[jira] [Created] (SPARK-34485) GeneratedIterator grows beyond 64 KB in Spark Structured Streaming application

["Kim Min Woo (Jira)" <ji...@apache.org>]

Kim Min Woo created SPARK-34485:
-----------------------------------

             Summary: GeneratedIterator grows beyond 64 KB in Spark Structured Streaming application
                 Key: SPARK-34485
                 URL: https://issues.apache.org/jira/browse/SPARK-34485
             Project: Spark
          Issue Type: Bug
          Components: SQL, Structured Streaming
    Affects Versions: 3.0.1, 3.0.0
         Environment: ||Name||Version||
|OS|Ubuntu 18.04|
|JAVA|11.0.9|
|Scala|2.12.10|
|Spark|3.0.1|
            Reporter: Kim Min Woo


I ran the following Spark Structured Streaming application, but failed with ERROR CodeGenerator: failed to compile: org.codehaus.janino.InternalCompilerException: Compiling "GeneratedClass" in "generated.java": Code of method "expand_doConsume_0$(Lorg/apache/spark/sql/catalyst/expressions/GeneratedClass$GeneratedIteratorForCodegenStage3;Lorg/apache/spark/sql/catalyst/InternalRow;Lorg/apache/spark/sql/catalyst/InternalRow;ZJZ)V" of class "org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3" grows beyond 64 KB

 
{code:java}
def window(timeColumn: Column, windowDuration: String, slideDuration: String): Column
{code}
 

However, if I increase the slideDuration(from 1 second to 2 seconds) value, the above error message does not show up.

It's a simple code, but I don't understand why the GeneratedIterator grows beyond 64 KB.

[With this link(Gist),|https://gist.github.com/c357c28138f7f57505189839ecef7fc0] it is possible to check the the entire error messages.

It can be observed that the "Expand" Unary Logical Operator is unusually long in the error messages.


Codes

 
{code:java}
  def inputSchema: StructType = StructType(
    Seq(
      StructField("timestamp", LongType, nullable = false),
      StructField("missingInfo", LongType, nullable = true),
      StructField("jobId", LongType, nullable = false),
      StructField("taskId", LongType, nullable = false),
      StructField("machineId", LongType, nullable = true),
      StructField("eventType", IntegerType, nullable = false),
      StructField("userId", IntegerType, nullable = true),
      StructField("category", IntegerType, nullable = true),
      StructField("priority", IntegerType, nullable = false),
      StructField("cpu", FloatType, nullable = true),
      StructField("ram", FloatType, nullable = true),
      StructField("disk", FloatType, nullable = true),
      StructField("constraints", IntegerType, nullable = true),
    )
  )
{code}
 

 
{code:java}
    ss.readStream
      .format("kafka")
      .option("kafka.bootstrap.servers", "localhost:9092")
      .option("subscribe", "test")
      .option("enable.auto.commit", "false")
      .option("startingOffsets", "earliest")
      .option("maxOffsetsPerTrigger", "1000")
      .load()
      .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
      .select(col("key"), from_csv(col("value"), inputSchema, Map("delimiter" -> ",")).as("task_event"))
      .withColumn("event_time", (col("task_event.timestamp") / 1000 + startTraceTime).cast(TimestampType))
      .where("task_event.eventType == 1")
      .dropDuplicates("key")
      .withWatermark("event_time", "60 seconds")
      .groupBy(
        window(col("event_time"), "60 seconds", "1 second"),
        col("task_event.jobId")
      ).agg(avg("task_event.cpu").as("avgCpu"))
      .writeStream
      .format("console")
      .option("checkpointLocation", "/tmp/checkpoint")
      .start()
      .awaitTermination()
{code}
 



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