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From Gianmarco De Francisci Morales <g...@apache.org>
Subject Re: Streaming KV store abstraction
Date Wed, 09 Sep 2015 12:29:03 GMT
Just a silly question.
For the example you described, in a data flow model, you would do something
like this:

Have query ids added to the city pairs (qid, city1, city2),
then split the query stream on the two cities and co-group it with the
updates stream ((city1, qid) , (city, temp)), same for city2,
then emit (qid, city1, temp1), (qid, city2, temp2) from the two co-groups,
group on the qid, and apply a difference operator to get the final answer.

Is your  idea to implement a way to generalize this logic, or it would use
remote read/write to a KV-store?

--
Gianmarco

On 8 September 2015 at 16:27, Aljoscha Krettek <aljoscha@apache.org> wrote:

> That's a very nice application of the Stream API and partitioned state. :D
>
> I think we should run some tests on a cluster  based on this to see what
> kind of throughput the partitioned state system can handle and also how it
> behaves with larger numbers of keys. The KVStore is just an interface and
> the really heavy lifting is done by the state system so this would be a
> good test for it.
>
>
> On Tue, 8 Sep 2015 at 15:10 Gyula Fóra <gyula.fora@gmail.com> wrote:
>
> > @Stephan:
> >
> > Technically speaking this is really just a partitioned key-value state
> and
> > a fancy operator executing special operations on this state.
> >
> > From the user's perspective though this is something hard to implement.
> If
> > you want to share state between two stream for instance this way (getting
> > updates from one stream and enriching the other one) you would probably
> use
> > a connected datastream and custom implement the Key-value store logic.
> But
> > once you have one(or more) update stream and many get streams this
> > implementation will not work. So either the user end up replicating the
> > whole state in multiple connected operators, or custom implement some
> > inefficient wrapper class to take care of all the put/get operations.
> >
> > The Idea behind this is to give a very simple abstraction for this type
> of
> > processing that uses the flink runtime efficiently instead of relying on
> > custom implementations.
> >
> > Let me give you a stupid example:
> >
> > You receive Temperature data in the form of (city, temperature), and you
> > are computing a rolling avg for each city.
> > Now you have 2 other incoming streams: first is a stream of some other
> info
> > about the city let's say population (city, population) and you want to
> > combine it with the last known avg temperature to produce (city, temp,
> pop)
> > triplets. The second stream is a pair of cities (city,city) and you are
> > interested in the difference of the temperature.
> >
> > For enriching the (city, pop) to (city,temp,pop) you would probably use a
> > CoFlatMap and store the last known rolling avg as state. For computing
> the
> > (city,city) temperature difference it is a little more difficult, as you
> > need to get the temperature for both cities then combine in a second
> > operator. If you don't want to replicate your state, you have to combine
> > these two problems to a common wrapper type and execute them on a same
> > operator which will keep the avg state.
> >
> > With the KVStore abstraction this is very simple:
> > you create a KVStore<City, Temp>
> > For enriching you use kvStore.getWithKeySelector() which will give you
> > ((cit,pop), temp) pairs and you are done. For computing the difference,
> you
> > can use kvStore.multiget(...) and get for the 2 cities at the same type.
> > The kv store will abstract away the getting of the 2 keys separately and
> > merging them so it will return [(city1, t1), (city2,t2)].
> >
> > This might be slightly artificial example but I think it makes the point.
> > Implementing these jobs efficiently is not trivial for the users but I
> > think it is a very common problem.
> >
> > Stephan Ewen <sewen@apache.org> ezt írta (időpont: 2015. szept. 8., K,
> > 14:53):
> >
> > > @Gyula
> > >
> > > Can you explain a bit what this KeyValue store would do more then the
> > > partitioned key/value state?
> > >
> > > On Tue, Sep 8, 2015 at 2:49 PM, Gábor Gévay <ggab90@gmail.com> wrote:
> > >
> > > > Hello,
> > > >
> > > > As for use cases, in my old job at Ericsson we were building a
> > > > streaming system that was processing data from telephone networks,
> and
> > > > it was using key-value stores a LOT. For example, keeping track of
> > > > various state info of the users (which cell are they currently
> > > > connected to, what bearers do they have, ...); mapping from IDs of
> > > > users in one subsystem of the telephone network to the IDs of the
> same
> > > > users in an other subsystem; mapping from IDs of phone calls to lists
> > > > of IDs of participating users; etc.
> > > > So I imagine they would like this a lot. (At least, if they were
> > > > considering moving to Flink :))
> > > >
> > > > Best,
> > > > Gabor
> > > >
> > > >
> > > >
> > > >
> > > > 2015-09-08 13:35 GMT+02:00 Gyula Fóra <gyfora@apache.org>:
> > > > > Hey All,
> > > > >
> > > > > The last couple of days I have been playing around with the idea
of
> > > > > building a streaming key-value store abstraction using stateful
> > > streaming
> > > > > operators that can be used within Flink Streaming programs
> > seamlessly.
> > > > >
> > > > > Operations executed on this KV store would be fault tolerant as it
> > > > > integrates with the checkpointing mechanism, and if we add
> timestamps
> > > to
> > > > > each put/get/... operation we can use the watermarks to create
> fully
> > > > > deterministic results. This functionality is very useful for many
> > > > > applications, and is very hard to implement properly with some
> > > dedicates
> > > > kv
> > > > > store.
> > > > >
> > > > > The KVStore abstraction could look as follows:
> > > > >
> > > > > KVStore<K,V> store = new KVStore<>;
> > > > >
> > > > > Operations:
> > > > >
> > > > > store.put(DataStream<Tuple2<K,V>>)
> > > > > store.get(DataStream<K>) -> DataStream<KV<K,V>>
> > > > > store.remove(DataStream<K>) -> DataStream<KV<K,V>>
> > > > > store.multiGet(DataStream<K[]>) -> DataStream<KV<K,V>[]>
> > > > > store.getWithKeySelector(DataStream<X>, KeySelector<X,K>)
->
> > > > > DataStream<KV<X,V>[]>
> > > > >
> > > > > For the resulting streams I used a special KV abstraction which
> let's
> > > us
> > > > > return null values.
> > > > >
> > > > > The implementation uses a simple streaming operator for executing
> > most
> > > of
> > > > > the operations (for multi get there is an additional merge
> operator)
> > > with
> > > > > either local or partitioned states for storing the kev-value pairs
> > (my
> > > > > current prototype uses local states). And it can either execute
> > > > operations
> > > > > eagerly (which would not provide deterministic results), or buffer
> up
> > > > > operations and execute them in order upon watermarks.
> > > > >
> > > > > As for use cases you can probably come up with many I will save
> that
> > > for
> > > > > now :D
> > > > >
> > > > > I have a prototype implementation here that can execute the
> > operations
> > > > > described above (does not handle watermarks and time yet):
> > > > >
> > > > > https://github.com/gyfora/flink/tree/KVStore
> > > > > And also an example job:
> > > > >
> > > > >
> > > >
> > >
> >
> https://github.com/gyfora/flink/blob/KVStore/flink-staging/flink-streaming/flink-streaming-core/src/test/java/org/apache/flink/streaming/api/KVStreamExample.java
> > > > >
> > > > > What do you think?
> > > > > If you like it I will work on writing tests and it still needs a
> lot
> > of
> > > > > tweaking and refactoring. This might be something we want to
> include
> > > with
> > > > > the standard streaming libraries at one point.
> > > > >
> > > > > Cheers,
> > > > > Gyula
> > > >
> > >
> >
>

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