Flink's built-in JDBC connector will read the data only once. JDBC does
not provide means to continously monitor a database table.
It depends on the size of your database, if you parameter table is small
it might be sufficient to write a simple Flink connector that
periodically reads the table and ingests the data to the streaming
pipeline. For larger database/streaming integrations, it might be worth
to look into Kafka's Connect API. Also Debezium where you hook into
database logs for retrieving continous data but this might be overkill
for your usecase.
The link that I've sent you to for streaming pattern slides should work
after registration.
Regards,
Timo
On 09.09.20 09:49, s_penakalap...@yahoo.com wrote:
Hi Timo,
Thank you for the suggestions.
I see now both Process function and CEP approach will not fit in. Now if
I follow the third approach to stream the values from database() . Is it
possible to stream data continuously?
If I follow the bellow approach, both I see one time load only not
continuously stream
Using JDBCInputFormat this will execute the query only once , so it will
not be a stream data. when we try to iterate source this may iterate
only on the data already fetched
Using RichFlatMapFunctions, in open() if I try to connect to DB again
this would be one time load. If I connect database in flatmap() then it
would lead to multiple hits to database.
Request your help on how to continuously stream the data, If possible
sample source code for reference to stream database. Please help me
badly stuck.
In the mail, I see you asked me to register. Are you referring to any
training here or any other registration.
Regards,
Sunitha.
On Tuesday, September 8, 2020, 08:19:49 PM GMT+5:30, Timo Walther
<twal...@apache.org> wrote:
Hi Sunitha,
what you are describing is a typical streaming enrichment. We need to
enrich the stream with some data from a database. There are different
strategies to handle this:
1) You are querying the database for every record. This is usually not
what you want because it would slow down your pipeline due to the
communication latenties to your database. It would also cause a lot of
pressure to the database in general.
2) You only query database from time to time and store the latest value
in a ProcessFunction ValueState or MapState.
3) You stream in the values as well and use connect() [1].
In any case, I think CEP might not be useful for this case. If you
really want to do option 1, it might make sense to also checkout the SQL
API of Flink because it offers different kind of joins with very good
abstraction. `Join with a Temporal Table` offers a JDBC connector for
lookups in your database.
If you like to use DataStream API, I would also recommend the Pattern
slides here [3] (unfortunately you have to register first).
Regards,
Timo
[1]
https://ci.apache.org/projects/flink/flink-docs-release-1.11/learn-flink/etl.html#connected-streams
[2]
https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/streaming/joins.html
[3] https://training.ververica.com/decks/patterns/
On 07.09.20 17:25, s_penakalap...@yahoo.com
<mailto:s_penakalap...@yahoo.com> wrote:
> Hi All,
>
> I am new to Flink, request your help!!!
>
> My scenario :
> 1> we receive Json messages at a very high frequency like 10,000
> messages / second
> 2> we need to raise an Alert for a particular user if there is any
> breach in threshold value against each attribute in Json.
> 3> These threshold values are part of my database table and can be
> frequently updated by different user.
> 4> In realtime I would like to know how to get latest data from the
> database.
>
> I tried using Flink CEP Pattern approach to generate alert. I would like
> to get some inputs on how I can implement the realtime lookup tables in
> Flink Java while monitoring alert, any sample code reference.
>
> Also for such scenarios do you recommend to use Flink CEP approach or we
> need to use Process function approach.
>
>
> Regards,
> Sunitha.
