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From Abraham Jacob <abe.jac...@gmail.com>
Subject Re: Spark Streaming KafkaUtils Issue
Date Sat, 11 Oct 2014 01:34:43 GMT
Ah I see... much clearer now...

Because "auto.offset.reset" will trigger KafkaReciver to delete the ZK
metadata; when the control passes over to Kafka consumer API it will see
that there is no offset available for the partition. This then will trigger
the "smallest" or "largest" logic to execute in kafka, depending on what we
set for "auto.offset.reset"...

Thanks for explaining this clearly! Appreciate your effort.



On Fri, Oct 10, 2014 at 6:08 PM, Shao, Saisai <saisai.shao@intel.com> wrote:

>  Hi abe,
>
>
>
> You can see the details in KafkaInputDStream.scala, here is the snippet
>
>
>
>     // When auto.offset.reset is defined, it is our responsibility to try
> and whack the
>
>     // consumer group zk node.
>
>     if (kafkaParams.contains("auto.offset.reset")) {
>
>      tryZookeeperConsumerGroupCleanup(zkConnect, kafkaParams("group.id"))
>
>     }
>
>
>
> KafkaReceiver will check your kafkaParams, if “auto.offset.reset” is set,
> it will clean ZK metadata immediately, so you will always read data from
> beginning (set to “smallest”) and end (set to “largest”) immediately,
> because the ZK metadata is deleted beforehand.
>
>
>
> If you do not set this parameter, this code path will not be triggered, so
> data will be read from the last commit point. And if last commit point is
> not yet available, Kafka will move the offset to the end of partition
> (Kafka is set “auto.commit.offset” to “largest” by default).
>
>
>
> If you want to keep the same semantics as Kafka, you need to remove the
> above code path manually and recompile the Spark.
>
>
>
> Thanks
>
> Jerry
>
>
>
> *From:* Abraham Jacob [mailto:abe.jacobs@gmail.com]
> *Sent:* Saturday, October 11, 2014 8:49 AM
> *To:* Shao, Saisai
> *Cc:* user@spark.apache.org; Sean McNamara
>
> *Subject:* Re: Spark Streaming KafkaUtils Issue
>
>
>
> Thanks Jerry, So, from what I can understand from the code, if I leave out
> "auto.offset.reset", it should theoretically read from the last commit
> point... Correct?
>
>
>
> -abe
>
>
>
> On Fri, Oct 10, 2014 at 5:40 PM, Shao, Saisai <saisai.shao@intel.com>
> wrote:
>
>  Hi Abraham,
>
>
>
> You are correct, the “auto.offset.reset“ behavior in KafkaReceiver is
> different from original Kafka’s semantics, if you set this configure,
> KafkaReceiver will clean the related immediately, but for Kafka this
> configuration is just a hint which will be effective only when offset is
> out-of-range. So you will always read data from the beginning as you set to
> “smallest”, otherwise if you set to “largest”, you will always get data
> from the end immediately.
>
>
>
> There’s a JIRA and PR to follow this, but still not merged to the master,
> you can check to see it (https://issues.apache.org/jira/browse/SPARK-2492
> ).
>
>
>
> Thanks
>
> Jerry
>
>
>
> *From:* Abraham Jacob [mailto:abe.jacobs@gmail.com]
> *Sent:* Saturday, October 11, 2014 6:57 AM
> *To:* Sean McNamara
> *Cc:* user@spark.apache.org
> *Subject:* Re: Spark Streaming KafkaUtils Issue
>
>
>
> Probably this is the issue -
>
>
>
>
> http://www.michael-noll.com/blog/2014/10/01/kafka-spark-streaming-integration-example-tutorial/
>
>
>
>
>
> ·        Spark’s usage of the Kafka consumer parameter auto.offset.reset
> <http://kafka.apache.org/documentation.html#consumerconfigs> is different
> from Kafka’s semantics. In Kafka, the behavior of setting
> auto.offset.reset to “smallest” is that the consumer will automatically
> reset the offset to the smallest offset when a) there is no existing offset
> stored in ZooKeeper or b) there is an existing offset but it is out of
> range. Spark however will *always* remove existing offsets and then start
> all the way from zero again. This means whenever you restart your
> application with auto.offset.reset = "smallest", your application will
> completely re-process all available Kafka data. Doh! See this discussion
> <http://apache-spark-user-list.1001560.n3.nabble.com/spark-streaming-and-the-spark-shell-tp3347p3387.html>
