For a given batch, for a given partition, the messages will be processed in order by the executor that is running that partition. That's because messages for the given offset range are pulled by the executor, not pushed from some other receiver.
If you have speculative execution, yes, another executor may be running that partition. If your job is lagging behind in processing such that the next batch starts executing before the last batch is finished processing, yes it is possible for some other executor to start working on messages from that same kafka partition. The obvious solution here seems to be turn off speculative execution and adjust your batch interval / sizes such that they can comfortably finish processing :) If your processing time is sufficiently non-linear with regard to the number of messages, yes you might be able to do something with overriding dstream.compute. Unfortunately the new kafka dstream implementation is private, so it's not straightforward to subclass it. I'd like to get a solution in place for people who need to be able to tune the batch generation policy (I need to as well, for unrelated reasons). Maybe you can say a little more about your use case. But regardless of the technology you're using to read from kafka (spark, storm, whatever), kafka only gives you ordering as to a particular partition. So you're going to need to do some kind of downstream sorting if you really care about a global order. On Fri, Feb 20, 2015 at 1:43 AM, Neelesh <neele...@gmail.com> wrote: > Even with the new direct streams in 1.3, isn't it the case that the job > *scheduling* follows the partition order, rather than job *execution*? Or > is it the case that the stream listens to job completion event (using a > streamlistener) before scheduling the next batch? To compare with storm > from a message ordering point of view, unless a tuple is fully processed by > the DAG (as defined by spout+bolts), the next tuple does not enter the DAG. > > > On Thu, Feb 19, 2015 at 9:47 PM, Cody Koeninger <c...@koeninger.org> > wrote: > >> Kafka ordering is guaranteed on a per-partition basis. >> >> The high-level consumer api as used by the spark kafka streams prior to >> 1.3 will consume from multiple kafka partitions, thus not giving any >> ordering guarantees. >> >> The experimental direct stream in 1.3 uses the "simple" consumer api, and >> there is a 1:1 correspondence between spark partitions and kafka >> partitions. So you will get deterministic ordering, but only on a >> per-partition basis. >> >> On Thu, Feb 19, 2015 at 11:31 PM, Neelesh <neele...@gmail.com> wrote: >> >>> I had a chance to talk to TD today at the Strata+Hadoop Conf in San >>> Jose. We talked a bit about this after his presentation about this - the >>> short answer is spark streaming does not guarantee any sort of ordering >>> (within batches, across batches). One would have to use updateStateByKey >>> to collect the events and sort them based on some attribute of the event. >>> But TD said message ordering is a frequently asked feature recently and is >>> getting on his radar. >>> >>> I went through the source code and there does not seem to be any >>> architectural/design limitation to support this. (JobScheduler, >>> JobGenerator are a good starting point to see how stuff works under the >>> hood). Overriding DStream#compute and using streaminglistener looks like a >>> simple way of ensuring ordered execution of batches within a stream. But >>> this would be a partial solution, since ordering within a batch needs some >>> more work that I don't understand fully yet. >>> >>> Side note : My custom receiver polls the metricsservlet once in a while >>> to decide whether jobs are getting done fast enough and throttle/relax >>> pushing data in to receivers based on the numbers provided by >>> metricsservlet. I had to do this because out-of-the-box rate limiting right >>> now is static and cannot adapt to the state of the cluster >>> >>> thnx >>> -neelesh >>> >>> On Wed, Feb 18, 2015 at 4:13 PM, jay vyas <jayunit100.apa...@gmail.com> >>> wrote: >>> >>>> This is a *fantastic* question. The idea of how we identify individual >>>> things in multiple DStreams is worth looking at. >>>> >>>> The reason being, that you can then fine tune your streaming job, based >>>> on the RDD identifiers (i.e. are the timestamps from the producer >>>> correlating closely to the order in which RDD elements are being produced) >>>> ? If *NO* then you need to (1) dial up throughput on producer sources or >>>> else (2) increase cluster size so that spark is capable of evenly handling >>>> load. >>>> >>>> You cant decide to do (1) or (2) unless you can track when the >>>> streaming elements are being converted to RDDs by spark itself. >>>> >>>> >>>> >>>> On Wed, Feb 18, 2015 at 6:54 PM, Neelesh <neele...@gmail.com> wrote: >>>> >>>>> There does not seem to be a definitive answer on this. Every time I >>>>> google for message ordering,the only relevant thing that comes up is this >>>>> - >>>>> http://samza.apache.org/learn/documentation/0.8/comparisons/spark-streaming.html >>>>> . >>>>> >>>>> With a kafka receiver that pulls data from a single kafka partition of >>>>> a kafka topic, are individual messages in the microbatch in same the order >>>>> as kafka partition? Are successive microbatches originating from a kafka >>>>> partition executed in order? >>>>> >>>>> >>>>> Thanks! >>>>> >>>>> >>>> >>>> >>>> >>>> -- >>>> jay vyas >>>> >>> >>> >> >
