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From Dibyendu Bhattacharya <dibyendu.bhattach...@gmail.com>
Subject Re: Recovery for Spark Streaming Kafka Direct in case of issues with Kafka
Date Thu, 03 Dec 2015 01:13:46 GMT
This consumer which I mentioned does not silently throw away data. If
offset out of range it start for earliest offset and that is correct way of
recovery from this error.

Dibyendu
On Dec 2, 2015 9:56 PM, "Cody Koeninger" <cody@koeninger.org> wrote:

> Again, just to be clear, silently throwing away data because your system
> isn't working right is not the same as "recover from any Kafka leader
> changes and offset out of ranges issue".
>
>
>
> On Tue, Dec 1, 2015 at 11:27 PM, Dibyendu Bhattacharya <
> dibyendu.bhattachary@gmail.com> wrote:
>
>> Hi, if you use Receiver based consumer which is available in
>> spark-packages (
>> http://spark-packages.org/package/dibbhatt/kafka-spark-consumer) , this
>> has all built in failure recovery and it can recover from any Kafka leader
>> changes and offset out of ranges issue.
>>
>> Here is the package form github :
>> https://github.com/dibbhatt/kafka-spark-consumer
>>
>>
>> Dibyendu
>>
>> On Wed, Dec 2, 2015 at 5:28 AM, swetha kasireddy <
>> swethakasireddy@gmail.com> wrote:
>>
>>> How to avoid those Errors with receiver based approach? Suppose we are
>>> OK with at least once processing and use receiver based approach which uses
>>> ZooKeeper but not query Kafka directly, would these errors(Couldn't
>>> find leader offsets for
>>> Set([test_stream,5])))    be avoided?
>>>
>>> On Tue, Dec 1, 2015 at 3:40 PM, Cody Koeninger <cody@koeninger.org>
>>> wrote:
>>>
>>>> KafkaRDD.scala , handleFetchErr
>>>>
>>>> On Tue, Dec 1, 2015 at 3:39 PM, swetha kasireddy <
>>>> swethakasireddy@gmail.com> wrote:
>>>>
>>>>> Hi Cody,
>>>>>
>>>>> How to look at Option 2(see the following)? Which portion of the code
>>>>> in Spark Kafka Direct to look at to handle this issue specific to our
>>>>> requirements.
>>>>>
>>>>>
>>>>> 2.Catch that exception and somehow force things to "reset" for that
>>>>> partition And how would it handle the offsets already calculated in the
>>>>> backlog (if there is one)?
>>>>>
>>>>> On Tue, Dec 1, 2015 at 6:51 AM, Cody Koeninger <cody@koeninger.org>
>>>>> wrote:
>>>>>
>>>>>> If you're consistently getting offset out of range exceptions, it's
>>>>>> probably because messages are getting deleted before you've processed
them.
>>>>>>
>>>>>> The only real way to deal with this is give kafka more retention,
>>>>>> consume faster, or both.
>>>>>>
>>>>>> If you're just looking for a quick "fix" for an infrequent issue,
>>>>>> option 4 is probably easiest.  I wouldn't do that automatically /
silently,
>>>>>> because you're losing data.
>>>>>>
>>>>>> On Mon, Nov 30, 2015 at 6:22 PM, SRK <swethakasireddy@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> So, our Streaming Job fails with the following errors. If you
see
>>>>>>> the errors
>>>>>>> below, they are all related to Kafka losing offsets and
>>>>>>> OffsetOutOfRangeException.
>>>>>>>
>>>>>>> What are the options we have other than fixing Kafka? We would
like
>>>>>>> to do
>>>>>>> something like the following. How can we achieve 1 and 2 with
Spark
>>>>>>> Kafka
>>>>>>> Direct?
>>>>>>>
>>>>>>> 1.Need to see a way to skip some offsets if they are not available
>>>>>>> after the
>>>>>>> max retries are reached..in that case there might be data loss.
>>>>>>>
>>>>>>> 2.Catch that exception and somehow force things to "reset" for
that
>>>>>>> partition And how would it handle the offsets already calculated
in
>>>>>>> the
>>>>>>> backlog (if there is one)?
>>>>>>>
>>>>>>> 3.Track the offsets separately, restart the job by providing
the
>>>>>>> offsets.
>>>>>>>
>>>>>>> 4.Or a straightforward approach would be to monitor the log for
this
>>>>>>> error,
>>>>>>> and if it occurs more than X times, kill the job, remove the
>>>>>>> checkpoint
>>>>>>> directory, and restart.
>>>>>>>
>>>>>>> ERROR DirectKafkaInputDStream:
>>>>>>> ArrayBuffer(kafka.common.UnknownException,
>>>>>>> org.apache.spark.SparkException: Couldn't find leader offsets
for
>>>>>>> Set([test_stream,5]))
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> java.lang.ClassNotFoundException:
>>>>>>> kafka.common.NotLeaderForPartitionException
>>>>>>>
>>>>>>> at
>>>>>>> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1699)
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> java.util.concurrent.RejectedExecutionException: Task
>>>>>>>
>>>>>>> org.apache.spark.streaming.CheckpointWriter$CheckpointWriteHandler@a48c5a8
>>>>>>> rejected from java.util.concurrent.ThreadPoolExecutor@543258e0
>>>>>>> [Terminated,
>>>>>>> pool size = 0, active threads = 0, queued tasks = 0, completed
tasks
>>>>>>> =
>>>>>>> 12112]
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> org.apache.spark.SparkException: Job aborted due to stage failure:
>>>>>>> Task 10
>>>>>>> in stage 52.0 failed 4 times, most recent failure: Lost task
10.3 in
>>>>>>> stage
>>>>>>> 52.0 (TID 255, 172.16.97.97): UnknownReason
>>>>>>>
>>>>>>> Exception in thread "streaming-job-executor-0" java.lang.Error:
>>>>>>> java.lang.InterruptedException
>>>>>>>
>>>>>>> Caused by: java.lang.InterruptedException
>>>>>>>
>>>>>>> java.lang.ClassNotFoundException:
>>>>>>> kafka.common.OffsetOutOfRangeException
>>>>>>>
>>>>>>> at
>>>>>>> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1699)
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> org.apache.spark.SparkException: Job aborted due to stage failure:
>>>>>>> Task 7 in
>>>>>>> stage 33.0 failed 4 times, most recent failure: Lost task 7.3
in
>>>>>>> stage 33.0
>>>>>>> (TID 283, 172.16.97.103): UnknownReason
>>>>>>>
>>>>>>> java.lang.ClassNotFoundException:
>>>>>>> kafka.common.OffsetOutOfRangeException
>>>>>>>
>>>>>>> at
>>>>>>> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1699)
>>>>>>>
>>>>>>> java.lang.ClassNotFoundException:
>>>>>>> kafka.common.OffsetOutOfRangeException
>>>>>>>
>>>>>>> at
>>>>>>> org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1699)
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> View this message in context:
>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Recovery-for-Spark-Streaming-Kafka-Direct-in-case-of-issues-with-Kafka-tp25524.html
>>>>>>> Sent from the Apache Spark User List mailing list archive at
>>>>>>> Nabble.com.
>>>>>>>
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>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>

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