TAESUK KIM created SPARK-27069:
----------------------------------
Summary: Spark(2.3.1) LDA transfomation memory
error(java.lang.OutOfMemoryError at
java.io.ByteArrayOutputStream.hugeCapacity(ByteArrayOutputStream.java:123)
Key: SPARK-27069
URL: https://issues.apache.org/jira/browse/SPARK-27069
Project: Spark
Issue Type: Bug
Components: ML
Affects Versions: 2.3.2
Environment: Below is my environment
DataSet
# Document : about 100,000,000 --> 10,000,000 --> 1,000,000(All fail)
# Word : about 3553918(can't change)
Spark environment
# executor-memory,driver-memory : 18G --> 32g --> 64 --> 128g(all fail)
# executor-core,driver-core : 3
# spark.serializer : default and
org.apache.spark.serializer.KryoSerializer(both fail)
# spark.executor.memoryOverhead : 18G --> 36G fail
Jave version : 1.8.0_191 (Oracle Corporation)
Reporter: TAESUK KIM
I trained LDA(feature dimension : 100, iteration: 100 or 50, Distributed
version , ml ) using Spark 2.3.2(emr-5.18.0) .
After that I want to transform new DataSet by using that model. But when I
transform new data, I alway get error related memory error.
I changed data size from x 0.1 , to x 0.01. But always get memory
error(java.lang.OutOfMemoryError at
java.io.ByteArrayOutputStream.hugeCapacity(ByteArrayOutputStream.java:123)
That hugeCapacity error(overflow) is happened when size of array is over
Integer.MAX_VALUE - 8. But I changed data size to small size. I can't find why
this error is happened.
And I want to change serializer to KryoSerializer. But I found
this org.apache.spark.util.ClosureCleaner$.ensureSerializable always call
org.apache.spark.serializer.JavaSerializationStream even though I register
KryoClasses
Is there any thing I can do ?
Below is code
{{val countvModel = CountVectorizerModel.load("s3://~/") }}
{{val ldaModel = DistributedLDAModel.load("s3://~/") }}
{{val transformeddata=countvModel.transform(inputData).select("productid",
"itemid", "ptkString", "features") var featureldaDF =
ldaModel.transform(transformeddata).select("productid", "itemid",
"topicDistribution", "ptkString").toDF("productid", "itemid", "features",
"ptkString") featureldaDF=featureldaDF.persist //this is 328 line }}
Other testing
# Java option : UseParallelGC , UseG1GC (all fail)
Below is log
{{19/03/05 20:59:03 ERROR ApplicationMaster: User class threw exception:
java.lang.OutOfMemoryError java.lang.OutOfMemoryError at
java.io.ByteArrayOutputStream.hugeCapacity(ByteArrayOutputStream.java:123) at
java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:117) at
java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93) at
java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153) at
org.apache.spark.util.ByteBufferOutputStream.write(ByteBufferOutputStream.scala:41)
at
java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
at
java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189) at
java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348) at
org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43)
at
org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100)
at
org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:342)
at
org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:335)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:159) at
org.apache.spark.SparkContext.clean(SparkContext.scala:2299) at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1.apply(RDD.scala:850)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1.apply(RDD.scala:849)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363) at
org.apache.spark.rdd.RDD.mapPartitionsWithIndex(RDD.scala:849) at
org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:608)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127) at
org.apache.spark.sql.execution.columnar.InMemoryRelation.buildBuffers(InMemoryRelation.scala:107)
at
org.apache.spark.sql.execution.columnar.InMemoryRelation.<init>(InMemoryRelation.scala:102)
at
org.apache.spark.sql.execution.columnar.InMemoryRelation$.apply(InMemoryRelation.scala:43)
at
org.apache.spark.sql.execution.CacheManager$$anonfun$cacheQuery$1.apply(CacheManager.scala:97)
at
org.apache.spark.sql.execution.CacheManager.writeLock(CacheManager.scala:67) at
org.apache.spark.sql.execution.CacheManager.cacheQuery(CacheManager.scala:91)
at org.apache.spark.sql.Dataset.persist(Dataset.scala:2907) at
coupang.cs.predictforxgboost.App$.main(App.scala:328) at
coupang.cs.predictforxgboost.App.main(App.scala) at
sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498) at
org.apache.spark.deploy.yarn.ApplicationMaster$$anon$4.run(ApplicationMaster.scala:721)
}}
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