Hi Fabian,
First of all thanks for all your prompt responses. With regards to 2)
Multiple looks ups, I have to clarify what I mean by that...
DS1<String> elementKeyStream = stream1.map(String<>); this maps each
of the streaming elements into string mapped value...
DS2<T> = stream2.xxx(); // where stream2 is a kafka source stream, as
you proposed.. xxx() should be my function() which splits the string and
generates key1:<value1>, key2:<value2>, key3:<value3> ....keyN:<value4>
Now,
I wish to map elementKeyStream with look ups within (key1,
key2...keyN) where key1, key2.. keyN and their respective values should be
available across the cluster...
Thanks a million !
CVP
On Wed, Sep 7, 2016 at 9:15 PM, Fabian Hueske <fhue...@gmail.com> wrote:
> That depends.
> 1) Growing/Shrinking: This should work. New entries can always be
> inserted. In order to remove entries from the k-v-state you have to set the
> value to null. Note that you need an explicit delete-value record to
> trigger the eviction.
> 2) Multiple lookups: This does only work if all lookups are independent
> from each other. You can partition DS1 only on a single key and the other
> keys might be located on different shards. A workaround might be to
> duplicate S1 events for each key that they need to look up. However, you
> might need to collect events from the same S1 event after the join. If that
> does not work for you, the only thing that comes to my mind is to broadcast
> the state and keep a full local copy in each operator.
>
> Let me add one more thing regarding the upcoming rescaling feature. If
> this is interesting for you, rescaling will also work (maybe not in the
> first version) for broadcasted state, i.e. state which is the same on all
> parallel operator instances.
>
> 2016-09-07 21:45 GMT+02:00 Chakravarthy varaga <chakravarth...@gmail.com>:
>
>> I'm understanding this better with your explanation..
>> With this use case, each element in DS1 has to look up against a
>> 'bunch of keys' from DS2 and DS2 could shrink/expand in terms of the no.,
>> of keys.... will the key-value shard work in this case?
>>
>> On Wed, Sep 7, 2016 at 7:44 PM, Fabian Hueske <fhue...@gmail.com> wrote:
>>
>>> Operator state is always local in Flink. However, with key-value state,
>>> you can have something which behaves kind of similar to a distribute
>>> hashmap, because each operator holds a different shard/partition of the
>>> hashtable.
>>>
>>> If you have to do only a single key lookup for each element of DS1, you
>>> should think about partitioning both streams (keyBy) and writing the state
>>> into Flink's key-value state [1].
>>>
>>> This will have several benefits:
>>> 1) State does not need to be replicated
>>> 2) Depending on the backend (RocksDB) [2], parts of the state can reside
>>> on disk. You are not bound to the memory of the JVM.
>>> 3) Flink takes care of the look-up. No need to have your own hashmap.
>>> 4) It will only be possible to rescale jobs with key-value state (this
>>> feature is currently under development).
>>>
>>> If using the key-value state is possible, I'd go for that.
>>>
>>> Best, Fabian
>>>
>>> [1] https://ci.apache.org/projects/flink/flink-docs-release-1.1/
>>> apis/streaming/state.html
>>> [2] https://ci.apache.org/projects/flink/flink-docs-release-1.1/
>>> apis/streaming/state_backends.html
>>>
>>> 2016-09-07 19:55 GMT+02:00 Chakravarthy varaga <chakravarth...@gmail.com
>>> >:
>>>
>>>> certainly, what I thought as well...
>>>> The output of DataStream2 could be in 1000s and there are state
>>>> updates...
>>>> reading this topic from the other job, job1, is okie.
>>>> However, assuming that we maintain this state into a collection, and
>>>> updating the state (by reading from the topic) in this collection, will
>>>> this be replicated across the cluster within this job1 ?
>>>>
>>>>
>>>>
>>>> On Wed, Sep 7, 2016 at 6:33 PM, Fabian Hueske <fhue...@gmail.com>
>>>> wrote:
>>>>
>>>>> Is writing DataStream2 to a Kafka topic and reading it from the other
>>>>> job an option?
>>>>>
>>>>> 2016-09-07 19:07 GMT+02:00 Chakravarthy varaga <
>>>>> chakravarth...@gmail.com>:
>>>>>
>>>>>> Hi Fabian,
>>>>>>
>>>>>> Thanks for your response. Apparently these DataStream
>>>>>> (Job1-DataStream1 & Job2-DataStream2) are from different flink
>>>>>> applications
>>>>>> running within the same cluster.
>>>>>> DataStream2 (from Job2) applies transformations and updates a
>>>>>> 'cache' on which (Job1) needs to work on.
>>>>>> Our intention is to not use the external key/value store as we
>>>>>> are trying to localize the cache within the cluster.
>>>>>> Is there a way?
>>>>>>
>>>>>> Best Regards
>>>>>> CVP
>>>>>>
>>>>>> On Wed, Sep 7, 2016 at 5:00 PM, Fabian Hueske <fhue...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> Flink does not provide shared state.
>>>>>>> However, you can broadcast a stream to CoFlatMapFunction, such that
>>>>>>> each operator has its own local copy of the state.
>>>>>>>
>>>>>>> If that does not work for you because the state is too large and if
>>>>>>> it is possible to partition the state (and both streams), you can also
>>>>>>> use
>>>>>>> keyBy instead of broadcast.
>>>>>>>
>>>>>>> Finally, you can use an external system like a KeyValue Store or
>>>>>>> In-Memory store like Apache Ignite to hold your distributed collection.
>>>>>>>
>>>>>>> Best, Fabian
>>>>>>>
>>>>>>> 2016-09-07 17:49 GMT+02:00 Chakravarthy varaga <
>>>>>>> chakravarth...@gmail.com>:
>>>>>>>
>>>>>>>> Hi Team,
>>>>>>>>
>>>>>>>> Can someone help me here? Appreciate any response !
>>>>>>>>
>>>>>>>> Best Regards
>>>>>>>> Varaga
>>>>>>>>
>>>>>>>> On Mon, Sep 5, 2016 at 4:51 PM, Chakravarthy varaga <
>>>>>>>> chakravarth...@gmail.com> wrote:
>>>>>>>>
>>>>>>>>> Hi Team,
>>>>>>>>>
>>>>>>>>> I'm working on a Flink Streaming application. The data is
>>>>>>>>> injected through Kafka connectors. The payload volume is roughly
>>>>>>>>> 100K/sec.
>>>>>>>>> The event payload is a string. Let's call this as DataStream1.
>>>>>>>>> This application also uses another DataStream, call it
>>>>>>>>> DataStream2, (consumes events off a kafka topic). The elements of this
>>>>>>>>> DataStream2 involves in a certain transformation that finally updates
>>>>>>>>> a
>>>>>>>>> Hashmap(/Java util Collection). Apparently the flink application
>>>>>>>>> should
>>>>>>>>> share this HashMap across the flink cluster so that DataStream1
>>>>>>>>> application
>>>>>>>>> could check the state of the values in this collection. Is there a
>>>>>>>>> way to
>>>>>>>>> do this in Flink?
>>>>>>>>>
>>>>>>>>> I don't see any Shared Collection used within the cluster?
>>>>>>>>>
>>>>>>>>> Best Regards
>>>>>>>>> CVP
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
