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From Aljoscha Krettek <aljos...@apache.org>
Subject Re: Stateful streaming question
Date Fri, 16 Jun 2017 09:55:57 GMT
I think it might be possible to do but I’m not aware of anyone working on that and I haven’t
seen anyone on the mailing lists express interest in working on that.

> On 16. Jun 2017, at 11:31, Flavio Pompermaier <pompermaier@okkam.it> wrote:
> 
> Ok thanks for the clarification. Do you think it could be possible (sooner or later)
to have in Flink some sort of synchronization between jobs (as in this case where the input
datastream should be "paused" until the second job finishes)? I know I coould use something
like Oozie or Falcon to orchestrate jobs but I'd prefer to avoid to add them to our architecture..
> 
> Best,
> Flavio
> 
> On Fri, Jun 16, 2017 at 11:23 AM, Aljoscha Krettek <aljoscha@apache.org <mailto:aljoscha@apache.org>>
wrote:
> Hi,
> 
> I’m afraid not. You would have to wait for one job to finish before starting the next
one.
> 
> Best,
> Aljoscha
>> On 15. Jun 2017, at 20:11, Flavio Pompermaier <pompermaier@okkam.it <mailto:pompermaier@okkam.it>>
wrote:
>> 
>> Hi Aljoscha,
>> we're still investigating possible solutions here. Yes, as you correctly said there
are links between data of different keys so we can only proceed with the next job only once
we are sure at 100% that all input data has been consumed and no other data will be read until
this last jobs ends.
>> There should be some sort of synchronization between these 2 jobs...is that possible
right now in Flink?
>> 
>> Thanks a lot for the support,
>> Flavio
>> 
>> On Thu, Jun 15, 2017 at 12:16 PM, Aljoscha Krettek <aljoscha@apache.org <mailto:aljoscha@apache.org>>
wrote:
>> Hi,
>> 
>> Trying to revive this somewhat older thread: have you made any progress? I think
going with a ProcessFunction that keeps all your state internally and periodically outputs
to, say, Elasticsearch using a sink seems like the way to go? You can do the periodic emission
using timers in the ProcessFunction. 
>> 
>> In your use case, does the data you would store in the Flink managed state have links
between data of different keys? This sounds like it could be a problem when it comes to consistency
when outputting to an external system.
>> 
>> Best,
>> Aljoscha
>> 
>>> On 17. May 2017, at 14:12, Flavio Pompermaier <pompermaier@okkam.it <mailto:pompermaier@okkam.it>>
wrote:
>>> 
>>> Hi to all,
>>> there are a lot of useful discussion points :)
>>> 
>>> I'll try to answer to everybody.
>>> 
>>> @Ankit: 
>>> right now we're using Parquet on HDFS to store thrift objects. Those objects
are essentially structured like
>>> key
>>> alternative_key
>>> list of tuples (representing the state of my Object)
>>> This model could be potentially modeled as a Monoid and it's very well suited
for a stateful streaming computation where updates to a single key state are not as expansive
as a call to any db to get the current list of tuples and update back that list with for an
update (IMHO). Maybe here I'm overestimating Flink streaming capabilities...
>>> serialization should be ok using thrift, but Flink advice to use tuples to have
better performance so just after reading the data from disk (as a ThriftObject) we convert
them to its equivalent representation as Tuple3<String, String, List<Tuple4>>
version
>>> Since I currently use Flink to ingest data that (in the end) means adding tuples
to my objects, it would be perfect to have an "online" state of the grouped tuples in order
to:
>>> add/remove tuples to my object very quickly
>>> from time to time, scan the whole online data (or a part of it) and "translate"
it into one ore more JSON indices (and put them into Elasticsearch)
>>> @Fabian:
>>> You're right that batch processes are bot very well suited to work with services
that can fail...if in a map function the remote call fails all the batch job fails...this
should be less problematic with streaming because there's checkpointing and with async IO
 is should be the possibile to add some retry/backoff policies in order to not overload remote
services like db or solr/es indices (maybe it's not already there but it should be possible
to add). Am I wrong?
>>> 
>>> @Kostas:
>>> 
>>> From what I understood Queryable state is usefult for gets...what if I need to
scan the entire db? For us it could be better do periodically dump the state to RocksDb or
HDFS but, as I already said, I'm not sure if it is safe to start a batch job that reads the
dumped data while, in the meantime, a possible update of this dump could happen...is there
any potential problem to data consistency (indeed tuples within grouped objects have references
to other objects keys)?
>>> 
>>> Best,
>>> Flavio
>>> 
>>> On Wed, May 17, 2017 at 10:18 AM, Kostas Kloudas <k.kloudas@data-artisans.com
<mailto:k.kloudas@data-artisans.com>> wrote:
>>> 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 <fhueske@gmail.com <mailto:fhueske@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.jain@here.com <mailto:ankit.jain@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 <pompermaier@okkam.it <mailto:pompermaier@okkam.it>>
>>>> Date: Tuesday, May 16, 2017 at 9:31 AM
>>>> To: Kostas Kloudas <k.kloudas@data-artisans.com <mailto:k.kloudas@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.kloudas@data-artisans.com
<mailto:k.kloudas@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 <pompermaier@okkam.it
<mailto:pompermaier@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>
>>> 
>>> 
>> 
>> 
> 
> 
> 


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