At the AMPLab we've been working on a research project that looks at just the scheduling latencies and on techniques to get lower scheduling latency. It moves away from the micro-batch model, but reuses the fault tolerance etc. in Spark. However we haven't yet figure out all the parts in integrating this with the rest of structured streaming. I'll try to post a design doc / SIP about this soon.
On a related note - are there other problems users face with micro-batch other than latency ? Thanks Shivaram On Wed, Oct 19, 2016 at 1:29 PM, Michael Armbrust <mich...@databricks.com> wrote: > I know people are seriously thinking about latency. So far that has not > been the limiting factor in the users I've been working with. > > On Wed, Oct 19, 2016 at 1:11 PM, Cody Koeninger <c...@koeninger.org> wrote: >> >> Is anyone seriously thinking about alternatives to microbatches? >> >> On Wed, Oct 19, 2016 at 2:45 PM, Michael Armbrust >> <mich...@databricks.com> wrote: >> > Anything that is actively being designed should be in JIRA, and it seems >> > like you found most of it. In general, release windows can be found on >> > the >> > wiki. >> > >> > 2.1 has a lot of stability fixes as well as the kafka support you >> > mentioned. >> > It may also include some of the following. >> > >> > The items I'd like to start thinking about next are: >> > - Evicting state from the store based on event time watermarks >> > - Sessionization (grouping together related events by key / eventTime) >> > - Improvements to the query planner (remove some of the restrictions on >> > what queries can be run). >> > >> > This is roughly in order based on what I've been hearing users hit the >> > most. >> > Would love more feedback on what is blocking real use cases. >> > >> > On Tue, Oct 18, 2016 at 1:51 AM, Ofir Manor <ofir.ma...@equalum.io> >> > wrote: >> >> >> >> Hi, >> >> I hope it is the right forum. >> >> I am looking for some information of what to expect from >> >> StructuredStreaming in its next releases to help me choose when / where >> >> to >> >> start using it more seriously (or where to invest in workarounds and >> >> where >> >> to wait). I couldn't find a good place where such planning discussed >> >> for 2.1 >> >> (like, for example ML and SPARK-15581). >> >> I'm aware of the 2.0 documented limits >> >> >> >> (http://spark.apache.org/docs/2.0.1/structured-streaming-programming-guide.html#unsupported-operations), >> >> like no support for multiple aggregations levels, joins are strictly to >> >> a >> >> static dataset (no SCD or stream-stream) etc, limited sources / sinks >> >> (like >> >> no sink for interactive queries) etc etc >> >> I'm also aware of some changes that have landed in master, like the new >> >> Kafka 0.10 source (and its on-going improvements) in SPARK-15406, the >> >> metrics in SPARK-17731, and some improvements for the file source. >> >> If I remember correctly, the discussion on Spark release cadence >> >> concluded >> >> with a preference to a four-month cycles, with likely code freeze >> >> pretty >> >> soon (end of October). So I believe the scope for 2.1 should likely >> >> quite >> >> clear to some, and that 2.2 planning should likely be starting about >> >> now. >> >> Any visibility / sharing will be highly appreciated! >> >> thanks in advance, >> >> >> >> Ofir Manor >> >> >> >> Co-Founder & CTO | Equalum >> >> >> >> Mobile: +972-54-7801286 | Email: ofir.ma...@equalum.io >> > >> > > > --------------------------------------------------------------------- To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
