Ryne Yang created SPARK-26718:
---------------------------------
Summary: structured streaming fetched wrong current offset from
kafka
Key: SPARK-26718
URL: https://issues.apache.org/jira/browse/SPARK-26718
Project: Spark
Issue Type: Bug
Components: Structured Streaming
Affects Versions: 2.4.0
Reporter: Ryne Yang
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:
{code:java}
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)
{code}
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:
{code:java}
=== 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}}}
{code}
so spark streaming recorded the correct value for partition: 0, but the current
available offsets returned from kafka is showing Long.MIN_VALUE.
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