Thank you very much for great answer!
--
Яндекс.Почта — надёжная почта
http://mail.yandex.ru/neo2/collect/?exp=1&t=1
08.09.2015, 23:53, "Cody Koeninger" <c...@koeninger.org>:
> Yeah, that's the general idea.
>
> When you say hard code topic name, do you mean Set(topicA, topicB, topicB) ?
> You should be able to use a variable for that - read it from a config file,
> whatever.
>
> If you're talking about the match statement, yeah you'd need to hardcode your
> cases.
>
> On Tue, Sep 8, 2015 at 3:49 PM, Понькин Алексей <alexey.pon...@ya.ru> wrote:
>> Ok. I got it!
>> But it seems that I need to hard code topic name.
>>
>> something like that?
>>
>> val source = KafkaUtils.createDirectStream[Array[Byte], Array[Byte],
>> DefaultDecoder, DefaultDecoder](
>> ssc, kafkaParams, Set(topicA, topicB, topicB))
>> .transform{ rdd =>
>> val offsetRange = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
>> rdd.mapPartitionsWithIndex(
>> (idx: Int, itr: Iterator[(Array[Byte], Array[Byte])]) =>
>> offsetRange(idx).topic match {
>> case "topicA" => ...
>> case "topicB" => ...
>> case _ => ....
>> }
>> )
>> }
>>
>> 08.09.2015, 19:21, "Cody Koeninger" <c...@koeninger.org>:
>>> That doesn't really matter. With the direct stream you'll get all objects
>>> for a given topicpartition in the same spark partition. You know what
>>> topic it's from via hasOffsetRanges. Then you can deserialize
>>> appropriately based on topic.
>>>
>>> On Tue, Sep 8, 2015 at 11:16 AM, Понькин Алексей <alexey.pon...@ya.ru>
>>> wrote:
>>>> The thing is, that these topics contain absolutely different AVRO
>>>> objects(Array[Byte]) that I need to deserialize to different Java(Scala)
>>>> objects, filter and then map to tuple (String, String). So i have 3
>>>> streams with different avro object in there. I need to cast them(using
>>>> some business rules) to pairs and unite.
>>>>
>>>> --
>>>> Яндекс.Почта — надёжная почта
>>>> http://mail.yandex.ru/neo2/collect/?exp=1&t=1
>>>>
>>>> 08.09.2015, 19:11, "Cody Koeninger" <c...@koeninger.org>:
>>>>
>>>>> I'm not 100% sure what's going on there, but why are you doing a union in
>>>>> the first place?
>>>>>
>>>>> If you want multiple topics in a stream, just pass them all in the set of
>>>>> topics to one call to createDirectStream
>>>>>
>>>>> On Tue, Sep 8, 2015 at 10:52 AM, Alexey Ponkin <alexey.pon...@ya.ru>
>>>>> wrote:
>>>>>> Ok.
>>>>>> Spark 1.4.1 on yarn
>>>>>>
>>>>>> Here is my application
>>>>>> I have 4 different Kafka topics(different object streams)
>>>>>>
>>>>>> type Edge = (String,String)
>>>>>>
>>>>>> val a = KafkaUtils.createDirectStream[...](sc,"A",params).filter(
>>>>>> nonEmpty ).map( toEdge )
>>>>>> val b = KafkaUtils.createDirectStream[...](sc,"B",params).filter(
>>>>>> nonEmpty ).map( toEdge )
>>>>>> val c = KafkaUtils.createDirectStream[...](sc,"C",params).filter(
>>>>>> nonEmpty ).map( toEdge )
>>>>>>
>>>>>> val u = a union b union c
>>>>>>
>>>>>> val source = u.window(Seconds(600), Seconds(10))
>>>>>>
>>>>>> val z = KafkaUtils.createDirectStream[...](sc,"Z",params).filter(
>>>>>> nonEmpty ).map( toEdge )
>>>>>>
>>>>>> val joinResult = source.rightOuterJoin( z )
>>>>>> joinResult.foreachRDD { rdd=>
>>>>>> rdd.foreachPartition { partition =>
>>>>>> .... // save to result topic in kafka
>>>>>> }
>>>>>> }
>>>>>>
>>>>>> The 'window' function in the code above is constantly growing,
>>>>>> no matter how many events appeared in corresponding kafka topics
>>>>>>
>>>>>> but if I change one line from
>>>>>>
>>>>>> val source = u.window(Seconds(600), Seconds(10))
>>>>>>
>>>>>> to
>>>>>>
>>>>>> val partitioner = ssc.sparkContext.broadcast(new HashPartitioner(8))
>>>>>>
>>>>>> val source = u.transform(_.partitionBy(partitioner.value)
>>>>>> ).window(Seconds(600), Seconds(10))
>>>>>>
>>>>>> Everything works perfect.
>>>>>>
>>>>>> Perhaps the problem was in WindowedDStream
>>>>>>
>>>>>> I forced to use PartitionerAwareUnionRDD( partitionBy the same
>>>>>> partitioner ) instead of UnionRDD.
>>>>>>
>>>>>> Nonetheless I did not see any hints about such a bahaviour in doc.
>>>>>> Is it a bug or absolutely normal behaviour?
>>>>>>
>>>>>> 08.09.2015, 17:03, "Cody Koeninger" <c...@koeninger.org>:
>>>>>>
>>>>>>> Can you provide more info (what version of spark, code example)?
>>>>>>>
>>>>>>> On Tue, Sep 8, 2015 at 8:18 AM, Alexey Ponkin <alexey.pon...@ya.ru>
>>>>>>> wrote:
>>>>>>>> Hi,
>>>>>>>>
>>>>>>>> I have an application with 2 streams, which are joined together.
>>>>>>>> Stream1 - is simple DStream(relativly small size batch chunks)
>>>>>>>> Stream2 - is a windowed DStream(with duration for example 60 seconds)
>>>>>>>>
>>>>>>>> Stream1 and Stream2 are Kafka direct stream.
>>>>>>>> The problem is that according to logs window operation is constantly
>>>>>>>> increasing(<a
>>>>>>>> href="http://en.zimagez.com/zimage/screenshot2015-09-0816-06-55.php";>screen</a>).
>>>>>>>> And also I see gap in pocessing window(<a
>>>>>>>> href="http://en.zimagez.com/zimage/screenshot2015-09-0816-10-22.php";>screen</a>)
>>>>>>>> in logs there are no events in that period.
>>>>>>>> So what is happen in that gap and why window is constantly insreasing?
>>>>>>>>
>>>>>>>> Thank you in advance
>>>>>>>>
>>>>>>>> ---------------------------------------------------------------------
>>>>>>>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
>>>>>>>> For additional commands, e-mail: user-h...@spark.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
For additional commands, e-mail: user-h...@spark.apache.org
