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From Chen Song <chen.song...@gmail.com>
Subject Re: spark streaming with kafka reset offset
Date Wed, 15 Jul 2015 03:18:12 GMT
Thanks TD.

As for 1), if timing is not guaranteed, how does exactly once semantics
supported? It feels like exactly once receiving is not necessarily exactly
once processing.

Chen

On Tue, Jul 14, 2015 at 10:16 PM, Tathagata Das <tdas@databricks.com> wrote:

>
>
> On Tue, Jul 14, 2015 at 6:42 PM, Chen Song <chen.song.82@gmail.com> wrote:
>
>> Thanks TD and Cody. I saw that.
>>
>> 1. By doing that (foreachRDD), does KafkaDStream checkpoints its offsets
>> on HDFS at the end of each batch interval?
>>
>
> The timing is not guaranteed.
>
>
>> 2. In the code, if I first apply transformations and actions on the
>> directKafkaStream and then use foreachRDD on the original KafkaDStream to
>> commit offsets myself, will offsets commits always happen after
>> transformation and action?
>>
>> What do you mean by "original KafkaDStream"? if you meant the
> directKafkaStream? If yes, then yes, output operations like foreachRDD is
> executed in each batch in the same order as they are defined.
>
> dstream1.foreachRDD { rdd => func1(rdd) }
> dstream2.foreachRDD { rdd => func2(rdd) }
>
> In every batch interval, func1 will be executed before func2.
>
>
>
>
>> Chen
>>
>> On Tue, Jul 14, 2015 at 6:43 PM, Tathagata Das <tdas@databricks.com>
>> wrote:
>>
>>> Relevant documentation -
>>> https://spark.apache.org/docs/latest/streaming-kafka-integration.html,
>>> towards the end.
>>>
>>> directKafkaStream.foreachRDD { rdd =>
>>>      val offsetRanges = rdd.asInstanceOf[HasOffsetRanges]
>>>      // offsetRanges.length = # of Kafka partitions being consumed
>>>      ...
>>>  }
>>>
>>>
>>> On Tue, Jul 14, 2015 at 3:17 PM, Cody Koeninger <cody@koeninger.org>
>>> wrote:
>>>
>>>> You have access to the offset ranges for a given rdd in the stream by
>>>> typecasting to HasOffsetRanges.  You can then store the offsets wherever
>>>> you need to.
>>>>
>>>> On Tue, Jul 14, 2015 at 5:00 PM, Chen Song <chen.song.82@gmail.com>
>>>> wrote:
>>>>
>>>>> A follow up question.
>>>>>
>>>>> When using createDirectStream approach, the offsets are checkpointed
>>>>> to HDFS and it is understandable by Spark Streaming job. Is there a way
to
>>>>> expose the offsets via a REST api to end users. Or alternatively, is
there
>>>>> a way to have offsets committed to Kafka Offset Manager so users can
query
>>>>> from a consumer programmatically?
>>>>>
>>>>> Essentially, all I need to do is monitor the progress of data
>>>>> consumption of the Kafka topic.
>>>>>
>>>>>
>>>>> On Tue, Jun 30, 2015 at 9:39 AM, Cody Koeninger <cody@koeninger.org>
>>>>> wrote:
>>>>>
>>>>>> You can't use different versions of spark in your application vs
your
>>>>>> cluster.
>>>>>>
>>>>>> For the direct stream, it's not 60 partitions per executor, it's
300
>>>>>> partitions, and executors work on them as they are scheduled.  Yes,
if you
>>>>>> have no messages you will get an empty partition.  It's up to you
whether
>>>>>> it's worthwhile to call coalesce or not.
>>>>>>
>>>>>> On Tue, Jun 30, 2015 at 2:45 AM, Shushant Arora <
>>>>>> shushantarora09@gmail.com> wrote:
>>>>>>
>>>>>>> Is this 3 is no of parallel consumer threads per receiver , means
in
>>>>>>> total we have 2*3=6 consumer in same consumer group consuming
from all 300
>>>>>>> partitions.
>>>>>>> 3 is just parallelism on same receiver and recommendation is
to use
>>>>>>> 1 per receiver since consuming from kafka is not cpu bound rather
>>>>>>> NIC(network bound)  increasing consumer thread on one receiver
won't make
>>>>>>> it parallel in ideal sense ?
