The part that works is the commented out, single receiver stream below the
loop. It seems that when I call KafkaUtils.createStream more than once, I
don’t receive any messages.
I’ll dig through the logs, but at first glance yesterday I didn’t see anything
suspect. I’ll have to look closer.
mn
On Sep 23, 2014, at 6:14 PM, Tim Smith <secs...@gmail.com> wrote:
> Maybe post the before-code as in what was the code before you did the
> loop (that worked)? I had similar situations where reviewing code
> before (worked) and after (does not work) helped. Also, what helped is
> the Scala REPL because I can see what are the object types being
> returned by each statement.
>
> Other than code, in the driver logs, you should see events that say
> "Registered receiver for stream 0 from
> akka.tcp://sp...@node5.acme.net:53135"
>
> Now, if you goto "node5" and look at Spark or YarnContainer logs
> (depending on who's doing RM), you should be able to see if the
> receiver has any errors when trying to talk to kafka.
>
>
>
> On Tue, Sep 23, 2014 at 3:21 PM, Matt Narrell <matt.narr...@gmail.com> wrote:
>> To my eyes, these are functionally equivalent. I’ll try a Scala approach,
>> but this may cause waves for me upstream (e.g., non-Java)
>>
>> Thanks for looking at this. If anyone else can see a glaring issue in the
>> Java approach that would be appreciated.
>>
>> Thanks,
>> Matt
>>
>> On Sep 23, 2014, at 4:13 PM, Tim Smith <secs...@gmail.com> wrote:
>>
>>> Sorry, I am almost Java illiterate but here's my Scala code to do the
>>> equivalent (that I have tested to work):
>>>
>>> val kInStreams = (1 to 10).map{_ =>
>>> KafkaUtils.createStream(ssc,zkhost.acme.net:2182,"myGrp",Map("myTopic"
>>> -> 1), StorageLevel.MEMORY_AND_DISK_SER) } //Create 10 receivers
>>> across the cluster, one for each partition, potentially but active
>>> receivers are only as many kafka partitions you have
>>>
>>> val kInMsg =
>>> ssc.union(kInStreams).persist(org.apache.spark.storage.StorageLevel.MEMORY_AND_DISK_SER)
>>>
>>>
>>>
>>>
>>> On Tue, Sep 23, 2014 at 2:20 PM, Matt Narrell <matt.narr...@gmail.com>
>>> wrote:
>>>> So, this is scrubbed some for confidentiality, but the meat of it is as
>>>> follows. Note, that if I substitute the commented section for the loop, I
>>>> receive messages from the topic.
>>>>
>>>> SparkConf sparkConf = new SparkConf();
>>>> sparkConf.set("spark.streaming.unpersist", "true");
>>>> sparkConf.set("spark.logConf", "true");
>>>>
>>>> Map<String, String> kafkaProps = new HashMap<>();
>>>> kafkaProps.put("zookeeper.connect", Constants.ZK_ENSEMBLE + "/kafka");
>>>> kafkaProps.put("group.id", groupId);
>>>>
>>>> JavaStreamingContext jsc = new JavaStreamingContext(sparkConf,
>>>> Seconds.apply(1));
>>>> jsc.checkpoint("hdfs://<some_location>");
>>>>
>>>> List<JavaPairDStream<String, ProtobufModel>> streamList = new
>>>> ArrayList<>(5);
>>>>
>>>> for (int i = 0; i < 5; i++) {
>>>> streamList.add(KafkaUtils.createStream(jsc,
>>>> String.class, ProtobufModel.class,
>>>> StringDecoder.class,
>>>> ProtobufModelDecoder.class,
>>>> kafkaProps,
>>>> Collections.singletonMap(topic,
>>>> 1),
>>>> StorageLevel.MEMORY_ONLY_SER()));
>>>> }
>>>>
>>>> final JavaPairDStream<String, ProtobufModel> stream =
>>>> jsc.union(streamList.get(0), streamList.subList(1, streamList.size()));
>>>>
>>>> // final JavaPairReceiverInputDStream<String, ProtobufModel> stream =
>>>> // KafkaUtils.createStream(jsc,
>>>> // String.class,
>>>> ProtobufModel.class,
>>>> // StringDecoder.class,
>>>> ProtobufModelDecoder.class,
>>>> // kafkaProps,
>>>> //
>>>> Collections.singletonMap(topic, 5),
>>>> //
>>>> StorageLevel.MEMORY_ONLY_SER());
>>>>
>>>> final JavaPairDStream<String, Integer> tuples = stream.mapToPair(
>>>> new PairFunction<Tuple2<String, ProtobufModel>, String, Integer>() {
>>>> @Override
>>>> public Tuple2<String, Integer> call(Tuple2<String,
>>>> ProtobufModel> tuple) throws Exception {
>>>> return new Tuple2<>(tuple._2().getDeviceId(), 1);
>>>> }
>>>> });
>>>>
>>>> … and futher Spark functions ...
>>>>
>>>> On Sep 23, 2014, at 2:55 PM, Tim Smith <secs...@gmail.com> wrote:
>>>>
>>>>> Posting your code would be really helpful in figuring out gotchas.
>>>>>
>>>>> On Tue, Sep 23, 2014 at 9:19 AM, Matt Narrell <matt.narr...@gmail.com>
>>>>> wrote:
>>>>>> Hey,
>>>>>>
>>>>>> Spark 1.1.0
>>>>>> Kafka 0.8.1.1
>>>>>> Hadoop (YARN/HDFS) 2.5.1
>>>>>>
>>>>>> I have a five partition Kafka topic. I can create a single Kafka
>>>>>> receiver
>>>>>> via KafkaUtils.createStream with five threads in the topic map and
>>>>>> consume
>>>>>> messages fine. Sifting through the user list and Google, I see that its
>>>>>> possible to split the Kafka receiver among the Spark workers such that I
>>>>>> can
>>>>>> have a receiver per topic, and have this distributed to workers rather
>>>>>> than
>>>>>> localized to the driver. I’m following something like this:
>>>>>> https://github.com/apache/spark/blob/ae58aea2d1435b5bb011e68127e1bcddc2edf5b2/extras/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java#L132
>>>>>> But for Kafka obviously. From the Streaming Programming Guide “
>>>>>> Receiving
>>>>>> multiple data streams can therefore be achieved by creating multiple
>>>>>> input
>>>>>> DStreams and configuring them to receive different partitions of the data
>>>>>> stream from the source(s)."
>>>>>>
>>>>>> However, I’m not able to consume any messages from Kafka after I perform
>>>>>> the
>>>>>> union operation. Again, if I create a single, multi-threaded, receiver I
>>>>>> can consume messages fine. If I create 5 receivers in a loop, and call
>>>>>> jssc.union(…) i get:
>>>>>>
>>>>>> INFO scheduler.ReceiverTracker: Stream 0 received 0 blocks
>>>>>> INFO scheduler.ReceiverTracker: Stream 1 received 0 blocks
>>>>>> INFO scheduler.ReceiverTracker: Stream 2 received 0 blocks
>>>>>> INFO scheduler.ReceiverTracker: Stream 3 received 0 blocks
>>>>>> INFO scheduler.ReceiverTracker: Stream 4 received 0 blocks
>>>>>>
>>>>>>
>>>>>> Do I need to do anything to the unioned DStream? Am I going about this
>>>>>> incorrectly?
>>>>>>
>>>>>> Thanks in advance.
>>>>>>
>>>>>> Matt
>>>>>
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>>>>
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