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.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.There are basically two ways to implement an enrichment join as in your use case.Hi Miki,Sorry for the late response.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, Fabian2018-04-16 20:38 GMT+02:00 Ken Krugler <[hidden email]>: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.— KenOn Apr 16, 2018, at 11:19 AM, miki haiat <[hidden email]> 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 ?ThanksmikiOn Mon, Apr 16, 2018 at 2:45 AM, Ken Krugler <[hidden email]> 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.— KenOn Apr 15, 2018, at 12:15 PM, miki haiat <[hidden email]> wrote:Hi,I have a case of meta data enrichment and im wondering if my approach is the correct way .
- input stream from kafka.
- MD in msSQL .
- 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 Kruglercustom big data solutions & trainingHadoop, Cascading, Cassandra & Solr--------------------------------------------<a href="tel:(530)%20210-6378" value="+15302106378" target="_blank">+1 530-210-6378
| Free forum by Nabble | Edit this page |