>>>>>>>
>>>>>>> In non receiver based consumer spark 1.3 If I use 5 execuots
and
>>>>>>> kafka topic has 300 partions , does kafkaRDD created on 5 executors
will
>>>>>>> have 60 partitions per executor (total 300 one to one mapping)
and if some
>>>>>>> of kafka partitions are empty say offset of last checkpoint to
current is
>>>>>>> same for partitons P123, still it will create empty partition
in kafkaRDD ?
>>>>>>> So we should call coalesce on kafkaRDD ?
>>>>>>>
>>>>>>>
>>>>>>> And is there any incompatibity issue when I include
>>>>>>> spark-streaming_2.10 (version 1.3) and spark-core_2.10(version
1.3) in my
>>>>>>> application but my cluster has spark version 1.2 ?
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Mon, Jun 29, 2015 at 7:56 PM, Shushant Arora <
>>>>>>> shushantarora09@gmail.com> wrote:
>>>>>>>
>>>>>>>> 1. Here you are basically creating 2 receivers and asking
each of
>>>>>>>> them to consume 3 kafka partitions each.
>>>>>>>>
>>>>>>>> - In 1.2 we have high level consumers so how can we restrict
no of
>>>>>>>> kafka partitions to consume from? Say I have 300 kafka partitions
in kafka
>>>>>>>> topic and as in above I gave 2 receivers and 3 kafka partitions
. Then is
>>>>>>>> it mean I will read from 6 out of 300 partitions only and
for rest 294
>>>>>>>> partitions data is lost?
>>>>>>>>
>>>>>>>>
>>>>>>>> 2.One more doubt in spark streaming how is it decided which
part of
>>>>>>>> main function of driver will run at each batch interval ?
Since whole code
>>>>>>>> is written in one function(main function in driver) so how
it determined
>>>>>>>> kafka streams receivers  not to be registered in each batch
only processing
>>>>>>>> to be done .
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> On Mon, Jun 29, 2015 at 7:35 PM, ayan guha <guha.ayan@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi
>>>>>>>>>
>>>>>>>>> Let me take ashot at your questions. (I am sure people
like Cody
>>>>>>>>> and TD will correct if I am wrong)
>>>>>>>>>
>>>>>>>>> 0. This is exact copy from the similar question in mail
thread
>>>>>>>>> from Akhil D:
>>>>>>>>> Since you set local[4] you will have 4 threads for your
>>>>>>>>> computation, and since you are having 2 receivers, you
are left
>>>>>>>>> with 2 threads to process ((0 + 2) <-- This 2 is your
2 threads.)
>>>>>>>>> And the other /2 means you are having 2 tasks in that
stage (with
>>>>>>>>> id 0).
>>>>>>>>>
>>>>>>>>> 1. Here you are basically creating 2 receivers and asking
each of
>>>>>>>>> them to consume 3 kafka partitions each.
>>>>>>>>> 2. How does that matter? It depends on how many receivers
you have
>>>>>>>>> created to consume that data and if you have repartitioned
it. Remember,
>>>>>>>>> spark is lazy and executors are relted to the context
>>>>>>>>> 3. I think in java, factory method is fixed. You just
pass around
>>>>>>>>> the contextFactory object. (I love python :) see the
signature isso much
>>>>>>>>> cleaner :) )
>>>>>>>>> 4. Yes, if you use spark checkpointing. You can use yourcustom
>>>>>>>>> check pointing too.
>>>>>>>>>
>>>>>>>>> Best
>>>>>>>>> Ayan
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Mon, Jun 29, 2015 at 4:02 AM, Shushant Arora <
>>>>>>>>> shushantarora09@gmail.com> wrote:
>>>>>>>>>
>>>>>>>>>> Few doubts :
>>>>>>>>>>
>>>>>>>>>> In 1.2 streaming when I use union of streams , my
streaming
>>>>>>>>>> application getting hanged sometimes and nothing
gets printed on driver.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> [Stage 2:>
>>>>>>>>>>
>>>>>>>>>>                                             (0 +
2) / 2]
>>>>>>>>>>  Whats is 0+2/2 here signifies.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> 1.Does no of streams in topicsMap.put("testSparkPartitioned",
3);
>>>>>>>>>> be same as numstreams=2 ? in unioned stream ?
>>>>>>>>>>
>>>>>>>>>> 2. I launched app on yarnRM with num-executors as
5 . It created
>>>>>>>>>> 2 receivers and 5 execuots . As in stream receivers
nodes get fixed at
>>>>>>>>>> start of app throughout its lifetime . Does executors
gets allicated at
>>>>>>>>>> start of each job on 1s batch interval? If yes, how
does its fast to
>>>>>>>>>> allocate resources. I mean if i increase num-executors
to 50 , it will
>>>>>>>>>> negotiate 50 executors from yarnRM at start of each
job so does it takes
>>>>>>>>>> more time in allocating executors than batch interval(here
1s , say if
>>>>>>>>>> 500ms).? Can i fixed processing executors also throughout
the app?
