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From "Hari Shreedharan" <hshreedha...@cloudera.com>
Subject Re: Which committers care about Kafka?
Date Thu, 18 Dec 2014 19:44:19 GMT
Hi Cody,




I am an absolute +1 on SPARK-3146. I think we can implement something pretty simple and lightweight
for that one.




For the Kafka DStream skipping the WAL implementation - this is something I discussed with
TD a few weeks ago. Though it is a good idea to implement this to avoid unnecessary HDFS writes,
it is an optimization. For that reason, we must be careful in implementation. There are a
couple of issues that we need to ensure works properly - specifically ordering. To ensure
we pull messages from different topics and partitions in the same order after failure, we’d
still have to persist the metadata to HDFS (or some other system) - this metadata must contain
the order of messages consumed, so we know how to re-read the messages. I am planning to explore
this once I have some time (probably in Jan). In addition, we must also ensure bucketing functions
work fine as well. I will file a placeholder jira for this one. 




I also wrote an API to write data back to Kafka a while back - https://github.com/apache/spark/pull/2994
. I am hoping that this will get pulled in soon, as this is something I know people want.
I am open to feedback on that - anything that I can do to make it better.




Thanks, Hari

On Thu, Dec 18, 2014 at 11:14 AM, Patrick Wendell <pwendell@gmail.com>
wrote:

> Hey Cody,
> Thanks for reaching out with this. The lead on streaming is TD - he is
> traveling this week though so I can respond a bit. To the high level
> point of whether Kafka is important - it definitely is. Something like
> 80% of Spark Streaming deployments (anecdotally) ingest data from
> Kafka. Also, good support for Kafka is something we generally want in
> Spark and not a library. In some cases IIRC there were user libraries
> that used unstable Kafka API's and we were somewhat waiting on Kafka
> to stabilize them to merge things upstream. Otherwise users wouldn't
> be able to use newer Kakfa versions. This is a high level impression
> only though, I haven't talked to TD about this recently so it's worth
> revisiting given the developments in Kafka.
> Please do bring things up like this on the dev list if there are
> blockers for your usage - thanks for pinging it.
> - Patrick
> On Thu, Dec 18, 2014 at 7:07 AM, Cody Koeninger <cody@koeninger.org> wrote:
>> Now that 1.2 is finalized...  who are the go-to people to get some
>> long-standing Kafka related issues resolved?
>>
>> The existing api is not sufficiently safe nor flexible for our production
>> use.  I don't think we're alone in this viewpoint, because I've seen
>> several different patches and libraries to fix the same things we've been
>> running into.
>>
>> Regarding flexibility
>>
>> https://issues.apache.org/jira/browse/SPARK-3146
>>
>> has been outstanding since August, and IMHO an equivalent of this is
>> absolutely necessary.  We wrote a similar patch ourselves, then found that
>> PR and have been running it in production.  We wouldn't be able to get our
>> jobs done without it.  It also allows users to solve a whole class of
>> problems for themselves (e.g. SPARK-2388, arbitrary delay of messages, etc).
>>
>> Regarding safety, I understand the motivation behind WriteAheadLog as a
>> general solution for streaming unreliable sources, but Kafka already is a
>> reliable source.  I think there's a need for an api that treats it as
>> such.  Even aside from the performance issues of duplicating the
>> write-ahead log in kafka into another write-ahead log in hdfs, I need
>> exactly-once semantics in the face of failure (I've had failures that
>> prevented reloading a spark streaming checkpoint, for instance).
>>
>> I've got an implementation i've been using
>>
>> https://github.com/koeninger/spark-1/tree/kafkaRdd/external/kafka
>> /src/main/scala/org/apache/spark/rdd/kafka
>>
>> Tresata has something similar at https://github.com/tresata/spark-kafka,
>> and I know there were earlier attempts based on Storm code.
>>
>> Trying to distribute these kinds of fixes as libraries rather than patches
>> to Spark is problematic, because large portions of the implementation are
>> private[spark].
>>
>>  I'd like to help, but i need to know whose attention to get.
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