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From Fabian Hueske <fhue...@gmail.com>
Subject Re: data enrichment with SQL use case
Date Tue, 24 Apr 2018 10:01:47 GMT
Hi Alex,

An operator that has to join two input streams obviously requires two
inputs. In case of an enrichment join, the operator should first read the
meta-data stream and build up a data structure as state against which the
other input is joined. If the meta data is (infrequently) updated, these
updates should be integrated into the state.

The problem is that it is currently not possible to implement such an
operator with Flink because operators cannot decide from which input to
read, i.e., they have to process whatever data is given to them.
Hence, it is not possible to build up a data structure from the meta data
stream before consuming the other stream.

There are a few workarounds that work in special cases.
1) The meta data is rather small and never updated. You put the meta data
as a file into a (distributed) file system an read it from each function
instance when it is initialized, i.e., in open(), and put into a hash map.
Each function instance will hold the complete meta data in memory (on the
heap). Since the meta data is broadcasted, the other stream does not need
to be partitioned to join against the meta data in the hash map. You can
implement this function as a FlatMapFunction or ProcessFunction.
2) The meta data is too large and/or is updated. In this case, you need a
function with two inputs. Both inputs are keyed (keyBy()) on a join
attribute. Since you cannot hold back the non-meta data stream, you need to
buffer it in (keyed) state until you've read the meta data stream up to a
point when you can start processing the other stream. If the meta data is
updated at some point, you can just add the new data to the state. The
benefits of this approach is that the state is shared across all operators
and can be updated. However, you might need to initially buffer quite a bit
of data in state if the non-meta data stream has a high volume.

Hope that one of these approaches works for your use case.

Best, Fabian

2018-04-23 13:29 GMT+02:00 Alexander Smirnov <alexander.smirnoff@gmail.com>:

> Hi Fabian,
> please share the workarounds, that must be helpful for my case as well
> Thank you,
> Alex
> On Mon, Apr 23, 2018 at 2:14 PM Fabian Hueske <fhueske@gmail.com> wrote:
>> Hi Miki,
>> Sorry for the late response.
>> There are basically two ways to implement an enrichment join as in your
>> use case.
>> 1) Keep the meta data in the database and implement a job that reads the
>> stream from Kafka and queries the database in an ASyncIO operator for every
>> stream record. This should be the easier implementation but it will send
>> one query to the DB for each streamed record.
>> 2) Replicate the meta data into Flink state and join the streamed records
>> with the state. This solution is more complex because you need propagate
>> updates of the meta data (if there are any) into the Flink state. At the
>> moment, Flink lacks a few features to have a good implementation of this
>> approach, but there a some workarounds that help in certain cases.
>> Note that Flink's SQL support does not add advantages for the either of
>> both approaches. You should use the DataStream API (and possible
>> ProcessFunctions).
>> I'd go for the first approach if one query per record is feasible.
>> Let me know if you need to tackle the second approach and I can give some
>> details on the workarounds I mentioned.
>> Best, Fabian
>> 2018-04-16 20:38 GMT+02:00 Ken Krugler <kkrugler_lists@transpac.com>:
>>> Hi Miki,
>>> I haven’t tried mixing AsyncFunctions with SQL queries.
>>> Normally I’d create a regular DataStream workflow that first reads from
>>> Kafka, then has an AsyncFunction to read from the SQL database.
>>> If there are often duplicate keys in the Kafka-based stream, you could
>>> keyBy(key) before the AsyncFunction, and then cache the result of the SQL
>>> query.
>>> — Ken
>>> On Apr 16, 2018, at 11:19 AM, miki haiat <miko5054@gmail.com> wrote:
>>> HI thanks  for the reply  i will try to break your reply to the flow
>>> execution order .
>>> First data stream Will use AsyncIO and select the table ,
>>> Second stream will be kafka and the i can join the stream and map it ?
>>> If that   the case  then i will select the table only once on load ?
>>> How can i make sure that my stream table is "fresh" .
>>> Im thinking to myself , is thire a way to use flink backend (ROKSDB)
>>> and create read/write through
>>> macanisem ?
>>> Thanks
>>> miki
>>> On Mon, Apr 16, 2018 at 2:45 AM, Ken Krugler <
>>> kkrugler_lists@transpac.com> wrote:
>>>> If the SQL data is all (or mostly all) needed to join against the data
>>>> from Kafka, then I might try a regular join.
>>>> Otherwise it sounds like you want to use an AsyncFunction to do ad hoc
>>>> queries (in parallel) against your SQL DB.
>>>> https://ci.apache.org/projects/flink/flink-docs-release-1.4/dev/stream/
>>>> operators/asyncio.html
>>>> — Ken
>>>> On Apr 15, 2018, at 12:15 PM, miki haiat <miko5054@gmail.com> wrote:
>>>> Hi,
>>>> I have a case of meta data enrichment and im wondering if my approach
>>>> is the correct way .
>>>>    1. input stream from kafka.
>>>>    2. MD in msSQL .
>>>>    3. map to new pojo
>>>> I need to extract  a key from the kafka stream   and use it to select
>>>> some values from the sql table  .
>>>> SO i thought  to use  the table SQL api in order to select the table MD
>>>> then convert the kafka stream to table and join the data by  the stream
>>>> key .
>>>> At the end i need to map the joined data to a new POJO and send it to
>>>> elesticserch .
>>>> Any suggestions or different ways to solve this use case ?
>>>> thanks,
>>>> Miki
>>>> --------------------------
>>>> Ken Krugler
>>>> http://www.scaleunlimited.com
>>>> custom big data solutions & training
>>>> Hadoop, Cascading, Cassandra & Solr
>>> --------------------------------------------
>>> http://about.me/kkrugler
>>> +1 530-210-6378 <(530)%20210-6378>

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