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> SparkConf conf = new
>>>>>>>>>> SparkConf().setAppName("SampleSparkStreamingApp");
>>>>>>>>>> JavaStreamingContext jssc = new
>>>>>>>>>> JavaStreamingContext(conf,Durations.milliseconds(1000));
>>>>>>>>>>
>>>>>>>>>> Map<String,String> kafkaParams = new HashMap<String,
String>();
>>>>>>>>>> kafkaParams.put("zookeeper.connect","ipadd:2181");
>>>>>>>>>> kafkaParams.put("group.id", "testgroup");
>>>>>>>>>> kafkaParams.put("zookeeper.session.timeout.ms", "10000");
>>>>>>>>>>  Map<String,Integer> topicsMap = new HashMap<String,Integer>();
>>>>>>>>>> topicsMap.put("testSparkPartitioned", 3);
>>>>>>>>>> int numStreams = 2;
>>>>>>>>>> List<JavaPairDStream<byte[],byte[]>>
kafkaStreams = new
>>>>>>>>>> ArrayList<JavaPairDStream<byte[], byte[]>>();
>>>>>>>>>>   for(int i=0;i<numStreams;i++){
>>>>>>>>>>  kafkaStreams.add(KafkaUtils.createStream(jssc, byte[].class,
>>>>>>>>>> byte[].class,kafka.serializer.DefaultDecoder.class
,
>>>>>>>>>> kafka.serializer.DefaultDecoder.class,
>>>>>>>>>> kafkaParams, topicsMap, StorageLevel.MEMORY_ONLY()));
>>>>>>>>>> }
>>>>>>>>>>  JavaPairDStream<byte[], byte[]> directKafkaStream
=
>>>>>>>>>> jssc.union(kafkaStreams.get(0),kafkaStreams.subList(1,
>>>>>>>>>> kafkaStreams.size()));
>>>>>>>>>>  JavaDStream<String> lines = directKafkaStream.map(new
>>>>>>>>>> Function<Tuple2<byte[],byte[]>, String>()
{
>>>>>>>>>>
>>>>>>>>>> public String call(Tuple2<byte[], byte[]> arg0)
throws Exception {
>>>>>>>>>> ...processing
>>>>>>>>>> ..return msg;
>>>>>>>>>> }
>>>>>>>>>> });
>>>>>>>>>> lines.print();
>>>>>>>>>> jssc.start();
>>>>>>>>>> jssc.awaitTermination();
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> -------------------------------------------------------------------------------------------------------------------------------------------------------
>>>>>>>>>> 3.For avoiding dataloss when we use checkpointing,
and factory
>>>>>>>>>> method to create sparkConytext, is method name fixed
>>>>>>>>>> or we can use any name and how to set in app the
method name to
>>>>>>>>>> be used ?
>>>>>>>>>>
>>>>>>>>>> 4.In 1.3 non receiver based streaming, kafka offset
is not stored
>>>>>>>>>> in zookeeper, is it because of zookeeper is not efficient
for high writes
>>>>>>>>>> and read is not strictly consistent? So
>>>>>>>>>>
>>>>>>>>>>  we use simple Kafka API that does not use Zookeeper
and offsets
>>>>>>>>>> tracked only by Spark Streaming within its checkpoints.
This
>>>>>>>>>> eliminates inconsistencies between Spark Streaming
and Zookeeper/Kafka, and
>>>>>>>>>> so each record is received by Spark Streaming effectively
exactly once
>>>>>>>>>> despite failures.
>>>>>>>>>>
>>>>>>>>>> So we have to call context.checkpoint(hdfsdir)? Or
is it implicit
>>>>>>>>>> checkoint location ? Means does hdfs be used for
small data(just offset?)
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Sat, Jun 27, 2015 at 7:37 PM, Dibyendu Bhattacharya
<
>>>>>>>>>> dibyendu.bhattachary@gmail.com> wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi,
>>>>>>>>>>>
>>>>>>>>>>> There is another option to try for Receiver Based
Low Level
>>>>>>>>>>> Kafka Consumer which is part of Spark-Packages
(
>>>>>>>>>>> http://spark-packages.org/package/dibbhatt/kafka-spark-consumer)
>>>>>>>>>>> . This can be used with WAL as well for end to
end zero data loss.
