Cool. Thanks for the detailed response Cody.
Thanks
Best Regards
On Tue, May 19, 2015 at 6:43 PM, Cody Koeninger c...@koeninger.org wrote:
If those questions aren't answered by
https://github.com/koeninger/kafka-exactly-once/blob/master/blogpost.md
please let me know so I can update it.
If those questions aren't answered by
https://github.com/koeninger/kafka-exactly-once/blob/master/blogpost.md
please let me know so I can update it.
If you set auto.offset.reset to largest, it will start at the largest
offset. Any messages before that will be skipped, so if prior runs of the
I have played a bit with the directStream kafka api. Good work cody. These
are my findings and also can you clarify a few things for me (see below).
- When auto.offset.reset- smallest and you have 60GB of messages in
Kafka, it takes forever as it reads the whole 60GB at once. largest will
only
As far as I can tell, Dibyendu's cons boil down to:
1. Spark checkpoints can't be recovered if you upgrade code
2. Some Spark transformations involve a shuffle, which can repartition data
It's not accurate to imply that either one of those things are inherently
cons of the direct stream api.
Thanks Cody for your email. I think my concern was not to get the ordering
of message within a partition , which as you said is possible if one knows
how Spark works. The issue is how Spark schedule jobs on every batch which
is not on the same order they generated. So if that is not guaranteed it
You linked to a google mail tab, not a public archive, so I don't know
exactly which conversation you're referring to.
As far as I know, streaming only runs a single job at a time in the order
they were defined, unless you turn on an experimental option for more
parallelism (TD or someone more
What I want is if the driver dies for some reason and it is restarted I
want to read only messages that arrived into Kafka following the restart of
the driver program and re-connection to Kafka.
Has anyone done this? any links or resources that can help explain this?
Regards
jk
Yep, you can try this lowlevel Kafka receiver
https://github.com/dibbhatt/kafka-spark-consumer. Its much more
flexible/reliable than the one comes with Spark.
Thanks
Best Regards
On Tue, May 12, 2015 at 5:15 PM, James King jakwebin...@gmail.com wrote:
What I want is if the driver dies for some
Very nice! will try and let you know, thanks.
On Tue, May 12, 2015 at 2:25 PM, Akhil Das ak...@sigmoidanalytics.com
wrote:
Yep, you can try this lowlevel Kafka receiver
https://github.com/dibbhatt/kafka-spark-consumer. Its much more
flexible/reliable than the one comes with Spark.
Thanks
Akhil, I hope I'm misreading the tone of this. If you have personal issues
at stake, please take them up outside of the public list. If you have
actual factual concerns about the kafka integration, please share them in a
jira.
Regarding reliability, here's a screenshot of a current production
Many thanks both, appreciate the help.
On Tue, May 12, 2015 at 4:18 PM, Cody Koeninger c...@koeninger.org wrote:
Yes, that's what happens by default.
If you want to be super accurate about it, you can also specify the exact
starting offsets for every topic/partition.
On Tue, May 12, 2015
Hi Cody,
I was just saying that i found more success and high throughput with the
low level kafka api prior to KafkfaRDDs which is the future it seems. My
apologies if you felt it that way. :)
On 12 May 2015 19:47, Cody Koeninger c...@koeninger.org wrote:
Akhil, I hope I'm misreading the tone of
Yes, that's what happens by default.
If you want to be super accurate about it, you can also specify the exact
starting offsets for every topic/partition.
On Tue, May 12, 2015 at 9:01 AM, James King jakwebin...@gmail.com wrote:
Thanks Cody.
Here are the events:
- Spark app connects to
The low level consumer which Akhil mentioned , has been running in Pearson
for last 4-5 months without any downtime. I think this one is the reliable
Receiver Based Kafka consumer as of today for Spark .. if you say it that
way ..
Prior to Spark 1.3 other Receiver based consumers have used Kafka
I don't think it's accurate for Akhil to claim that the linked library is
much more flexible/reliable than what's available in Spark at this point.
James, what you're describing is the default behavior for the
createDirectStream api available as part of spark since 1.3. The kafka
parameter
Hi Cody,
If you are so sure, can you share a bench-marking (which you ran for days
maybe?) that you have done with Kafka APIs provided by Spark?
Thanks
Best Regards
On Tue, May 12, 2015 at 7:22 PM, Cody Koeninger c...@koeninger.org wrote:
I don't think it's accurate for Akhil to claim that
Thanks Cody.
Here are the events:
- Spark app connects to Kafka first time and starts consuming
- Messages 1 - 10 arrive at Kafka then Spark app gets them
- Now driver dies
- Messages 11 - 15 arrive at Kafka
- Spark driver program reconnects
- Then Messages 16 - 20 arrive Kafka
What I want is
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