> and that discussion <http://markmail.org/message/257a5l3oqyftsjxj>.
>
>
>
> Hmm.... interesting... Wondering what happens if I set it as largest...?
>
>
>
>
>
> On Fri, Oct 10, 2014 at 3:47 PM, Abraham Jacob <abe.jacobs@gmail.com>
> wrote:
>
>  Sure... I do set the group.id for all the consumers to be the same. Here
> is the code ---
>
>
>
>                 SparkConf sparkConf = new
> SparkConf().setMaster("yarn-cluster").setAppName("Streaming WordCount");
>
> sparkConf.set("spark.shuffle.manager", "SORT");
>
> sparkConf.set("spark.streaming.unpersist", "true");
>
> JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new
> Duration(1000));
>
> Map<String, String> kafkaConf = new HashMap<String, String>();
>
> kafkaConf.put("zookeeper.connect", zookeeper);
>
> kafkaConf.put("group.id", consumerGrp);
>
> kafkaConf.put("auto.offset.reset", "smallest");
>
> kafkaConf.put("zookeeper.conection.timeout.ms", "1000");
>
> kafkaConf.put("rebalance.max.retries", "4");
>
> kafkaConf.put("rebalance.backoff.ms", "3000");
>
> Map<String, Integer> topicMap = new HashMap<String, Integer>();
>
> topicMap.put(topic, 1);
>
> List<JavaPairDStream<byte[], String>> kafkaStreams = new
> ArrayList<JavaPairDStream<byte[], String>>();
>
> for(int i = 0; i < numPartitions; i++) {
>
> kafkaStreams.add(KafkaUtils.createStream(jssc, byte[].class, String.class,
>
> DefaultDecoder.class, PayloadDeSerializer.class,
>
> kafkaConf, topicMap, StorageLevel.MEMORY_ONLY_SER()).mapToPair(new
> PairFunction<Tuple2<byte[],String>, byte[], String>() {
>
>
>
> private static final long serialVersionUID = -1936810126415608167L;
>
>
>
> public Tuple2<byte[], String> call(Tuple2<byte[], String> tuple2) throws
> Exception {
>
> return tuple2;
>
> }
>
> }
>
> )
>
> );
>
> }
>
>
>
>
>
>                 JavaPairDStream<byte[], String> unifiedStream;
>
> if (kafkaStreams.size() > 1) {
>
> unifiedStream = jssc.union(kafkaStreams.get(0), kafkaStreams.subList(1,
> kafkaStreams.size()));
>
> } else {
>
> unifiedStream = kafkaStreams.get(0);
>
> }
>
> unifiedStream.print();
>
> jssc.start();
>
> jssc.awaitTermination();
>
>
>
>
>
> -abe
>
>
>
>
>
> On Fri, Oct 10, 2014 at 3:37 PM, Sean McNamara <
> Sean.McNamara@webtrends.com> wrote:
>
> Would you mind sharing the code leading to your createStream?  Are you
> also setting group.id?
>
> Thanks,
>
> Sean
>
>
>
> On Oct 10, 2014, at 4:31 PM, Abraham Jacob <abe.jacobs@gmail.com> wrote:
>
> > Hi Folks,
> >
> > I am seeing some strange behavior when using the Spark Kafka connector
> in Spark streaming.
> >
> > I have a Kafka topic which has 8 partitions. I have a kafka producer
> that pumps some messages into this topic.
> >
> > On the consumer side I have a spark streaming application that that has
> 8 executors on 8 worker nodes and 8 ReceiverInputDStream with the same
> kafka group id connected to the 8 partitions I have for the topic. Also the
> kafka consumer property "auto.offset.reset" is set to "smallest".
> >
> >
> > Now here is the sequence of steps -
> >
> > (1) I Start the the spark streaming app.
> > (2) Start the producer.
> >
> > As this point I see the messages that are being pumped from the producer
> in Spark Streaming.  Then I -
> >
> > (1) Stopped the producer
> > (2) Wait for all the message to be consumed.
> > (2) Stopped the spark streaming app.
> >
> > Now when I restart the spark streaming app (note - the producer is still
> down and no messages are being pumped into the topic) - I observe the
> following -
> >
> > (1) Spark Streaming starts reading from each partition right from the
> beginning.
> >
> >
> > This is not what I was expecting. I was expecting the consumers started
> by spark streaming to start from where it left off....
> >
> > Is my assumption not correct that "the consumers (the kafka/spark
> connector) to start reading from the topic where it last left off."..?
> >
> > Has anyone else seen this behavior? Is there a way to make it such that
> it starts from where it left off?
> >
> > Regards,
> > - Abraham
>
>
>
>
>
> --
> ~
>
>
>
>
>
> --
> ~
>
>
>
>
>
> --
> ~
>



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