Thanks Marco, I'll give it a try. cheers Noel
On Sat, Sep 17, 2022 at 7:14 PM Marco Villalobos <mvillalo...@kineteque.com> wrote: > > I might need more details, but conceptually, streams can be thought of as > never ending tables > and our code as functions applied to them. > > JOIN is a concept supported in the SQL API and DataStream API. > > However, the SQL API is more succinct (unlike my writing ;). > > So, how about the "fast stream" mapped to an SQL API Table and > the "slow" table mapped to SQL API versioned table that is joined with a > "temporal join." > > I'd try to use the SQL for the first part of the job to make this join, and > then if I need the DataStream API convert it. > > https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#temporal-joins > > > On Sep 17, 2022, at 9:29 AM, Noel OConnor <noel.ocon...@gmail.com> wrote: > > Hi, > I'm trying to determine the best way to enrich the event payload of a > fast moving incoming stream with values in another stream which is far > more slow moving. > I'm converting the second stream into a table for continuous query > functionality and I wonder what is the best way to take the values of > that query and enrich the fast moving stream. > > Is it best to store the output of the continuous query in a value > state and access this in a process function being applied to the fast > moving stream? > Or do I execute the query on the table created by the slow moving > stream as part of a map function on the fast moving stream. > > I suspect there's multiple ways to do this but I want to use the more > appropriate method for flink. > > > cheers > Noel > >
