I don't to join the third stream. And Yes, This is what I was thinking of.also : s1.union(s2).keyBy().window().apply(// outerjoin).keyBy.flatMap(// backup join)
I am already done integrating with Cassandra but I feel RocksDB will be a better option, I will have to take care of the clearing part as you have suggested, will check that in documentation. I have the DTO with almost 50 fields , converting it to JSON and storing it as a state should not be a problem , or there is no harm in storing the DTO ? I think the documentation should specify the point that the state will be maintained for user-defined operators to avoid confusion. Regards, Vinay Patil On Thu, Sep 1, 2016 at 1:12 PM, Fabian Hueske-2 [via Apache Flink User Mailing List archive.] <ml-node+s2336050n8843...@n4.nabble.com> wrote: > I thought you would like to join the non-matched elements with another > (third) stream. > > --> s1.union(s2).keyBy().window().apply(// > outerjoin).keyBy.connect(s3.keyBy).coFlatMap(// > backup join) > > If you want to match the non-matched stream with itself a FlatMapFunction > is the right choice. > > --> s1.union(s2).keyBy().window().apply(// outerjoin).keyBy.flatMap(// > backup join) > > The backup join puts all non-match elements in the state and waits for > another non-matched element with the same key to do the join. > > Best, Fabian > > > > 2016-09-01 19:55 GMT+02:00 vinay patil <[hidden email] > <http:///user/SendEmail.jtp?type=node&node=8843&i=0>>: > >> Yes, that's what I am looking for. >> >> But why to use CoFlatMapFunction , I have already got the >> matchingAndNonMatching Stream , by doing the union of two streams and >> having the logic in apply method for performing outer-join. >> >> I am thinking of applying the same key on matchingAndNonMatching and >> flatmap to take care of rest logic. >> >> Or are you suggestion to use Co-FlatMapFunction after the outer-join >> operation (I mean after doing the window and >> getting matchingAndNonMatching stream )? >> >> Regards, >> Vinay Patil >> >> On Thu, Sep 1, 2016 at 11:38 AM, Fabian Hueske-2 [via Apache Flink User >> Mailing List archive.] <[hidden email] >> <http:///user/SendEmail.jtp?type=node&node=8842&i=0>> wrote: >> >>> Thanks for the explanation. I think I understood your usecase. >>> >>> Yes, I'd go for the RocksDB approach in a CoFlatMapFunction on a keyed >>> stream (keyed by join key). >>> One input would be the unmatched outer join records, the other input >>> would serve the events you want to match them with. >>> Retrieving elements from RocksDB will be local and should be fast. >>> >>> You should be confident though, that all unmatched record will be picked >>> up at some point (RocksDB persists to disk, so you won't run out of memory >>> but snapshots size will increase). >>> The future state expiry feature will avoid such situations. >>> >>> Best, Fabian >>> >>> 2016-09-01 18:29 GMT+02:00 vinay patil <[hidden email] >>> <http:///user/SendEmail.jtp?type=node&node=8837&i=0>>: >>> >>>> Hi Fabian, >>>> >>>> I had already used Co-Group function earlier but were getting some >>>> issues while dealing with watermarks (for one use case I was not getting >>>> the correct result), so I have used the union operator for performing the >>>> outer-join (WindowFunction on a keyedStream), this approach is working >>>> correctly and giving me correct results. >>>> >>>> As I have discussed the scenario, I want to maintain the non-matching >>>> records in some store, so that's why I was thinking of using RocksDB as a >>>> store here, where I will maintain the user-defined state after the >>>> outer-join window operator, and I can query it using Flink to check if the >>>> value for a particular key is present or not , if present I can match them >>>> and send it downstream. >>>> >>>> The final goal is to have zero non-matching records, so this is the >>>> backup plan to handle edge-case scenarios. >>>> >>>> I have already integrated code to write to Cassandra using Flink >>>> Connector, but I think this will be a better option rather than hitting the >>>> query to external store since RocksDb will store the data to local TM disk, >>>> the retrieval will be faster here than Cassandra , right ? >>>> >>>> What do you think ? >>>> >>>> >>>> Regards, >>>> Vinay Patil >>>> >>>> On Thu, Sep 1, 2016 at 10:19 AM, Fabian Hueske-2 [via Apache Flink User >>>> Mailing List archive.] <[hidden email] >>>> <http:///user/SendEmail.jtp?type=node&node=8836&i=0>> wrote: >>>> >>>>> Hi Vinay, >>>>> >>>>> can you give a bit more detail about how you plan to implement the >>>>> outer join? Using a WIndowFunction or a CoFlatMapFunction on a >>>>> KeyedStream? >>>>> >>>>> An alternative could be to use a CoGroup operator which collects from >>>>> two inputs all elements that share