[SPARK-40622][SQL][CORE]Remove the limitation that single task result must fit in 2GB #38064

liuzqt · 2022-10-01T00:42:38Z

What changes were proposed in this pull request?

Single task result must fit in 2GB, because we're using byte array or ByteBuffer(which is backed by byte array as well), and thus has a limit of 2GB(java array size limit on byte[]).

This PR is trying to fix this by replacing byte array with ChunkedByteBuffer.

Why are the changes needed?

To overcome the 2GB limit for single task.

Does this PR introduce any user-facing change?

No

How was this patch tested?

Unit test

AmplabJenkins · 2022-10-01T04:54:14Z

Can one of the admins verify this patch?

…cting

core/src/main/scala/org/apache/spark/serializer/SerializerHelper.scala

Ngone51 · 2022-10-10T08:14:58Z

core/src/main/scala/org/apache/spark/executor/Executor.scala

              s"dropping it.")
-            ser.serialize(new IndirectTaskResult[Any](TaskResultBlockId(taskId), resultSize))
+            SerializerHelper.serializeToChunkedBuffer(ser,
+              new IndirectTaskResult[Any](TaskResultBlockId(taskId), resultSize))


The object IndirectTaskResult should be small. Do we also want to use chunked buffer for it?

Good point. The serializedDirectResult in line 668 is the one that might exceed 2GB and we want to replace it with ChunkedByteBuffer.

While the serializedResult here is always guaranteed to be smaller than 2GB in all cases

larger than maxResultSize, dropping, sending a IndirectTaskResult indication failure, which is small

larger than maxDirectResultSize, sending a IndirectTaskResult with the blockId, which is small

directly send back to driver, which is always smaller than 2GB, guarded by direct result size value check I added here

So let's just use ByteBuffer for serializedResult.

Thanks for the point!

core/src/main/scala/org/apache/spark/executor/Executor.scala

core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala

core/src/main/scala/org/apache/spark/serializer/SerializerHelper.scala

core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala

Ngone51 · 2022-10-10T08:37:36Z

core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala

+      if (chunk.hasArray) {
+        // zero copy if the bytebuffer is backed by array
+        out.write(chunk.array(), chunk.arrayOffset(), chunk.limit())
+      } else {
+        // fallback to copy approach
+        if (buffer == null) {
+          buffer = new Array[Byte](bufferLen)
+        }
+        val originalPos = chunk.position()
+        chunk.rewind()
+        var bytesToRead = Math.min(chunk.remaining(), bufferLen)
+        while (bytesToRead > 0) {
+          chunk.get(buffer, 0, bytesToRead)
+          out.write(buffer, 0, bytesToRead)
+          bytesToRead = Math.min(chunk.remaining(), bufferLen)
+        }
+        chunk.position(originalPos)


Shall we extract this common code block to a function?

Good idea! Extracted to writeBufferToDest helper function in companion object.

core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala

….scala Co-authored-by: wuyi <[email protected]>

mridulm

Took a quick pass, yet to go over the tests.

core/src/main/scala/org/apache/spark/serializer/SerializerHelper.scala

mridulm · 2022-10-12T05:20:03Z

core/src/main/scala/org/apache/spark/executor/Executor.scala

-        val serializedDirectResult = ser.serialize(directResult)
-        val resultSize = serializedDirectResult.limit()
+        val directResult = new DirectTaskResult(valueByteBuffer, accumUpdates, metricPeaks)
+        val serializedDirectResult = SerializerHelper.serializeToChunkedBuffer(ser, directResult)


Unlike the earlier invocation of serializeToChunkedBuffer (L#599) , here we have a good estimate of the size - something to leverage and minimize the cost of serializeToChunkedBuffer

do you mean we can explicitly choose the chunk size here to avoid too-small/to-large chunk?

Yes, we do know valueByteBuffer.size - and we know what the serialization overhead of DirectTaskResult is.
Will give us a much tighter bound on the size.

core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala

mridulm · 2022-10-12T05:41:21Z

core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala

+    val chunksNum = in.readInt()
+    val indices = 0 until chunksNum
+    val chunksSize = indices.map(_ => in.readInt())
+    val chunksDirect = indices.map(_ => in.readBoolean())


Do we have cases where we want to preserve whether the buffer was direct or not across VM ? The current usecase does not require it ?
+CC @Ngone51 ?

