Do you believe that all exceptions (including catastrophic ones like out of heap space) should be caught and silently discarded?

Do you believe that a database system that runs out of disk space should silently continue to accept writes?

What I am trying to say is, when something is broken in a way that cant be fixed without external intervention, the system shouldn't hide it from you.  Systems fail, that's a fact of life.  Pretending that a system hasn't failed when it in fact is broken... not a good plan.



On Wed, Dec 2, 2015 at 11:38 PM, Dibyendu Bhattacharya <dibyendu.bhattachary@gmail.com> wrote:
There are other ways to deal with the problem than shutdown the streaming job. You can monitor the lag in your consumer to see if consumer if falling behind . If lag is too high that offsetOutOfRange can happen, you either increase retention period or increase consumer rate..or do both ..

What I am trying to say, streaming job should not fail in any cases ..

Dibyendu

On Thu, Dec 3, 2015 at 9:40 AM, Cody Koeninger <cody@koeninger.org> wrote:
I believe that what differentiates reliable systems is individual components should fail fast when their preconditions aren't met, and other components should be responsible for monitoring them.

If a user of the direct stream thinks that your approach of restarting and ignoring data loss is the right thing to do, they can monitor the job (which they should be doing in any case) and restart.

If a user of your library thinks that my approach of failing (so they KNOW there was data loss and can adjust their system) is the right thing to do, how do they do that?

On Wed, Dec 2, 2015 at 9:49 PM, Dibyendu Bhattacharya <dibyendu.bhattachary@gmail.com> wrote:
Well, even if you do correct retention and increase speed, OffsetOutOfRange can still come depends on how your downstream processing is. And if that happen , there is No Other way to recover old messages . So best bet here from Streaming Job point of view  is to start from earliest offset rather bring down the streaming job . In many cases goal for a streaming job is not to shut down and exit in case of any failure. I believe that is what differentiate a always running streaming job.

Dibyendu

On Thu, Dec 3, 2015 at 8:26 AM, Cody Koeninger <cody@koeninger.org> wrote:
No, silently restarting from the earliest offset in the case of offset out of range exceptions during a streaming job is not the "correct way of recovery". 

If you do that, your users will be losing data without knowing why.  It's more like  a "way of ignoring the problem without actually addressing it".

The only really correct way to deal with that situation is to recognize why it's happening, and either increase your Kafka retention or increase the speed at which you are consuming.

On Wed, Dec 2, 2015 at 7:13 PM, Dibyendu Bhattacharya <dibyendu.bhattachary@gmail.com> wrote:

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)



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