>>>>>>>>>>>
>>>>>>>>>>> This is also Reliable Receiver and Commit offset
to ZK.  Given
>>>>>>>>>>> the number of Kafka Partitions you have ( >
100) , using High Level Kafka
>>>>>>>>>>> API for Receiver based approach may leads to
issues related Consumer
>>>>>>>>>>> Re-balancing  which is a major issue of Kafka
High Level API.
>>>>>>>>>>>
>>>>>>>>>>> Regards,
>>>>>>>>>>> Dibyendu
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Sat, Jun 27, 2015 at 3:04 PM, Tathagata Das
<
>>>>>>>>>>> tdas@databricks.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> In the receiver based approach, If the receiver
crashes for any
>>>>>>>>>>>> reason (receiver crashed or executor crashed)
the receiver should get
>>>>>>>>>>>> restarted on another executor and should
start reading data from the offset
>>>>>>>>>>>> present in the zookeeper. There is some chance
of data loss which can
>>>>>>>>>>>> alleviated using Write Ahead Logs (see streaming
programming guide for more
>>>>>>>>>>>> details, or see my talk [Slides PDF
>>>>>>>>>>>> <http://www.slideshare.net/SparkSummit/recipes-for-running-spark-streaming-apploications-in-production-tathagata-daspptx>
>>>>>>>>>>>> , Video
>>>>>>>>>>>> <https://www.youtube.com/watch?v=d5UJonrruHk&list=PL-x35fyliRwgfhffEpywn4q23ykotgQJ6&index=4>
>>>>>>>>>>>> ] from last Spark Summit 2015). But that
approach can give
>>>>>>>>>>>> duplicate records. The direct approach gives
exactly-once guarantees, so
>>>>>>>>>>>> you should try it out.
>>>>>>>>>>>>
>>>>>>>>>>>> TD
>>>>>>>>>>>>
>>>>>>>>>>>> On Fri, Jun 26, 2015 at 5:46 PM, Cody Koeninger
<
>>>>>>>>>>>> cody@koeninger.org> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Read the spark streaming guide ad the
kafka integration guide
>>>>>>>>>>>>> for a better understanding of how the
receiver based stream works.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Capacity planning is specific to your
environment and what the
>>>>>>>>>>>>> job is actually doing, youll need to
determine it empirically.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Friday, June 26, 2015, Shushant Arora
<
>>>>>>>>>>>>> shushantarora09@gmail.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> In 1.2 how to handle offset management
after stream
>>>>>>>>>>>>>> application starts in each job .
I should commit offset after job
>>>>>>>>>>>>>> completion manually?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> And what is recommended no of consumer
threads. Say I have
>>>>>>>>>>>>>> 300 partitions in kafka cluster .
Load is ~ 1 million events per
>>>>>>>>>>>>>> second.Each event is of ~500bytes.
Having 5 receivers with 60 partitions
>>>>>>>>>>>>>> each receiver is sufficient for spark
streaming to consume ?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Fri, Jun 26, 2015 at 8:40 PM,
Cody Koeninger <
>>>>>>>>>>>>>> cody@koeninger.org> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The receiver-based kafka createStream
in spark 1.2 uses
>>>>>>>>>>>>>>> zookeeper to store offsets. 
If you want finer-grained control over
>>>>>>>>>>>>>>> offsets, you can update the values
in zookeeper yourself before starting
>>>>>>>>>>>>>>> the job.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> createDirectStream in spark 1.3
is still marked as
>>>>>>>>>>>>>>> experimental, and subject to
change.  That being said, it works better for
>>>>>>>>>>>>>>> me in production than the receiver
based api.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Fri, Jun 26, 2015 at 6:43
AM, Shushant Arora <
>>>>>>>>>>>>>>> shushantarora09@gmail.com>
wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I am using spark streaming
1.2.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> If processing executors get
crashed will receiver rest the
>>>>>>>>>>>>>>>> offset back to last processed
offset?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> If receiver itself got crashed
is there a way to reset the
>>>>>>>>>>>>>>>> offset without restarting
streaming application other than smallest or
>>>>>>>>>>>>>>>> largest.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Is spark streaming 1.3  which
uses low level consumer api,
>>>>>>>>>>>>>>>> stabe? And which is recommended
for handling data  loss 1.2 or 1.3 .
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>> Best Regards,
>>>>>>>>> Ayan Guha
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Chen Song
>>>>>
>>>>>
>>>>
>>>
>>
>>
>> --
>> Chen Song
>>
>>
>


-- 
Chen Song

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