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Posted to issues@spark.apache.org by "Pablo Panero (JIRA)" <ji...@apache.org> on 2017/07/18 09:50:00 UTC
[jira] [Commented] (SPARK-21453) Streaming kafka source (structured
spark)
[ https://issues.apache.org/jira/browse/SPARK-21453?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16091353#comment-16091353 ]
Pablo Panero commented on SPARK-21453:
--------------------------------------
[~srowen] could you point me to the mailing list?
I checked further and it starts by a:
{code:java}
17/07/18 11:36:31 ERROR Executor: Exception in task 6.0 in stage 50.0 (TID 2159)
java.lang.IllegalStateException: Cannot send after the producer is closed.
at org.apache.kafka.clients.producer.internals.RecordAccumulator.append(RecordAccumulator.java:174)
at org.apache.kafka.clients.producer.KafkaProducer.doSend(KafkaProducer.java:478)
at org.apache.kafka.clients.producer.KafkaProducer.send(KafkaProducer.java:440)
at org.apache.spark.sql.kafka010.KafkaWriteTask.execute(KafkaWriteTask.scala:65)
at org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$1.apply$mcV$sp(KafkaWriter.scala:91)
at org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$1.apply(KafkaWriter.scala:91)
at org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$1.apply(KafkaWriter.scala:91)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1337)
at org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1.apply(KafkaWriter.scala:91)
at org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1.apply(KafkaWriter.scala:89)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$29.apply(RDD.scala:926)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$29.apply(RDD.scala:926)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
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:748)
{code}
Being it the integration with kafka from spark. But yes, I can start a email therad if you tell me where to :D thanks
> Streaming kafka source (structured spark)
> -----------------------------------------
>
> Key: SPARK-21453
> URL: https://issues.apache.org/jira/browse/SPARK-21453
> Project: Spark
> Issue Type: Bug
> Components: Structured Streaming
> Affects Versions: 2.2.0
> Environment: Spark 2.2.0 and kafka 0.10.2.0
> Reporter: Pablo Panero
> Priority: Minor
>
> On a streaming job using built-in kafka source and sink (over SSL), with I am getting the following exception:
> Config of the source:
> {code:java}
> val df = spark.readStream
> .format("kafka")
> .option("kafka.bootstrap.servers", config.bootstrapServers)
> .option("failOnDataLoss", value = false)
> .option("kafka.connections.max.idle.ms", 3600000)
> //SSL: this only applies to communication between Spark and Kafka brokers; you are still responsible for separately securing Spark inter-node communication.
> .option("kafka.security.protocol", "SASL_SSL")
> .option("kafka.sasl.mechanism", "GSSAPI")
> .option("kafka.sasl.kerberos.service.name", "kafka")
> .option("kafka.ssl.truststore.location", "/etc/pki/java/cacerts")
> .option("kafka.ssl.truststore.password", "changeit")
> .option("subscribe", config.topicConfigList.keys.mkString(","))
> .load()
> {code}
> Config of the sink:
> {code:java}
> .writeStream
> .option("checkpointLocation", s"${config.checkpointDir}/${topicConfig._1}/")
> .format("kafka")
> .option("kafka.bootstrap.servers", config.bootstrapServers)
> .option("kafka.connections.max.idle.ms", 3600000)
> //SSL: this only applies to communication between Spark and Kafka brokers; you are still responsible for separately securing Spark inter-node communication.
> .option("kafka.security.protocol", "SASL_SSL")
> .option("kafka.sasl.mechanism", "GSSAPI")
> .option("kafka.sasl.kerberos.service.name", "kafka")
> .option("kafka.ssl.truststore.location", "/etc/pki/java/cacerts")
> .option("kafka.ssl.truststore.password", "changeit")
> .start()
> {code}
> {code:java}
> 17/07/18 10:11:58 WARN SslTransportLayer: Failed to send SSL Close message
> java.io.IOException: Broken pipe
> at sun.nio.ch.FileDispatcherImpl.write0(Native Method)
> at sun.nio.ch.SocketDispatcher.write(SocketDispatcher.java:47)
> at sun.nio.ch.IOUtil.writeFromNativeBuffer(IOUtil.java:93)
> at sun.nio.ch.IOUtil.write(IOUtil.java:65)
> at sun.nio.ch.SocketChannelImpl.write(SocketChannelImpl.java:471)
> at org.apache.kafka.common.network.SslTransportLayer.flush(SslTransportLayer.java:195)
> at org.apache.kafka.common.network.SslTransportLayer.close(SslTransportLayer.java:163)
> at org.apache.kafka.common.utils.Utils.closeAll(Utils.java:731)
> at org.apache.kafka.common.network.KafkaChannel.close(KafkaChannel.java:54)
> at org.apache.kafka.common.network.Selector.doClose(Selector.java:540)
> at org.apache.kafka.common.network.Selector.close(Selector.java:531)
> at org.apache.kafka.common.network.Selector.pollSelectionKeys(Selector.java:378)
> at org.apache.kafka.common.network.Selector.poll(Selector.java:303)
> at org.apache.kafka.clients.NetworkClient.poll(NetworkClient.java:349)
> at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:226)
> at org.apache.kafka.clients.consumer.KafkaConsumer.pollOnce(KafkaConsumer.java:1047)
> at org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:995)
> at org.apache.spark.sql.kafka010.CachedKafkaConsumer.poll(CachedKafkaConsumer.scala:298)
> at org.apache.spark.sql.kafka010.CachedKafkaConsumer.org$apache$spark$sql$kafka010$CachedKafkaConsumer$$fetchData(CachedKafkaConsumer.scala:206)
> at org.apache.spark.sql.kafka010.CachedKafkaConsumer$$anonfun$get$1.apply(CachedKafkaConsumer.scala:117)
> at org.apache.spark.sql.kafka010.CachedKafkaConsumer$$anonfun$get$1.apply(CachedKafkaConsumer.scala:106)
> at org.apache.spark.util.UninterruptibleThread.runUninterruptibly(UninterruptibleThread.scala:85)
> at org.apache.spark.sql.kafka010.CachedKafkaConsumer.runUninterruptiblyIfPossible(CachedKafkaConsumer.scala:68)
> at org.apache.spark.sql.kafka010.CachedKafkaConsumer.get(CachedKafkaConsumer.scala:106)
> at org.apache.spark.sql.kafka010.KafkaSourceRDD$$anon$1.getNext(KafkaSourceRDD.scala:157)
> at org.apache.spark.sql.kafka010.KafkaSourceRDD$$anon$1.getNext(KafkaSourceRDD.scala:148)
> at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
> at org.apache.spark.sql.kafka010.KafkaWriteTask.execute(KafkaWriteTask.scala:47)
> at org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$1.apply$mcV$sp(KafkaWriter.scala:91)
> at org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$1.apply(KafkaWriter.scala:91)
> at org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1$$anonfun$apply$1.apply(KafkaWriter.scala:91)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1337)
> at org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1.apply(KafkaWriter.scala:91)
> at org.apache.spark.sql.kafka010.KafkaWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1.apply(KafkaWriter.scala:89)
> at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$29.apply(RDD.scala:926)
> at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$29.apply(RDD.scala:926)
> at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
> at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:108)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
> 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:748)
> {code}
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