFile);
    Files.move(mapSpillFile.toPath(), tempFile.toPath());
    blockResolver
      .writeMetadataFileAndCommit(shuffleId, mapId, partitionLengths, checksums, tempFile);
  }
This is only going to marginally alleviate the issues though, the slow disks are the RC here - and their impact will manifest in a lot of other ways as well. (We built push based shuffle when we started seeing very high disk issues of this sort - though unfortunately it is only supported in yarn right now)

Thanks @mridulm
Do you mean to change the location of the final shuffle data file? I prefer keep the current shuffle files layout, the executors write shuffle files, and then the external shuffle service can read them without any additional work.
KubernetesLocalDiskShuffleExecutorComponents.recoverDiskStore(sparkConf, blockManager) will perform a heavy disk scan in only KubernetesLocalDiskShuffleExecutorComponents.initializeExecutor() method, the internal layout of shuffle data files is the same as in yarn.

mridulm · 2024-03-27T04:57:41Z

Actually, thinking more, what I suggested will not work - since the local directory is chosen based on the hash of the block id - which is different, sigh.

mridulm

I am still very hesitant about this change ... it is trying to workaround overloaded disks for a specific corner case observed.
You will have pervasive slowdowns and sporadic failures when this sort of disk characterstics are exhibited, and there is only so much we can make spark robust unfortunately in this context - without looking at other options to mitigate the underlying RC.

mridulm · 2024-03-27T05:12:16Z

core/src/main/java/org/apache/spark/shuffle/sort/ShuffleExternalSorter.java

+      finalDataFileDir.map(blockManager.diskBlockManager()::createTempShuffleBlockInDir)
+        .orElseGet(blockManager.diskBlockManager()::createTempShuffleBlock);


Suggested change

finalDataFileDir.map(blockManager.diskBlockManager()::createTempShuffleBlockInDir)

.orElseGet(blockManager.diskBlockManager()::createTempShuffleBlock);

finalDataFileDir.filter(v -> spills.isEmpty()).map(blockManager.diskBlockManager()::createTempShuffleBlockInDir)

.orElseGet(blockManager.diskBlockManager()::createTempShuffleBlock);

We need this only when there is a single output file, else we want to spread it.

Commit this change, thanks

mridulm · 2024-03-27T05:39:13Z

core/src/main/java/org/apache/spark/shuffle/sort/ShuffleExternalSorter.java

@@ -198,7 +201,8 @@ private void writeSortedFile(boolean isFinalFile) {
    // spark.shuffle.compress instead of spark.shuffle.spill.compress, so we need to use
    // createTempShuffleBlock here; see SPARK-3426 for more details.
    final Tuple2<TempShuffleBlockId, File> spilledFileInfo =
-      blockManager.diskBlockManager().createTempShuffleBlock();


That would assume the final output has to go to blockResolver.getDataFile(shuffleId, mapId) right @cloud-fan ?
Currently at this layer we do not make that assumption ...

I was initially toying with the idea of passing mapId and shuffleId as constructor params ... and do something similar when I realized this would make assumptions that the code currently does not make - and so why the base directory is being passed around.

(And then ofcourse I thought we could solve it in LocalDiskSingleSpillMapOutputWriter here, but was completely wrong :-( ).

[SPARK-47518][CORE] Skip transfer the last spilled shuffle data

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github-actions bot added the CORE label Mar 22, 2024

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wankunde changed the title ~~[WIP][SPARK-47518][CORE] Skip transfer the last spilled shuffle data~~ [SPARK-47518][CORE] Skip transfer the last spilled shuffle data Mar 22, 2024

wankunde changed the title ~~[SPARK-47518][CORE] Skip transfer the last spilled shuffle data~~ [WIP][SPARK-47518][CORE] Skip transfer the last spilled shuffle data Mar 22, 2024

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wankunde changed the title ~~[WIP][SPARK-47518][CORE] Skip transfer the last spilled shuffle data~~ [SPARK-47518][CORE] Skip transfer the last spilled shuffle data Mar 25, 2024

Update code

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wankunde added 2 commits March 26, 2024 18:39

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Only change for one spill file

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		finalDataFileDir.map(blockManager.diskBlockManager()::createTempShuffleBlockInDir)
		.orElseGet(blockManager.diskBlockManager()::createTempShuffleBlock);

[SPARK-47518][CORE] Skip transfer the last spilled shuffle data #45661

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[SPARK-47518][CORE] Skip transfer the last spilled shuffle data #45661

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wankunde commented Mar 22, 2024 • edited

What changes were proposed in this pull request?

Why are the changes needed?

Does this PR introduce any user-facing change?

How was this patch tested?

Was this patch authored or co-authored using generative AI tooling?

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