[
https://issues.apache.org/jira/browse/SPARK-19185?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15826527#comment-15826527
]
Cody Koeninger commented on SPARK-19185:
----------------------------------------
I'd expect setting cache capacity to zero to cause failures, but it's probably
(slightly) faster to try than just patching the lines of code I pointed out.
In general, a single application using kafka consumers should not be reading
from different places in the same topicpartition in different threads, because
it breaks ordering guarantees. That's an implication of Kafka semantics, not
Spark semantics. That's why the consumer cache exists the way it does.
Changing that behavior on a widespread basis is going to break ordering
guarantees, which will break some people's existing jobs. Hence my comments
about arguing design decisions with committers.
> ConcurrentModificationExceptions with CachedKafkaConsumers when Windowing
> -------------------------------------------------------------------------
>
> Key: SPARK-19185
> URL: https://issues.apache.org/jira/browse/SPARK-19185
> Project: Spark
> Issue Type: Bug
> Components: DStreams
> Affects Versions: 2.0.2
> Environment: Spark 2.0.2
> Spark Streaming Kafka 010
> Mesos 0.28.0 - client mode
> spark.executor.cores 1
> spark.mesos.extra.cores 1
> Reporter: Kalvin Chau
> Labels: streaming, windowing
>
> We've been running into ConcurrentModificationExcpetions "KafkaConsumer is
> not safe for multi-threaded access" with the CachedKafkaConsumer. I've been
> working through debugging this issue and after looking through some of the
> spark source code I think this is a bug.
> Our set up is:
> Spark 2.0.2, running in Mesos 0.28.0-2 in client mode, using
> Spark-Streaming-Kafka-010
> spark.executor.cores 1
> spark.mesos.extra.cores 1
> Batch interval: 10s, window interval: 180s, and slide interval: 30s
> We would see the exception when in one executor there are two task worker
> threads assigned the same Topic+Partition, but a different set of offsets.
> They would both get the same CachedKafkaConsumer, and whichever task thread
> went first would seek and poll for all the records, and at the same time the
> second thread would try to seek to its offset but fail because it is unable
> to acquire the lock.
> Time0 E0 Task0 - TopicPartition("abc", 0) X to Y
> Time0 E0 Task1 - TopicPartition("abc", 0) Y to Z
> Time1 E0 Task0 - Seeks and starts to poll
> Time1 E0 Task1 - Attempts to seek, but fails
> Here are some relevant logs:
> {code}
> 17/01/06 03:10:01 Executor task launch worker-1 INFO KafkaRDD: Computing
> topic test-topic, partition 2 offsets 4394204414 -> 4394238058
> 17/01/06 03:10:01 Executor task launch worker-0 INFO KafkaRDD: Computing
> topic test-topic, partition 2 offsets 4394238058 -> 4394257712
> 17/01/06 03:10:01 Executor task launch worker-1 DEBUG CachedKafkaConsumer:
> Get spark-executor-consumer test-topic 2 nextOffset 4394204414 requested
> 4394204414
> 17/01/06 03:10:01 Executor task launch worker-0 DEBUG CachedKafkaConsumer:
> Get spark-executor-consumer test-topic 2 nextOffset 4394204414 requested
> 4394238058
> 17/01/06 03:10:01 Executor task launch worker-0 INFO CachedKafkaConsumer:
> Initial fetch for spark-executor-consumer test-topic 2 4394238058
> 17/01/06 03:10:01 Executor task launch worker-0 DEBUG CachedKafkaConsumer:
> Seeking to test-topic-2 4394238058
> 17/01/06 03:10:01 Executor task launch worker-0 WARN BlockManager: Putting
> block rdd_199_2 failed due to an exception
> 17/01/06 03:10:01 Executor task launch worker-0 WARN BlockManager: Block
> rdd_199_2 could not be removed as it was not found on disk or in memory
> 17/01/06 03:10:01 Executor task launch worker-0 ERROR Executor: Exception in
> task 49.0 in stage 45.0 (TID 3201)
> java.util.ConcurrentModificationException: KafkaConsumer is not safe for
> multi-threaded access
> at
> org.apache.kafka.clients.consumer.KafkaConsumer.acquire(KafkaConsumer.java:1431)
> at
> org.apache.kafka.clients.consumer.KafkaConsumer.seek(KafkaConsumer.java:1132)
> at
> org.apache.spark.streaming.kafka010.CachedKafkaConsumer.seek(CachedKafkaConsumer.scala:95)
> at
> org.apache.spark.streaming.kafka010.CachedKafkaConsumer.get(CachedKafkaConsumer.scala:69)
> at
> org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:227)
> at
> org.apache.spark.streaming.kafka010.KafkaRDD$KafkaRDDIterator.next(KafkaRDD.scala:193)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:462)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at
> org.apache.spark.storage.memory.MemoryStore.putIteratorAsBytes(MemoryStore.scala:360)
> at
> org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:951)
> at
> org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:926)
> at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:866)
> at
> org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:926)
> at
> org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:670)
> at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:330)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:281)
> at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:105)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
> at org.apache.spark.scheduler.Task.run(Task.scala:86)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> 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)
> 17/01/06 03:10:01 Executor task launch worker-1 DEBUG CachedKafkaConsumer:
> Polled [test-topic-2] 8237
> 17/01/06 03:10:01 Executor task launch worker-1 DEBUG CachedKafkaConsumer:
> Get spark-executor-consumer test-topic 2 nextOffset 4394204415 requested
> 4394204415
> 17/01/06 03:10:01 Executor task launch worker-1 DEBUG CachedKafkaConsumer:
> Get spark-executor-consumer test-topic 2 nextOffset 4394204416 requested
> 4394204416
> ...
> {code}
> It looks like when WindowedDStream does the getOrCompute call its computing
> all the sets of of offsets it needs and tries to farm out the work in
> parallel. So each available worker task gets each set of offsets that need to
> be read.
> After realizing what was going on I tested four states:
> * spark.executor.cores 1 and spark.mesos.extra.cores 0
> ** No Exceptions
> * spark.executor.cores 1 and spark.mesos.extra.cores 1
> ** ConcurrentModificationException
> * spark.executor.cores 2 and spark.mesos.extra.cores 0
> ** ConcurrentModificationException
> * spark.executor.cores 2 and spark.mesos.extra.cores 1
> ** ConcurrentModificationException
> Minimal set of code I was able to reproduce with:
> Streaming batch interval was set to 2 seconds. This increased the rate of
> exceptions I saw.
> {code}
> val kafkaParams = Map[String, Object](
> "bootstrap.servers" -> brokers,
> "key.deserializer" -> classOf[KafkaAvroDeserializer],
> "value.deserializer" -> classOf[KafkaAvroDeserializer],
> "enable.auto.commit" -> (false: java.lang.Boolean),
> "group.id" -> groupId,
> "schema.registry.url" -> schemaRegistryUrl,
> "auto.offset.reset" -> offset
> )
> val inputStream = KafkaUtils.createDirectStream[Object, Object](
> ssc,
> PreferConsistent,
> Subscribe[Object, Object]
> (kafkaTopic, kafkaParams)
> )
> val windowStream = inputStream.map(_.toString).window(Seconds(180),
> Seconds(30))
> windowStream.foreachRDD{
> rdd => {
> val filtered = rdd.filter(_.contains("idb"))
> filtered.foreach(
> message => {
> var i = 0
> if (i == 0) {
> logger.info(message)
> i = i + 1
> }
> }
> )
> }
> }
> {code}
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
