Hi Flavio, For setting the retries, unfortunately there is no such setting yet and, if I am not wrong, in case of a failure of a request, an exception will be thrown and the job will restart. I am also including Till in the thread as he may know better.
For consistency guarantees and concurrency control, this depends on your underlying backend. But if you want to have end-to-end control, then you could do as Ankit suggested at his point 3), i.e have a single job for the whole pipeline (if this fits your needs of course). This will allow you to set your own “precedence” rules for your operations. Now finally, there is no way currently to expose the state of a job to another job. The way to do so is either Queryable State, or writing to a Sink. If the problem for having one job is that you emit one element at a time, you can always group elements together and emit downstream less often, in batches. Finally, if you need 2 jobs, you can always use a hybrid solution where you keep your current state in Flink, and you dump it to a Sink that is queryable once per week for example. The Sink then can be queried at any time, and data will be at most one week old. Thanks, Kostas > On May 17, 2017, at 9:35 AM, Fabian Hueske <fhue...@gmail.com> wrote: > > Hi Ankit, just a brief comment on the batch job is easier than streaming job > argument. I'm not sure about that. > I can see that just the batch job might seem easier to implement, but this is > only one part of the whole story. The operational side of using batch is more > complex IMO. > You need a tool to ingest your stream, you need storage for the ingested > data, you need a periodic scheduler to kick of your batch job, and you need > to take care of failures if something goes wrong. > The streaming case, this is not needed or the framework does it for you. > > Just my 2 cents, Fabian > > 2017-05-16 20:58 GMT+02:00 Jain, Ankit <ankit.j...@here.com > <mailto:ankit.j...@here.com>>: > Hi Flavio, > > While you wait on an update from Kostas, wanted to understand the use-case > better and share my thoughts- > > > > 1) Why is current batch mode expensive? Where are you persisting the > data after updates? Way I see it by moving to Flink, you get to use RocksDB(a > key-value store) that makes your lookups faster – probably right now you are > using a non-indexed store like S3 maybe? > > So, gain is coming from moving to a better persistence store suited to your > use-case than from batch->streaming. Myabe consider just going with a > different data store. > > IMHO, stream should only be used if you really want to act on the new events > in real-time. It is generally harder to get a streaming job correct than a > batch one. > > > > 2) If current setup is expensive due to serialization-deserialization > then that should be fixed by moving to a faster format (maybe AVRO? - I don’t > have a lot of expertise in that). I don’t see how that problem will go away > with Flink – so still need to handle serialization. > > > > 3) Even if you do decide to move to Flink – I think you can do this > with one job, two jobs are not needed. At every incoming event, check the > previous state and update/output to kafka or whatever data store you are > using. > > > > > > Thanks > > Ankit > > > > From: Flavio Pompermaier <pomperma...@okkam.it <mailto:pomperma...@okkam.it>> > Date: Tuesday, May 16, 2017 at 9:31 AM > To: Kostas Kloudas <k.klou...@data-artisans.com > <mailto:k.klou...@data-artisans.com>> > Cc: user <user@flink.apache.org <mailto:user@flink.apache.org>> > Subject: Re: Stateful streaming question > > > > Hi Kostas, > > thanks for your quick response. > > I also thought about using Async IO, I just need to figure out how to > correctly handle parallelism and number of async requests. > > However that's probably the way to go..is it possible also to set a number of > retry attempts/backoff when the async request fails (maybe due to a too busy > server)? > > > > For the second part I think it's ok to persist the state into RocksDB or > HDFS, my question is indeed about that: is it safe to start reading (with > another Flink job) from RocksDB or HDFS having an updatable state "pending" > on it? Should I ensure that state updates are not possible until the other > Flink job hasn't finish to read the persisted data? > > > > And another question...I've tried to draft such a processand basically I have > the following code: > > > > DataStream<MyGroupedObj> groupedObj = tuples.keyBy(0) > > .flatMap(new RichFlatMapFunction<Tuple4, MyGroupedObj>() { > > > > private transient ValueState<MyGroupedObj> state; > > > > @Override > > public void flatMap(Tuple4 t, Collector<MyGroupedObj> out) throws > Exception { > > MyGroupedObj current = state.value(); > > if (current == null) { > > current = new MyGroupedObj(); > > } > > .... > > current.addTuple(t); > > ... > > state.update(current); > > out.collect(current); > > } > > > > @Override > > public void open(Configuration config) { > > ValueStateDescriptor<MyGroupedObj> descriptor = > > new ValueStateDescriptor<>( > "test",TypeInformation.of(MyGroupedObj.class)); > > state = getRuntimeContext().getState(descriptor); > > } > > }); > > groupedObj.print(); > > > > but obviously this way I emit the updated object on every update while, > actually, I just want to persist the ValueState somehow (and make it > available to another job that runs one/moth for example). Is that possible? > > > > > > On Tue, May 16, 2017 at 5:57 PM, Kostas Kloudas <k.klou...@data-artisans.com > <mailto:k.klou...@data-artisans.com>> wrote: > > Hi Flavio, > > > > From what I understand, for the first part you are correct. You can use > Flink’s internal state to keep your enriched data. > > In fact, if you are also querying an external system to enrich your data, it > is worth looking at the AsyncIO feature: > > > > https://ci.apache.org/projects/flink/flink-docs-release-1.2/dev/stream/asyncio.html > > <https://ci.apache.org/projects/flink/flink-docs-release-1.2/dev/stream/asyncio.html> > > > Now for the second part, currently in Flink you cannot iterate over all > registered keys for which you have state. A pointer > > to look at the may be useful is the queryable state: > > > > https://ci.apache.org/projects/flink/flink-docs-release-1.2/dev/stream/queryable_state.html > > <https://ci.apache.org/projects/flink/flink-docs-release-1.2/dev/stream/queryable_state.html> > > > This is still an experimental feature, but let us know your opinion if you > use it. > > > > Finally, an alternative would be to keep state in Flink, and periodically > flush it to an external storage system, which you can > > query at will. > > > > Thanks, > > Kostas > > > > > > On May 16, 2017, at 4:38 PM, Flavio Pompermaier <pomperma...@okkam.it > <mailto:pomperma...@okkam.it>> wrote: > > > > Hi to all, > > we're still playing with Flink streaming part in order to see whether it can > improve our current batch pipeline. > > At the moment, we have a job that translate incoming data (as Row) into > Tuple4, groups them together by the first field and persist the result to > disk (using a thrift object). When we need to add tuples to those grouped > objects we need to read again the persisted data, flat it back to Tuple4, > union with the new tuples, re-group by key and finally persist. > > > > This is very expansive to do with batch computation while is should pretty > straightforward to do with streaming (from what I understood): I just need to > use ListState. Right? > > Then, let's say I need to scan all the data of the stateful computation (key > and values), in order to do some other computation, I'd like to know: > > how to do that? I.e. create a DataSet/DataSource<Key,Value> from the stateful > data in the stream > is there any problem to access the stateful data without stopping incoming > data (and thus possible updates to the states)? > Thanks in advance for the support, > > Flavio > > > > > > > > > > > -- > > Flavio Pompermaier > Development Department > > OKKAM S.r.l. > Tel. +(39) 0461 1823908 <tel:+39%200461%20182%203908>
