I am pretty sure I got those changes with the jar I compiled (I pulled from
master on 8/8 and it looks like SPARK-18057 was resolved on 8/3) but no luck,
here is a copy-paste of the error I’m seeing. The semantics for Event Hubs’
Kafka head is highlighted for reference – we connect to port 9093 on a FQDN
instead of port 9092 on a Kafka broker’s IP address, but I don’t think that
should change anything.
18/08/20 22:29:13 INFO AbstractCoordinator: Discovered coordinator <broker
FQDN>:9093 (id: 2147483647 rack: null) for group
spark-kafka-source-1aa50598-99d1-4c53-a73c-fa6637a219b2--1338794993-driver-0.
18/08/20 22:29:13 INFO AbstractCoordinator: (Re-)joining group
spark-kafka-source-1aa50598-99d1-4c53-a73c-fa6637a219b2--1338794993-driver-0
18/08/20 22:29:33 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
kafkashaded.org.apache.kafka.common.network.SslTransportLayer.flush(SslTransportLayer.java:195)
at
kafkashaded.org.apache.kafka.common.network.SslTransportLayer.close(SslTransportLayer.java:163)
at
kafkashaded.org.apache.kafka.common.utils.Utils.closeAll(Utils.java:690)
at
kafkashaded.org.apache.kafka.common.network.KafkaChannel.close(KafkaChannel.java:47)
at
kafkashaded.org.apache.kafka.common.network.Selector.close(Selector.java:471)
at
kafkashaded.org.apache.kafka.common.network.Selector.pollSelectionKeys(Selector.java:348)
at
kafkashaded.org.apache.kafka.common.network.Selector.poll(Selector.java:283)
at
kafkashaded.org.apache.kafka.clients.NetworkClient.poll(NetworkClient.java:260)
at
kafkashaded.org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.clientPoll(ConsumerNetworkClient.java:360)
at
kafkashaded.org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:224)
at
kafkashaded.org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:192)
at
kafkashaded.org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:163)
at
kafkashaded.org.apache.kafka.clients.consumer.internals.AbstractCoordinator.ensureActiveGroup(AbstractCoordinator.java:243)
at
kafkashaded.org.apache.kafka.clients.consumer.internals.ConsumerCoordinator.ensurePartitionAssignment(ConsumerCoordinator.java:366)
at
kafkashaded.org.apache.kafka.clients.consumer.KafkaConsumer.pollOnce(KafkaConsumer.java:978)
at
kafkashaded.org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:938)
at
org.apache.spark.sql.kafka010.KafkaOffsetReader$$anonfun$fetchLatestOffsets$1$$anonfun$apply$9.apply(KafkaOffsetReader.scala:214)
at
org.apache.spark.sql.kafka010.KafkaOffsetReader$$anonfun$fetchLatestOffsets$1$$anonfun$apply$9.apply(KafkaOffsetReader.scala:212)
at
org.apache.spark.sql.kafka010.KafkaOffsetReader$$anonfun$org$apache$spark$sql$kafka010$KafkaOffsetReader$$withRetriesWithoutInterrupt$1.apply$mcV$sp(KafkaOffsetReader.scala:303)
at
org.apache.spark.sql.kafka010.KafkaOffsetReader$$anonfun$org$apache$spark$sql$kafka010$KafkaOffsetReader$$withRetriesWithoutInterrupt$1.apply(KafkaOffsetReader.scala:302)
at
org.apache.spark.sql.kafka010.KafkaOffsetReader$$anonfun$org$apache$spark$sql$kafka010$KafkaOffsetReader$$withRetriesWithoutInterrupt$1.apply(KafkaOffsetReader.scala:302)
at
org.apache.spark.util.UninterruptibleThread.runUninterruptibly(UninterruptibleThread.scala:77)
at
org.apache.spark.sql.kafka010.KafkaOffsetReader.org$apache$spark$sql$kafka010$KafkaOffsetReader$$withRetriesWithoutInterrupt(KafkaOffsetReader.scala:301)
at
org.apache.spark.sql.kafka010.KafkaOffsetReader$$anonfun$fetchLatestOffsets$1.apply(KafkaOffsetReader.scala:212)
at
org.apache.spark.sql.kafka010.KafkaOffsetReader$$anonfun$fetchLatestOffsets$1.apply(KafkaOffsetReader.scala:212)
at
org.apache.spark.sql.kafka010.KafkaOffsetReader.runUninterruptibly(KafkaOffsetReader.scala:270)
at
org.apache.spark.sql.kafka010.KafkaOffsetReader.fetchLatestOffsets(KafkaOffsetReader.scala:211)
at
org.apache.spark.sql.kafka010.KafkaMicroBatchReader$$anonfun$getOrCreateInitialPartitionOffsets$1.apply(KafkaMicroBatchReader.scala:212)
at
org.apache.spark.sql.kafka010.KafkaMicroBatchReader$$anonfun$getOrCreateInitialPartitionOffsets$1.apply(KafkaMicroBatchReader.scala:207)
at scala.Option.getOrElse(Option.scala:121)
at
org.apache.spark.sql.kafka010.KafkaMicroBatchReader.getOrCreateInitialPartitionOffsets(KafkaMicroBatchReader.scala:207)
at
org.apache.spark.sql.kafka010.KafkaMicroBatchReader.org$apache$spark$sql$kafka010$KafkaMicroBatchReader$$initialPartitionOffsets$lzycompute(KafkaMicroBatchReader.scala:82)
at
org.apache.spark.sql.kafka010.KafkaMicroBatchReader.org$apache$spark$sql$kafka010$KafkaMicroBatchReader$$initialPartitionOffsets(KafkaMicroBatchReader.scala:82)
at
org.apache.spark.sql.kafka010.KafkaMicroBatchReader.setOffsetRange(KafkaMicroBatchReader.scala:89)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$constructNextBatch$1$$anonfun$6$$anonfun$apply$2.apply$mcV$sp(MicroBatchExecution.scala:364)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$constructNextBatch$1$$anonfun$6$$anonfun$apply$2.apply(MicroBatchExecution.scala:358)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$constructNextBatch$1$$anonfun$6$$anonfun$apply$2.apply(MicroBatchExecution.scala:358)
at
org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:379)
at
org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:60)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$constructNextBatch$1$$anonfun$6.apply(MicroBatchExecution.scala:353)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$constructNextBatch$1$$anonfun$6.apply(MicroBatchExecution.scala:340)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at
scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at
scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at
scala.collection.AbstractTraversable.map(Traversable.scala:104)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$constructNextBatch$1.apply$mcZ$sp(MicroBatchExecution.scala:340)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$constructNextBatch$1.apply(MicroBatchExecution.scala:336)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$constructNextBatch$1.apply(MicroBatchExecution.scala:336)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution.withProgressLocked(MicroBatchExecution.scala:563)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$constructNextBatch(MicroBatchExecution.scala:336)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:189)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:172)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:172)
at
org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:379)
at
org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:60)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:172)
at
org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at
org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:166)
at
org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:293)
at
org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:203)
18/08/20 22:29:33 INFO AbstractCoordinator: Marking the coordinator <broker
FQDN>:9093 (id: 2147483647 rack: null) dead for group
spark-kafka-source-1aa50598-99d1-4c53-a73c-fa6637a219b2--1338794993-driver-0
18/08/20 22:29:34 INFO AbstractCoordinator: Discovered coordinator <broker
FQDN>:9093 (id: 2147483647 rack: null) for group
spark-kafka-source-1aa50598-99d1-4c53-a73c-fa6637a219b2--1338794993-driver-0.
<repeat this error over and over until the job is terminated>
Also, I’m not sure if it’s relevant but I am running on Databricks (currently
working on running it on a local cluster to verify that it isn’t a Databricks
issue). The only jars I’m using are the Spark-Kafka connector from github
master on 8/8/18 and Kafka v2.0. Thanks so much for your help, let me know if
there’s anything else I can provide
Sincerely,
Basil
From: Ted Yu <[email protected]>
Sent: Friday, August 17, 2018 4:20 PM
To: [email protected]
Cc: dev <[email protected]>
Subject: Re: Spark Kafka adapter questions
If you have picked up all the changes for SPARK-18057, the Kafka “broker”
supporting v1.0+ should be compatible with Spark's Kafka adapter.
Can you post more details about the “failed to send SSL close message” errors ?
(The default Kafka version is 2.0.0 in Spark Kafka adapter after SPARK-18057)
Thanks
On Fri, Aug 17, 2018 at 3:53 PM Basil Hariri
<[email protected]<mailto:[email protected]>>
wrote:
Hi all,
I work on Azure Event Hubs (Microsoft’s PaaS offering similar to Apache Kafka)
and am trying to get our new Kafka
head<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fazure.microsoft.com%2Fen-us%2Fblog%2Fazure-event-hubs-for-kafka-ecosystems-in-public-preview%2F&data=02%7C01%7CBasil.Hariri%40microsoft.com%7C9c2387763e53418d4b4e08d6049813a9%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C636701448547293693&sdata=kNuSO1yNNJzOOyg%2FDRlyv4ZKB568f%2FKKn0zCnWQDK0A%3D&reserved=0>
to play nice with Spark’s Kafka adapter. The goal is for our Kafka endpoint to
be completely compatible with Spark’s Kafka adapter, but I’m running into some
issues that I think are related to versioning. I’ve been trying to tinker with
the
kafka-0-10-sql<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fapache%2Fspark%2Ftree%2Fmaster%2Fexternal%2Fkafka-0-10-sql&data=02%7C01%7CBasil.Hariri%40microsoft.com%7C9c2387763e53418d4b4e08d6049813a9%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C636701448547293693&sdata=s5BoYXcUhrVb5uaj3Y2soxjn8Zm3LFVOyGD8bwDZkkM%3D&reserved=0>
and
kafka-0-10<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fapache%2Fspark%2Ftree%2Fmaster%2Fexternal%2Fkafka-0-10-sql&data=02%7C01%7CBasil.Hariri%40microsoft.com%7C9c2387763e53418d4b4e08d6049813a9%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C636701448547303703&sdata=5H9%2FFGxz1VsL0OfWx7mrsQU2cGIR7zB3VuMADZop9RE%3D&reserved=0>
adapters on Github and was wondering if someone could take a second to point
me in the right direction with:
1. What is the difference between those two adapters? My hunch is that
kafka-0-10-sql supports structured streaming while kafka-10-0 still uses Spark
streaming, but I haven’t found anything to verify that.
2. Event Hubs’ Kafka endpoint only supports Kafka 1.0 and later, and the
errors I get when trying to connect to Spark (“failed to send SSL close
message” / broken pipe errors) have usually shown up when using Kafka v0.10
applications with our endpoint. I built from source after I saw that both
libraries were updated for Kafka 2.0 support (late last week), but I’m still
running into the same issues. Do Spark’s Kafka adapters generally downgrade to
Kafka v0.10 protocols? If not, is there any other reason to believe that a
Kafka “broker” that doesn’t support v0.10 protocols but supports v1.0+ would be
incompatible with Spark’s Kafka adapter?
Thanks in advance, please let me know if there’s a different place I should be
posting this
Sincerely,
Basil
