Again, that looks like you lost a kafka broker. Executors will retry
failed tasks automatically up to the max failures.
spark.streaming.kafka.maxRetries controls the number of times the driver
will retry when attempting to get offsets.
If your broker isn't up / rebalance hasn't finished after N
My bad Got that exception in driver code of same job not in executor.
But it says of socket close exception only.
org.apache.spark.SparkException: ArrayBuffer(java.io.EOFException: Received
-1 when reading from channel, socket has likely been closed.,
org.apache.spark.SparkException: Couldn't fi
NotLeaderForPartitionException means you lost a kafka broker or had a
rebalance... why did you say " I am getting Connection tmeout in my code."
You've asked questions about this exact same situation before, the answer
remains the same
On Thu, Sep 10, 2015 at 9:44 AM, Shushant Arora
wrote:
> St
Stack trace is
15/09/09 22:49:52 ERROR kafka.KafkaRDD: Lost leader for topic topicname
partition 99, sleeping for 200ms
kafka.common.NotLeaderForPartitionException
at sun.reflect.GeneratedConstructorAccessor26.newInstance(Unknown
Source)
at
sun.reflect.DelegatingConstructorAccessor
Post the actual stacktrace you're getting
On Thu, Sep 10, 2015 at 12:21 AM, Shushant Arora
wrote:
> Executors in spark streaming 1.3 fetch messages from kafka in batches and
> what happens when executor takes longer time to complete a fetch batch
>
> say in
>
>
> directKafkaStream.foreachRDD(new
I feel need of pause and resume in streaming app :)
Is there any limit on max queued jobs ? If yes what happens once that limit
reaches? Does job gets killed?
On Tue, Sep 1, 2015 at 10:02 PM, Cody Koeninger wrote:
> Sounds like you'd be better off just failing if the external server is
> down,
Sounds like you'd be better off just failing if the external server is
down, and scripting monitoring / restarting of your job.
On Tue, Sep 1, 2015 at 11:19 AM, Shushant Arora
wrote:
> Since in my app , after processing the events I am posting the events to
> some external server- if external se
Since in my app , after processing the events I am posting the events to
some external server- if external server is down - I want to backoff
consuming from kafka. But I can't stop and restart the consumer since it
needs manual effort.
Backing off few batches is also not possible -since decision o
Honestly I'd concentrate more on getting your batches to finish in a timely
fashion, so you won't even have the issue to begin with...
On Tue, Sep 1, 2015 at 10:16 AM, Shushant Arora
wrote:
> What if I use custom checkpointing. So that I can take care of offsets
> being checkpointed at end of ea
No, if you start arbitrarily messing around with offset ranges after
compute is called, things are going to get out of whack.
e.g. checkpoints are no longer going to correspond to what you're actually
processing
On Tue, Sep 1, 2015 at 10:04 AM, Shushant Arora
wrote:
> can I reset the range base
What if I use custom checkpointing. So that I can take care of offsets
being checkpointed at end of each batch.
Will it be possible then to reset the offset.
On Tue, Sep 1, 2015 at 8:42 PM, Cody Koeninger wrote:
> No, if you start arbitrarily messing around with offset ranges after
> compute is
can I reset the range based on some condition - before calling
transformations on the stream.
Say -
before calling :
directKafkaStream.foreachRDD(new Function, Void>() {
@Override
public Void call(JavaRDD v1) throws Exception {
v1.foreachPartition(new VoidFunction>{
@Override
public void call(I
It's at the time compute() gets called, which should be near the time the
batch should have been queued.
On Tue, Sep 1, 2015 at 8:02 AM, Shushant Arora
wrote:
> Hi
>
> In spark streaming 1.3 with kafka- when does driver bring latest offsets
> of this run - at start of each batch or at time when
13 matches
Mail list logo
