[jira] [Created] (SPARK-36553) KMeans fails with NegativeArraySizeException for K = 50000 after issue #27758 was introduced

["Anders Rydbirk (Jira)" <ji...@apache.org>]

Anders Rydbirk created SPARK-36553:
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             Summary: 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


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|[https://github.com/apache/spark/pull/27758/files#diff-725d4624ddf4db9cc51721c2ddaef50a1bc30e7b471e0439da28c5b5582efdfdR52]] which significantly reduces the maximum value of K:

[DistanceMeasure.scala|[https://github.com/zhengruifeng/spark/blob/d31d488e0e48a82fd5b43c406f07b8c7d27dd53c/mllib/src/main/scala/org/apache/spark/mllib/clustering/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.

 

*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.


Please let me know if more information is needed, this is my first time raising a bug for a OS.



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