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From: Sand Stone <sand.m.stone@gmail.com>
Date: Fri, 19 May 2017 10:03:17 -0700
Message-ID: <CADrk5qPF_jGaLmpwy2ASk-vShFkVGwixvq15iqDBbLtNNSJoGw@mail.gmail.com>
Subject: Re: Best practices to maintain reference data for Flink Jobs
To: Fabian Hueske <fhueske@gmail.com>
Cc: "Tzu-Li (Gordon) Tai" <tzulitai@apache.org>, user <user@flink.apache.org>
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archived-at: Fri, 19 May 2017 17:03:28 -0000

Thanks Gordon and Fabian.

The enriching data is really reference data, e.g. the reverseIP
database. It's hard to be keyed as the main data stream as the "ip
address" in the event is not a primary key in the main data stream.

QueryableState is close, but it does not support range scan as far as
I could tell. The remote datastore has a clean semantics: a logical
single copy plus supports range scan, but the RPC to another cluster
is not optimal.

I assume this is a quite common streaming processing pattern for Flink
based services.


On Fri, May 19, 2017 at 2:08 AM, Fabian Hueske <fhueske@gmail.com> wrote:
> +1 to what Gordon said.
>
> Queryable state is rather meant as an external interface to streaming job=
s
> than for lookups within jobs.
> Accessing co-located state should give you better performance and is
> probably easier to implement and maintain.
>
> Cheers,
> Fabian
>
> 2017-05-19 7:43 GMT+02:00 Tzu-Li (Gordon) Tai <tzulitai@apache.org>:
>>
>> Hi,
>>
>> Can the enriching data be keyed? Or is it something that has to be
>> broadcasted to each operator?
>> Either way, I think Side Inputs (an upcoming feature in the future) is t=
he
>> best fit for this. You can take a look at
>> https://issues.apache.org/jira/browse/FLINK-6131.
>>
>> Regarding the 3 options you listed:
>>
>> By using QueryableState in option B, what you mean is that you want to
>> feed the enriching data stream to a separate job, let that job allow
>> queryable state, and query that state from the actual application job
>> operators, correct? If so, I think options A and B would mean the same
>> thing; i.e., they require accessing data external to the job.
>>
>> If the enriching data can somehow be keyed with the stream that requires
>> it, I would go for option C using connected streams, with the enriching =
data
>> as one input and the actual data as the other. Instead of just =E2=80=9C=
caching the
>> enriching data in memory=E2=80=9D, you should register it as a managed L=
ink state
>> for the CoMapFunction / CoFlatMapFunction. The actual input stream recor=
ds
>> can just access that registered state locally.
>>
>> Cheers,
>> Gordon
>>
>>
>> On 19 May 2017 at 7:11:07 AM, Sand Stone (sand.m.stone@gmail.com) wrote:
>>
>> Hi. Say I have a few reference data sets need to be used for a
>> streaming job. The sizes range between 10M-10GB. The data is not
>> static, will be refreshed at minutes and/or day intervals.
>>
>> With the new advancements in Flink, it seems there are quite a few
>> options.
>> A. Store all the data in an external (kv) database cluster. And use
>> async io calls
>> * data refresh can be done in a few different ways
>> B. Use the new Querytable State feature
>> * it seems there is no "easy" API to discover the
>> queryable state at the moment. Need to use the restful API to figure
>> out the job id.
>> C. Ingest the reference data into the job and cache them in memory
>> Any other option?
>>
>> On paper, it seems option B with the Queryable State is the cleanest
>> solution.
>>
>> Any comment/suggestion is greatly appreciated in particular in terms
>> of robustness and consistent recovery.
>>
>> Thanks much!
>
>