Spark Bloom Filter, others using treeAggregate can throw OutOfMemoryError

When the Bloom filter stat function is invoked on a large dataframe that requires a BitArray of size >2GB, it will result in a java.lang.OutOfMemoryError. As mentioned in a similar bug, this is due to the zero value passed to treeAggrete. Irrespective of spark.serializer value, this will be serialized using JavaSerializer which has a hard limit of 2GB. Using a solution similar to SPARK-26228 and setting spark.serializer to KryoSerializer can avoid this error.

Steps to reproduce:

val df = List.range(0, 10).toDF("Id")val expectedNumItems = 2000000000L // 2 billion
val fpp = 0.03
val bf = df.stat.bloomFilter("Id", expectedNumItems, fpp)

Stack trace:

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:44) at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:101) at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:413) at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:406) at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:162) at org.apache.spark.SparkContext.clean(SparkContext.scala:2604) at org.apache.spark.rdd.PairRDDFunctions.$anonfun$combineByKeyWithClassTag$1(PairRDDFunctions.scala:86) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:165) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:125) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:395) at org.apache.spark.rdd.PairRDDFunctions.combineByKeyWithClassTag(PairRDDFunctions.scala:75) at org.apache.spark.rdd.PairRDDFunctions.$anonfun$foldByKey$1(PairRDDFunctions.scala:218) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:165) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:125) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:395) at org.apache.spark.rdd.PairRDDFunctions.foldByKey(PairRDDFunctions.scala:207) at org.apache.spark.rdd.RDD.$anonfun$treeAggregate$1(RDD.scala:1224) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:165) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:125) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:395) at org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1203) at org.apache.spark.sql.DataFrameStatFunctions.buildBloomFilter(DataFrameStatFunctions.scala:602) at org.apache.spark.sql.DataFrameStatFunctions.bloomFilter(DataFrameStatFunctions.scala:541)