If not, drop this and simplify the impl ?

Do we have cases where we want to preserve whether the buffer was direct or not across VM ?

I don't have such cases in my mind..

Shall we default to on-heap buffer in deserialization regardless what it was before serialization? @mridulm @Ngone51

Yes, let us do that - if some specific reason comes up in future, we can add support as required.
Will make the implementation much more simpler.

Thanks for the suggestion, updated.

Looks like we are still preserving chunksDirect, remove it ? And always deserialize to heap buffer via ByteBuffer.wrap ?

core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala

liuzqt · 2022-10-12T17:35:37Z

Some general comments about the performance implication regarding replacing Array[Byte] and ByteBuffer(backed by Array[Byte]) with ChunkedByteBuffer:

when reading from stream (i.e., ByteArrayInputStream vs ChunkedByteBufferInputStream), no much differences, while ByteArrayInputStream might a little bit win in terms of cache locality because of continuous memory, but ChunkedByteBuffer won't be too bad along as the chunk is reasonable
when we're writing to stream(i.e., ByteArrayOutputStream vs ChunkedByteBufferOutputStream)
- ByteArrayOutputStream start with a small buffer(32 bytes) and grow 2x exponentially, and have to do array copy every grow
- ChunkedByteBufferOutputStream use fixed chunk size to grow(which you can specify when you create the stream), while the grow is append style instead of copy style
- do some manual benchmark on large data, ChunkedByteBufferOutputStream is much faster, (tried different data size from 100MB to 1GB and different chunk size from 1KB to 1MB, can see at least ~2x speedup), I would attribute to array copy overhead mostly.
- when eventually dump to ByteBuffer(or raw byte array) vs. ChunkedByteBuffer, the latter might waste some memory space in the last chunk, but not a big deal I believe. And in serialization they're the same.
- after all, result collection is a small portion in the whole end-to-end query

Ngone51 · 2022-10-13T06:45:23Z

core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala

        tasks.foreach { task =>
-          val result = new DirectTaskResult[Int](valueSer.serialize(task.taskId), Seq(), Array())
+          val result = new DirectTaskResult[Int](valueSer.serialize(task.taskId), Seq(),
+            Array[Long]())


Is it a required change (adding [Long]) now? Seems like it was Long already.

If I remove [Long], will get a compile error:

overloaded method constructor DirectTaskResult with alternatives: (valueByteBuffer: java.nio.ByteBuffer,accumUpdates: Seq[org.apache.spark.util.AccumulatorV2[_, _]],metricPeaks: Array[Long])org.apache.spark.scheduler.DirectTaskResult[Int] <and> (valueByteBuffer: org.apache.spark.util.io.ChunkedByteBuffer,accumUpdates: Seq[org.apache.spark.util.AccumulatorV2[_, _]],metricPeaks: Array[Long])org.apache.spark.scheduler.DirectTaskResult[Int] cannot be applied to (java.nio.ByteBuffer, Seq[Nothing], Array[Nothing]) val result = new DirectTaskResult[Int](valueSer.serialize(task.taskId), Seq(),

The compiler somehow failed to infer the type. This happened after I added the overloaded constructor in DirectTaskResult.

My guessing is that overloading has to be resolved before type constraints can be determined for each parameter, those that drive type inference but overloading resolution can't happen without knowing the types of each parameter.....I haven't figured out a way to solve this

Ngone51 · 2022-10-13T06:47:07Z

sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala

+    // create data of size ~3GB in single partition, which exceeds the byte array limit
+    // random gen to make sure it's poorly compressed
+    val df = spark.range(0, 3000, 1, 1).selectExpr("id", s"genData(id, 1000000) as data")
+    val res = df.queryExecution.executedPlan.executeCollect()


Will this take much memory on the driver?

Yes. We want to test both serialization(executor side)/deserialization(driver side) handle the large data(>2GB limit) properly

mridulm · 2022-10-13T18:37:02Z

@liuzqt Most task results are very small.
We will now be over-provisioning that by a few orders of magnitude when moving to ChunkedByteBufferOutputStream - while a vanishingly small set of cases hit the large buffer case.
This can potentially have an impact on memory utilization at executor, and if possible look at ways to mitigate - particularly, for example, when we have a good estimate of the output size.

