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From Feroze Daud <khic...@yahoo.com.INVALID>
Subject Re: log compaction scaling with ~100m messages
Date Thu, 08 Oct 2015 19:34:06 GMT
Thank you for your response!
Our use case is more similar to a traditional k/v store. We are doing a new process that is
going to spit huge amounts of data. We are using kafka as a broker so that downstream clients
can all consume from the kafka topic. What we would like to avoid, is writing two systems
- a stream processing system that runs on kafka and another one that runs on snapshots. SO
for eg, when we ship, we will have a process running on kafka doing stream processing. If
we change any business logic in our process we would like to reset our queue level back to
zero and reprocess the whole queue.
but if I understand you, it seems that givben our key space, we cant do that?
Our update rate is as follows:
we expect ~150m unique K,V pairs to be created in the initial ship. After that, we expect
about 3 updates to each key per year. Updates for all keys will not happen at the same time. so,
what do you think? Do you still advise that using the topic as a K/V store with log compatction
( not time base retention ) will work?
If not, is there any other processing paradigm we can look into where we can use the same
code for stream processing as well as reprocessing entire dataset? 


     On Wednesday, October 7, 2015 11:16 AM, Joel Koshy <jjkoshy.w@gmail.com> wrote:
   

 Using log compaction is well-suited for applications that use Kafka directly and need to
persist some state associated with its processing. So something like offset management for
consumers is a good fit. Another good use-case is for storing schemas associated with your
Kafka topics. These are both very specific to maintaining metadata around your stream processing.
Although it can be used for more general K-V storage it is not always a good fit. This is
especially true if your key-space is bound to grow significantly over time or has an high
update rate. The other aspect is the need to do some sort of caching of your key-value pairs
(since otherwise lookups would require scanning the log). So for application-level general
K-V storage, you could certainly use Kafka as a persistence mechanism for recording recent
updates (with traditional time-based retention), but you would probably want a more suitable
K-V store separate from Kafka. I'm not sure this (i.e., traditional db storage) is your use
case since you mention "a lot of stream processing on these messages" - so it sounds more
like repetitive processing over the entire key space. For that it may be more reasonable.
The alternative is to use snapshots and read more recent updates from the updates stream in
Kafka. Samza folks may want to weigh in here as well.
That said, to answer your question: sure it is feasible to use log compaction with 1B keys,
especially if you have enough brokers, partitions, and log cleaner threads but I'm not sure
it is the best approach to take. We did hit various issues (bugs/feature gaps) with log compaction
while using it for consumer offset management: e.g., support for compressed messages, various
other bugs, but most of these have been resolved.
Hope that helps,

Joel

On Tue, Oct 6, 2015 at 8:34 PM, Feroze Daud <khichdo@yahoo.com.invalid> wrote:
> hi!
> We have a use case where we want to store ~100m keys in kafka. Is there any problem with
this approach?
> I have heard from some people using kafka, that kafka has a problem when doing log compaction
with those many number of keys.
> Another topic might have around 10 different K/V pairs for each key in the primary topic.
The primary topic's keyspace is approx of 100m keys. We would like to store this in kafka
because we are doing a lot of stream processing on these messages, and want to avoid writing
another process to recompute data from snapshots.
> So, in summary:
> primary topic: ~100m keyssecondary topic: ~1B keys
> Is it feasible to use log compaction at such a scale of data?
> Thanks
> feroze.



  
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