[SPARK-26718] Fixed integer overflow in SS kafka rateLimit calculation - ASF JIRA

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Details

Type: Bug
Status: Resolved
Priority: Major
Resolution: Fixed
Affects Version/s: 2.4.0
Fix Version/s: 2.3.3, 2.4.1, 3.0.0
Component/s: Structured Streaming
Labels:
None

Description

when running spark structured streaming using lib: `"org.apache.spark" %% "spark-sql-kafka-0-10" % "2.4.0"`, we keep getting error regarding current offset fetching:

Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 3, qa2-hdp-4.acuityads.org, executor 2): java.lang.AssertionError: assertion failed: latest offs
et -9223372036854775808 does not equal -1
at scala.Predef$.assert(Predef.scala:170)
at org.apache.spark.sql.kafka010.KafkaMicroBatchInputPartitionReader.resolveRange(KafkaMicroBatchReader.scala:371)
at org.apache.spark.sql.kafka010.KafkaMicroBatchInputPartitionReader.<init>(KafkaMicroBatchReader.scala:329)
at org.apache.spark.sql.kafka010.KafkaMicroBatchInputPartition.createPartitionReader(KafkaMicroBatchReader.scala:314)
at org.apache.spark.sql.execution.datasources.v2.DataSourceRDD.compute(DataSourceRDD.scala:42)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)

for some reason, looks like fetchLatestOffset returned a Long.MIN_VALUE for one of the partitions. I checked the structured streaming checkpoint, that was correct, it's the currentAvailableOffset was set to Long.MIN_VALUE.

kafka broker version: 1.1.0.
lib we used:

{{libraryDependencies += "org.apache.spark" %% "spark-sql-kafka-0-10" % "2.4.0" }}

how to reproduce:
basically we started a structured streamer and subscribed a topic of 4 partitions. then produced some messages into topic, job crashed and logged the stacktrace like above.

also the committed offsets seem fine as we see in the logs:

=== Streaming Query ===
Identifier: [id = c46c67ee-3514-4788-8370-a696837b21b1, runId = 31878627-d473-4ee8-955d-d4d3f3f45eb9]
Current Committed Offsets: {KafkaV2[Subscribe[REVENUEEVENT]]: {"REVENUEEVENT":{"0":1}}}
Current Available Offsets: {KafkaV2[Subscribe[REVENUEEVENT]]: {"REVENUEEVENT":{"0":-9223372036854775808}}}

so spark streaming recorded the correct value for partition: 0, but the current available offsets returned from kafka is showing Long.MIN_VALUE.