This is not to say I have serious concerns (we do use ChunkedByteBufferOutputStream with precisely that size everywhere else !) - but it is not without tradeoff.

mridulm · 2022-10-13T18:42:46Z

core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala

-private[spark] class ChunkedByteBuffer(var chunks: Array[ByteBuffer]) {
+private[spark] class ChunkedByteBuffer(var chunks: Array[ByteBuffer]) extends Externalizable {
  require(chunks != null, "chunks must not be null")
+  require(!chunks.contains(null), "chunks must not contain null")


The next line should have implicitly checked for it - was this getting triggered ?

The ChunkeByteBuffer has a constructor overload def this(byteBuffer: ByteBuffer), which is invoked with null in the DirectTaskResult empty constructor.

It was triggered in some temp code snippet during my dev, but should not have any usage in the current code base(at least can not be found explicitly). That empty constructor might serve as default constructor for some serialization code I guess.......After all, the code in ChunkeByteBuffer are assuming chunks doesn't contains null, so I think it's no harm to do this check at the beginning.

It is another iteration over chunks as part of construction. Not a big deal, but I was looking to minimize impact of the change.

core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala

mridulm · 2022-10-13T18:46:17Z

core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala

+          chunk.get(buffer, 0, bytesToRead)
+          out.write(buffer, 0, bytesToRead)
+          bytesToRead = Math.min(chunk.remaining(), bufferLen)
+        }


nit: Pull this out into a separate method ?
I will need to search our codebase, but I would have expected this snippet to be already there somewhere.

There should be... but I was not able to locate them....not very familiar with the code base. Feel free to let me know if you find them

There is Utils.writeByteBuffer which writes a buffer to an OutputStream.
It does not do a very good job, since it allocates a buffer the same size as remaining - instead, we should enhance it to do what this method is doing.

Additionally, we can use a ThreadLocal[Array[Byte]] in Utils for use with this copy (the buffer here)

I tried to reuse Utils.writeByteBuffer, and I noticed that there're two versions of Utils.writeByteBuffer for OutputStream and DataOutput respectively and the code is identical so added a Utils.writeByteBufferImpl to extract the common logic, also added ThreadLocal[Array[Byte]]

liuzqt · 2022-11-09T23:56:56Z

Tried to reduce the large result test to ~2.1GB, as well as local-cluster approach but neither works....Since I've verified that test on my local, so I removed the test

mridulm · 2022-11-11T00:50:16Z

Ideally, we would want to make sure there are no regressions as the codebase evolves - having a unittest to capture the case would help for that.
Does bringing up a local cluster with a higher memory limit help to test this ?

Ngone51 · 2022-11-11T07:16:19Z

Should the PR title be changed to something like "Remove the limitation of a single task result must fit in 2GB"?

liuzqt · 2022-11-11T07:48:36Z

@mridulm I've tried local-cluster[1,1,3072] but doesn't help. Is there any way to increase the JVM mem in github action job?

jiangxb1987

LGTM, thanks for working on it!

mridulm · 2022-11-14T07:21:50Z

+CC @Yikun, @HyukjinKwon - any suggestions for @liuzqt's query above ? Thanks

Yikun · 2022-11-14T11:23:41Z

The 2GB should not the limited of github action, there only are a 7GB limited for total memory.

I'm not sure adding some jvm mem opts by setting sbt opt could help or not.

This reverts commit b2b933b.

liuzqt · 2022-11-15T01:44:25Z

Increasing jvm mem to 6G will lead to TPCDS test get killed https://github.com/liuzqt/spark/actions/runs/3464533302/jobs/5786183673

let me try 5GB...

HyukjinKwon

I actually don't think we can have a test with 2GB because GA is already using a lot of memory in fact. One way would be to add a test, and mark it as ignore so other people can verify that manually in the future.

Another way is to skip the tests in GitHub Actions enviornment (e.g., assume(!sys.env.contains("GITHUB_ACTIONS"))).

The change looks very nice to have BTW. Thanks for fixing this.

mridulm · 2022-11-15T07:15:37Z

Looks like TPCDS is still dying ...
Can we try @HyukjinKwon's suggestion @liuzqt - it will ensure we are testing this during local builds and release votes, while not causing GA to fail.
TPCDSQuerySuite already uses this for not executing q72

mridulm · 2022-11-16T02:56:24Z

Merged to master.
Thanks for fixing this @liuzqt !
Thanks for the reviews @Ngone51, @sadikovi, @jiangxb1987 :-)
And thanks for help with GA @HyukjinKwon and @Yikun !