a common key (the join key) and are in >>>>> the same window. The interface of the function provides two iterators over >>>>> the elements of both inputs and can be used to implement outer join >>>>> functionality. The benefit of working with a CoGroupFunction is that you >>>>> do >>>>> not have to take care of state handling at all. >>>>> >>>>> In case you go for a custom implementation you will need to work with >>>>> operator state. >>>>> However, you do not need to directly interact with RocksDB. Flink is >>>>> taking care of that for you. >>>>> >>>>> Best, Fabian >>>>> >>>>> 2016-09-01 16:13 GMT+02:00 vinay patil <[hidden email] >>>>> <http:///user/SendEmail.jtp?type=node&node=8832&i=0>>: >>>>> >>>>>> Hi Fabian/Stephan, >>>>>> >>>>>> Waiting for your suggestion >>>>>> >>>>>> Regards, >>>>>> Vinay Patil >>>>>> >>>>>> On Wed, Aug 31, 2016 at 1:46 PM, Vinay Patil <[hidden email] >>>>>> <http:///user/SendEmail.jtp?type=node&node=8829&i=0>> wrote: >>>>>> >>>>>>> Hi Fabian/Stephan, >>>>>>> >>>>>>> This makes things clear. >>>>>>> >>>>>>> This is the use case I have : >>>>>>> I am performing a outer join operation on the two streams (in >>>>>>> window) after which I get matchingAndNonMatchingStream, now I want to >>>>>>> make >>>>>>> sure that the matching rate is high (matching cannot happen if one of >>>>>>> the >>>>>>> source is not emitting elements for certain time) , so to tackle this >>>>>>> situation I was thinking of using RocksDB as a state Backend, where I >>>>>>> will >>>>>>> insert the unmatched records in it (key - will be same as used for >>>>>>> window >>>>>>> and value will be DTO ), so before inserting into it I will check if it >>>>>>> is >>>>>>> already present in RocksDB, if yes I will take the data from it and >>>>>>> send it >>>>>>> downstream (and ensure I perform the clean operation for that key). >>>>>>> (Also the data to store should be encrypted, encryption part can be >>>>>>> handled ) >>>>>>> >>>>>>> so instead of using Cassandra , Can I do this using RocksDB as state >>>>>>> backend since the state is not gone after checkpointing ? >>>>>>> >>>>>>> P.S I have kept the watermark behind by 1500 secs just to be safe on >>>>>>> handling late elements but to tackle edge case scenarios like the one >>>>>>> mentioned above we are having a backup plan of using Cassandra as >>>>>>> external >>>>>>> store since we are dealing with financial critical data. >>>>>>> >>>>>>> Regards, >>>>>>> Vinay Patil >>>>>>> >>>>>>> On Wed, Aug 31, 2016 at 11:34 AM, Fabian Hueske <[hidden email] >>>>>>> <http:///user/SendEmail.jtp?type=node&node=8829&i=1>> wrote: >>>>>>> >>>>>>>> Hi Vinaj, >>>>>>>> >>>>>>>> if you use user-defined state, you have to manually clear it. >>>>>>>> Otherwise, it will stay in the state backend (heap or RocksDB) >>>>>>>> until the >>>>>>>> job goes down (planned or due to an OOM error). >>>>>>>> >>>>>>>> This is esp. important to keep in mind, when using keyed state. >>>>>>>> If you have an unbounded, evolving key space you will likely run >>>>>>>> out-of-memory. >>>>>>>> The job will constantly add state for each new key but won't be >>>>>>>> able to >>>>>>>> clean up the state for "expired" keys. >>>>>>>> >>>>>>>> You could implement a clean-up mechanism this if you implement a >>>>>>>> custom >>>>>>>> stream operator. >>>>>>>> However this is a very low level interface and requires solid >>>>>>>> understanding >>>>>>>> of the internals like timestamps, watermarks and the checkpointing >>>>>>>> mechanism. >>>>>>>> >>>>>>>> The community is currently working on a state expiry feature (state >>>>>>>> will be >>>>>>>> discarded if not requested or updated for x minutes). >>>>>>>> >>>>>>>> Regarding the second question: Does state remain local after >>>>>>>> checkpointing? >>>>>>>> Yes, the local state is only copied to the remote FS (HDFS, S3, >>>>>>>> ...) but >>>>>>>> remains in the operator. So the state is not gone after a >>>>>>>> checkpoint is >>>>>>>> completed. >>>>>>>> >>>>>>>> Hope this helps, >>>>>>>> Fabian >>>>>>>> >>>>>>>> 2016-08-31 18:17 GMT+02:00 Vinay Patil <[hidden email] >>>>>>>> <http:///user/SendEmail.jtp?type=node&node=8829&i=2>>: >>>>>>>> >>>>>>>> > Hi Stephan, >>>>>>>> > >>>>>>>> > Just wanted to jump into this discussion regarding state. >>>>>>>> > >>>>>>>> > So do you mean that if we maintain user-defined state (for >>>>>>>> non-window >>>>>>>> > operators), then if we do not clear it explicitly will the data >>>>>>>> for that >>>>>>>> > key remains in RocksDB. >>>>>>>> > >>>>>>>> > What happens in case of checkpoint ? I read in the documentation >>>>>>>> that after >>>>>>>> > the checkpoint happens the rocksDB data is pushed to the desired >>>>>>>> location >>>>>>>> > (hdfs or s3 or other fs), so for user-defined state does the data >>>>>>>> still >>>>>>>> > remain in RocksDB after checkpoint ? >>>>>>>> > >>>>>>>> > Correct me if I have misunderstood this concept >>>>>>>> > >>>>>>>> > For one of our use we were going for this, but since I read the >>>>>>>> above part >>>>>>>> > in documentation so we are going for Cassandra now (to store >>>>>>>> records and >>>>>>>> > query them for a special case) >>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>> > >>>>>>>> > Regards, >>>>>>>> > Vinay Patil >>>>>>>> > >>>>>>>> > On Wed, Aug 31, 2016 at 4:51 AM, Stephan Ewen <[hidden email] >>>>>>>> <http:///user/SendEmail.jtp?type=node&node=8829&i=3>> wrote: >>>>>>>> > >>>>>>>> > > In streaming, memory is mainly needed for state (key/value >>>>>>>> state). The >>>>>>>> > > exact representation depends on the chosen StateBackend. >>>>>>>> > > >>>>>>>> > > State is explicitly released: For windows, state is cleaned up >>>>>>>> > > automatically (firing / expiry), for user-defined state, keys >>>>>>>> have to be >>>>>>>> > > explicitly cleared (clear() method) or in the future will have >>>>>>>> the option >>>>>>>> > > to expire. >>>>>>>> > > >>>>>>>> > > The heavy work horse for streaming state is currently RocksDB, >>>>>>>> which >>>>>>>> > > internally uses native (off-heap) memory to keep the data. >>>>>>>> > > >>>>>>>> > > Does that help? >>>>>>>> > > >>>>>>>> > > Stephan >>>>>>>> > > >>>>>>>> > > >>>>>>>> > > On Tue, Aug 30, 2016 at 11:52 PM, Roshan Naik <[hidden email] >>>>>>>> <http:///user/SendEmail.jtp?type=node&node=8829&i=4>> >>>>>>>> > > wrote: >>>>>>>> > > >>>>>>>> > > > As per the docs, in Batch mode, dynamic memory allocation is >>>>>>>> avoided by >>>>>>>> > > > storing messages being processed in ByteBuffers via Unsafe >>>>>>>> methods. >>>>>>>> > > > >>>>>>>> > > > Couldn't find any docs describing mem mgmt in Streamingn >>>>>>>> mode. So... >>>>>>>> > > > >>>>>>>> > > > - Am wondering if this is also the case with Streaming ? >>>>>>>> > > > >>>>>>>> > > > - If so, how does Flink detect that an object is no longer >>>>>>>> being used >>>>>>>> > and >>>>>>>> > > > can be reclaimed for reuse once again ? >>>>>>>> > > > >>>>>>>> > > > -roshan >>>>>>>> > > > >>>>>>>> > > >>>>>>>> > >>>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>>> ------------------------------ >>>>>> View this message in context: Re: Streaming - memory management >>>>>> <http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Re-Streaming-memory-management-tp8829.html> >>>>>> Sent from the Apache Flink User Mailing List archive. mailing list >>>>>> archive >>>>>> <http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/> >>>>>> at Nabble.com. >>>>>> >>>>> >>>>> >>>>> >>>>> ------------------------------ >>>>> If you reply to this email, your message will be added to the >>>>> discussion below: >>>>> http://apache-flink-user-mailing-list-archive.2336050.n4.nab >>>>> ble.com/Re-Streaming-memory-management-tp8829p8832.html >>>>> To start a new topic under Apache Flink User Mailing List archive., >>>>> email [hidden email] >>>>> <http:///user/SendEmail.jtp?type=node&node=8836&i=1> >>>>> To unsubscribe from Apache Flink User Mailing List archive., click >>>>> here. >>>>> NAML >>>>> <http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/template/NamlServlet.jtp?macro=macro_viewer&id=instant_html%21nabble%3Aemail.naml&base=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespace&breadcrumbs=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml> >>>>> >>>> >>>> >>>> ------------------------------ >>>> View this message in context: Re: Streaming - memory management >>>> <http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Re-Streaming-memory-management-tp8829p8836.html> >>>> Sent from the Apache Flink User Mailing List archive. mailing list >>>> archive >>>> <http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/> >>>> at Nabble.com. >>>> >>> >>> >>> >>> ------------------------------ >>> If you reply to this email, your message will be added to the discussion >>> below: >>> http://apache-flink-user-mailing-list-archive.2336050.n4.nab >>> ble.com/Re-Streaming-memory-management-tp8829p8837.html >>> To start a new topic under Apache Flink User Mailing List archive., >>> email [hidden email] >>> <http:///user/SendEmail.jtp?type=node&node=8842&i=1> >>> To unsubscribe from Apache Flink User Mailing List archive., click here. >>> NAML >>> <http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/template/NamlServlet.jtp?macro=macro_viewer&id=instant_html%21nabble%3Aemail.naml&base=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespace&breadcrumbs=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml> >>> >> >> >> ------------------------------ >> View this message in context: Re: Streaming - 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