Shixiong Zhu created SPARK-30208:
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Summary: A race condition when reading from Kafka in PySpark
Key: SPARK-30208
URL: https://issues.apache.org/jira/browse/SPARK-30208
Project: Spark
Issue Type: Bug
Components: Structured Streaming
Affects Versions: 2.4.4
Reporter: Jiawen Zhu
When using PySpark to read from Kafka, there is a race condition that Spark may
use KafkaConsumer in multiple threads at the same time and throw the following
error:
{code}
java.util.ConcurrentModificationException: KafkaConsumer is not safe for
multi-threaded access
at
kafkashaded.org.apache.kafka.clients.consumer.KafkaConsumer.acquire(KafkaConsumer.java:2215)
at
kafkashaded.org.apache.kafka.clients.consumer.KafkaConsumer.close(KafkaConsumer.java:2104)
at
kafkashaded.org.apache.kafka.clients.consumer.KafkaConsumer.close(KafkaConsumer.java:2059)
at
org.apache.spark.sql.kafka010.InternalKafkaConsumer.close(KafkaDataConsumer.scala:451)
at
org.apache.spark.sql.kafka010.KafkaDataConsumer$NonCachedKafkaDataConsumer.release(KafkaDataConsumer.scala:508)
at
org.apache.spark.sql.kafka010.KafkaSourceRDD$$anon$1.close(KafkaSourceRDD.scala:126)
at
org.apache.spark.util.NextIterator.closeIfNeeded(NextIterator.scala:66)
at
org.apache.spark.sql.kafka010.KafkaSourceRDD$$anonfun$compute$3.apply(KafkaSourceRDD.scala:131)
at
org.apache.spark.sql.kafka010.KafkaSourceRDD$$anonfun$compute$3.apply(KafkaSourceRDD.scala:130)
at
org.apache.spark.TaskContext$$anon$1.onTaskCompletion(TaskContext.scala:162)
at
org.apache.spark.TaskContextImpl$$anonfun$markTaskCompleted$1.apply(TaskContextImpl.scala:131)
at
org.apache.spark.TaskContextImpl$$anonfun$markTaskCompleted$1.apply(TaskContextImpl.scala:131)
at
org.apache.spark.TaskContextImpl$$anonfun$invokeListeners$1.apply(TaskContextImpl.scala:144)
at
org.apache.spark.TaskContextImpl$$anonfun$invokeListeners$1.apply(TaskContextImpl.scala:142)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at
org.apache.spark.TaskContextImpl.invokeListeners(TaskContextImpl.scala:142)
at
org.apache.spark.TaskContextImpl.markTaskCompleted(TaskContextImpl.scala:130)
at org.apache.spark.scheduler.Task.doRunTask(Task.scala:155)
at org.apache.spark.scheduler.Task.run(Task.scala:112)
at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$13.apply(Executor.scala:497)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1526)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:503)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
{code}
When using PySpark, reading from Kafka is actually happening in a separate
writer thread rather that the task thread. When a task is early terminated
(e.g., there is a limit operator), the task thread may stop the KafkaConsumer
when the writer thread is using it.
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