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https://issues.apache.org/jira/browse/SPARK-36553?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Huaxin Gao resolved SPARK-36553.
--------------------------------
Fix Version/s: 3.1.3
3.3.0
3.2.2
Assignee: zhengruifeng
Resolution: Fixed
> KMeans fails with NegativeArraySizeException for K = 50000 after issue #27758
> was introduced
> --------------------------------------------------------------------------------------------
>
> Key: SPARK-36553
> URL: https://issues.apache.org/jira/browse/SPARK-36553
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib, PySpark
> Affects Versions: 3.1.1
> Reporter: Anders Rydbirk
> Assignee: zhengruifeng
> Priority: Major
> Fix For: 3.1.3, 3.3.0, 3.2.2
>
>
> We are running KMeans on approximately 350M rows of x, y, z coordinates using
> the following configuration:
> {code:java}
> KMeans(
> featuresCol='features',
> predictionCol='centroid_id',
> k=50000,
> initMode='k-means||',
> initSteps=2,
> tol=0.00005,
> maxIter=20,
> seed=SEED,
> distanceMeasure='euclidean'
> )
> {code}
> When using Spark 3.0.0 this worked fine, but when upgrading to 3.1.1 we are
> consistently getting errors unless we reduce K.
> Stacktrace:
>
> {code:java}
> An error occurred while calling o167.fit.An error occurred while calling
> o167.fit.: java.lang.NegativeArraySizeException: -897458648 at
> scala.reflect.ManifestFactory$DoubleManifest.newArray(Manifest.scala:194) at
> scala.reflect.ManifestFactory$DoubleManifest.newArray(Manifest.scala:191) at
> scala.Array$.ofDim(Array.scala:221) at
> org.apache.spark.mllib.clustering.DistanceMeasure.computeStatistics(DistanceMeasure.scala:52)
> at
> org.apache.spark.mllib.clustering.KMeans.runAlgorithmWithWeight(KMeans.scala:280)
> at org.apache.spark.mllib.clustering.KMeans.runWithWeight(KMeans.scala:231)
> at org.apache.spark.ml.clustering.KMeans.$anonfun$fit$1(KMeans.scala:354) at
> org.apache.spark.ml.util.Instrumentation$.$anonfun$instrumented$1(Instrumentation.scala:191)
> at scala.util.Try$.apply(Try.scala:213) at
> org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:191)
> at org.apache.spark.ml.clustering.KMeans.fit(KMeans.scala:329) at
> java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native
> Method) at
> java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(Unknown
> Source) at
> java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(Unknown
> Source) at java.base/java.lang.reflect.Method.invoke(Unknown Source) at
> py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at
> py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at
> py4j.Gateway.invoke(Gateway.java:282) at
> py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at
> py4j.commands.CallCommand.execute(CallCommand.java:79) at
> py4j.GatewayConnection.run(GatewayConnection.java:238) at
> java.base/java.lang.Thread.run(Unknown Source)
> {code}
>
> The issue is introduced by
> [#27758|#diff-725d4624ddf4db9cc51721c2ddaef50a1bc30e7b471e0439da28c5b5582efdfdR52]]
> which significantly reduces the maximum value of K. Snippit of line that
> throws error from [DistanceMeasure.scala:|#L52]]
> {code:java}
> val packedValues = Array.ofDim[Double](k * (k + 1) / 2)
> {code}
>
> *What we have tried:*
> * Reducing iterations
> * Reducing input volume
> * Reducing K
> Only reducing K have yielded success.
>
> *Possible workaround:*
> # Roll back to Spark 3.0.0 since a KMeansModel generated with 3.0.0 cannot
> be loaded in 3.1.1.
> # Reduce K. Currently trying with 45000.
>
> *What we don't understand*:
> Given the line of code above, we do not understand why we would get an
> integer overflow.
> For K=50,000, packedValues should be allocated with the size of 1,250,025,000
> < (2^31) and not result in a negative array size.
>
> *Suggested resolution:*
> I'm not strong in the inner workings on KMeans, but my immediate thought
> would be to add a fallback to previous logic for K larger than a set
> threshold if the optimisation is to stay in place, as it breaks compatibility
> from 3.0.0 to 3.1.1 for edge cases.
>
> Please let me know if more information is needed, this is my first time
> raising a bug for a OS.
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