LuciferYang · 2022-11-18T03:35:27Z

sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala

+  test("collect data with single partition larger than 2GB bytes array limit") {
+    // This test requires large memory and leads to OOM in Github Action so we skip it. Developer
+    // should verify it in local build.
+    assume(!sys.env.contains("GITHUB_ACTIONS"))


hmm... I test this suite with Java 8/11/17 on Linux/MacOS On Apple Silicon with following commands:

Maven:

build/mvn clean install -DskipTests -pl sql/core -am build/mvn clean test -pl sql/core -Dtest=none -DwildcardSuites=org.apache.spark.sql.DatasetLargeResultCollectingSuite

and

dev/change-scala-version.sh 2.13 build/mvn clean install -DskipTests -pl sql/core -am -Pscala-2.13 build/mvn clean test -pl sql/core -Pscala-2.13 -Dtest=none -DwildcardSuites=org.apache.spark.sql.DatasetLargeResultCollectingSuite

SBT:

build/sbt clean "sql/testOnly org.apache.spark.sql.DatasetLargeResultCollectingSuite"

dev/change-scala-version.sh 2.13 build/sbt clean "sql/testOnly org.apache.spark.sql.DatasetLargeResultCollectingSuite" -Pscala-2.13

All test failed with java.lang.OutOfMemoryError: Java heap space as follows:

10:19:56.910 ERROR org.apache.spark.executor.Executor: Exception in task 0.0 in stage 0.0 (TID 0) java.lang.OutOfMemoryError: Java heap space at java.nio.HeapByteBuffer.<init>(HeapByteBuffer.java:57) at java.nio.ByteBuffer.allocate(ByteBuffer.java:335) at org.apache.spark.serializer.SerializerHelper$.$anonfun$serializeToChunkedBuffer$1(SerializerHelper.scala:40) at org.apache.spark.serializer.SerializerHelper$.$anonfun$serializeToChunkedBuffer$1$adapted(SerializerHelper.scala:40) at org.apache.spark.serializer.SerializerHelper$$$Lambda$2321/1995130077.apply(Unknown Source) at org.apache.spark.util.io.ChunkedByteBufferOutputStream.allocateNewChunkIfNeeded(ChunkedByteBufferOutputStream.scala:87) at org.apache.spark.util.io.ChunkedByteBufferOutputStream.write(ChunkedByteBufferOutputStream.scala:75) at java.io.ObjectOutputStream$BlockDataOutputStream.write(ObjectOutputStream.java:1853) at java.io.ObjectOutputStream.write(ObjectOutputStream.java:709) at org.apache.spark.util.Utils$.$anonfun$writeByteBuffer$1(Utils.scala:271) at org.apache.spark.util.Utils$.$anonfun$writeByteBuffer$1$adapted(Utils.scala:271) at org.apache.spark.util.Utils$$$Lambda$2324/69671223.apply(Unknown Source) at org.apache.spark.util.Utils$.writeByteBufferImpl(Utils.scala:249) at org.apache.spark.util.Utils$.writeByteBuffer(Utils.scala:271) at org.apache.spark.util.io.ChunkedByteBuffer.$anonfun$writeExternal$2(ChunkedByteBuffer.scala:103) at org.apache.spark.util.io.ChunkedByteBuffer.$anonfun$writeExternal$2$adapted(ChunkedByteBuffer.scala:103) at org.apache.spark.util.io.ChunkedByteBuffer$$Lambda$2323/1073743200.apply(Unknown Source) at scala.collection.ArrayOps$.foreach$extension(ArrayOps.scala:1328) at org.apache.spark.util.io.ChunkedByteBuffer.writeExternal(ChunkedByteBuffer.scala:103) at java.io.ObjectOutputStream.writeExternalData(ObjectOutputStream.java:1459) at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1430) at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178) at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548) at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509) at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432) at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178) at java.io.ObjectOutputStream.writeArray(ObjectOutputStream.java:1378) at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1174) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:46) at org.apache.spark.serializer.SerializerHelper$.serializeToChunkedBuffer(SerializerHelper.scala:42) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:599)

@liuzqt Could you tell me how to run the local test successfully with Spark default Java options?

