In a streaming job it'll be roughly once per thread per worker, and Dataflow Streaming runner may create hundreds of threads per worker because it assumes that they are not heavyweight and that low latency is the primary goal rather than high throughput (as in batch runner).A hacky way to limit this parallelism would be to emulate the "repartition", by inserting a chain of transforms: pair with a random key in [0,n), group by key, ungroup - procesing of the result until the next GBK will not be parallelized more than n-wise in practice in the Dataflow streaming runner, so in the particular case of JdbcIO.write() with its current implementation it should help. It may break in the future, e.g. if JdbcIO.write() ever changes to include a GBK before writing. Unfortunately I can't recommend a long-term reliable solution for the moment.On Wed, Mar 14, 2018 at 12:57 PM Aleksandr <aleksandr.vl@xxxxxxxxx> wrote:Hello,How many times will the setup per node be called? Is it possible to limit pardo intances in google dataflow?Aleksandr.14. märts 2018 9:22 PM kirjutas kuupäeval "Eugene Kirpichov" <kirpichov@xxxxxxxxxx>:"Jdbcio will create for each prepared statement new connection" - this is not the case: the connection is created in @Setup and deleted in @Teardown.https://github.com/apache/
beam/blob/v2.3.0/sdks/java/io/ jdbc/src/main/java/org/apache/ beam/sdk/io/jdbc/JdbcIO.java# L503https://github.com/apache/ beam/blob/v2.3.0/sdks/java/io/ jdbc/src/main/java/org/apache/ beam/sdk/io/jdbc/JdbcIO.java# L631Something else must be going wrong.On Wed, Mar 14, 2018 at 12:11 PM Aleksandr <aleksandr.vl@xxxxxxxxx> wrote:Hello, we had similar problem. Current jdbcio will cause alot of connection errors.Typically you have more than one prepared statement. Jdbcio will create for each prepared statement new connection(and close only in teardown) So it is possible that connection will get timeot or in case in case of auto scaling you will get to many connections to sql.Our solution was to create connection pool in setup and get connection and return back to pool in processElement.Best Regards,Aleksandr Gortujev.14. märts 2018 8:52 PM kirjutas kuupäeval "Jean-Baptiste Onofré" <jb@xxxxxxxxxxxx>:Agree especially using the current JdbcIO impl that creates connection in the @Setup. Or it means that @Teardown is never called ?RegardsJBLe 14 mars 2018, à 11:40, Eugene Kirpichov <kirpichov@xxxxxxxxxx> a écrit:Hi Derek - could you explain where does the "3000 connections" number come from, i.e. how did you measure it? It's weird that 5-6 workers would use 3000 connections.On Wed, Mar 14, 2018 at 3:50 AM Derek Chan <derekcsy@xxxxxxxxx> wrote:Hi,
We are new to Beam and need some help.
We are working on a flow to ingest events and writes the aggregated
counts to a database. The input rate is rather low (~2000 message per
sec), but the processing is relatively heavy, that we need to scale out
to 5~6 nodes. The output (via JDBC) is aggregated, so the volume is also
low. But because of the number of workers, it keeps 3000 connections to
the database and it keeps hitting the database connection limits.
Is there a way that we can reduce the concurrency only at the output
stage? (In Spark we would have done a repartition/coalesce).
And, if it matters, we are using Apache Beam 2.2 via Scio, on Google
Thank you in advance!