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.] <ml-node+s2336050n8837...@n4.nabble.com> 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.nabble.com/Re-Streaming-memory-management-tp8829p8837.html > To start a new topic under Apache Flink User Mailing List archive., email > ml-node+s2336050n1...@n4.nabble.com > To unsubscribe from Apache Flink User Mailing List archive., click here > <http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/template/NamlServlet.jtp?macro=unsubscribe_by_code&node=1&code=dmluYXkxOC5wYXRpbEBnbWFpbC5jb218MXwxODExMDE2NjAx> > . > 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: http://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/Re-Streaming-memory-management-tp8829p8842.html Sent from the Apache Flink User Mailing List archive. mailing list archive at Nabble.com.