@HyukjinKwon @mridulm @jiangxb1987 @sadikovi @Ngone51 I failed to run this case locally with Spark default Java options. Could you run successfully? Or should we ignore this case by default?

Ah .. this is tricky.. Let's just probably mark this test as ignore for now then.. with adding some comments like some custom memory configurations would have to be applied ...

#38704 ignore this case as default

…t must fit in 2GB ### What changes were proposed in this pull request? Single task result must fit in 2GB, because we're using byte array or `ByteBuffer`(which is backed by byte array as well), and thus has a limit of 2GB(java array size limit on `byte[]`). This PR is trying to fix this by replacing byte array with `ChunkedByteBuffer`. ### Why are the changes needed? To overcome the 2GB limit for single task. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Unit test Closes apache#38064 from liuzqt/SPARK-40622. Authored-by: Ziqi Liu <[email protected]> Signed-off-by: Mridul <mridul<at>gmail.com>

github-actions bot added CORE SQL labels Oct 1, 2022

liuzqt force-pushed the SPARK-40622 branch from 12ccc98 to ee9e153 Compare October 1, 2022 01:11

liuzqt force-pushed the SPARK-40622 branch from a5f35f8 to 94234be Compare October 3, 2022 18:27

liuzqt marked this pull request as ready for review October 3, 2022 18:36

replace byte array with ChunkedByteBuffer in single task result colle…

5690fed

…cting

liuzqt force-pushed the SPARK-40622 branch from 988e455 to 5690fed Compare October 10, 2022 04:38

Ngone51 reviewed Oct 10, 2022

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liuzqt and others added 3 commits October 10, 2022 10:04

Merge branch 'master' into SPARK-40622

91695ef

Update core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer…

f9151a4

….scala Co-authored-by: wuyi <[email protected]>

minor refine

f486dd8

liuzqt force-pushed the SPARK-40622 branch from 38c40dc to 1a812d0 Compare October 10, 2022 17:33

extract zero-copy writing in ChunkedByteBuffer as common util function

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liuzqt force-pushed the SPARK-40622 branch from 1a812d0 to ec9c738 Compare October 10, 2022 17:40

liuzqt requested a review from Ngone51 October 10, 2022 18:43

use ByteBuffer for serializedResult

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liuzqt force-pushed the SPARK-40622 branch from f35824d to fbbe788 Compare October 11, 2022 21:13

mridulm reviewed Oct 12, 2022

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liuzqt added 3 commits October 12, 2022 10:39

refine

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remove writeToStream

e557605

use getChunks

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Ngone51 reviewed Oct 13, 2022

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mridulm reviewed Oct 13, 2022

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core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala Outdated Show resolved Hide resolved

mridulm reviewed Oct 13, 2022

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core/src/main/scala/org/apache/spark/util/io/ChunkedByteBuffer.scala Outdated Show resolved Hide resolved

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liuzqt force-pushed the SPARK-40622 branch from a280673 to 8fc8ad9 Compare November 9, 2022 23:52

remove large result test

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liuzqt force-pushed the SPARK-40622 branch from 8fc8ad9 to b2b933b Compare November 10, 2022 00:04

Ngone51 approved these changes Nov 11, 2022

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liuzqt changed the title ~~[SPARK-40622][SQL][CORE]Result of a single task in collect() must fit in 2GB~~ [SPARK-40622][SQL][CORE]Remove the limitation that single task result must fit in 2GB Nov 11, 2022

jiangxb1987 approved these changes Nov 11, 2022

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liuzqt added 2 commits November 14, 2022 11:23

Revert "remove large result test"

0853b95

This reverts commit b2b933b.

increase jvm mem to 6G in SparkBuild

20e2bcc

set jvm mem to 5GB

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HyukjinKwon reviewed Nov 15, 2022

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skip DatasetLargeResultCollectingSuite in Github Action

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mridulm approved these changes Nov 16, 2022

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asfgit closed this in c23245d Nov 16, 2022

LuciferYang reviewed Nov 18, 2022

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[SPARK-40622][SQL][CORE]Remove the limitation that single task result must fit in 2GB #38064

[SPARK-40622][SQL][CORE]Remove the limitation that single task result must fit in 2GB #38064

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liuzqt commented Oct 1, 2022

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?

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