Hi, For the above use case, you should do the following :
1. Convert your DataStream into KeyedDataStream by defining a key which would be used to get validated against your rules. 2. Same as 1 for rules stream. 3. Join the two keyedStreams using Flink's connect operator. 4. Store the rules into Flink's internal state i,e. Flink's managed keyed state. 5. Validate the data coming in the dataStream against the managed keyed state. Refer to [1] [2] for more details. [1] https://ci.apache.org/projects/flink/flink-docs-release-1.5/dev/stream/ operators/ [2] https://ci.apache.org/projects/flink/flink-docs-release-1.5/dev/stream/state/state.html On Tue, Jun 5, 2018 at 5:52 PM, Sandybayev, Turar (CAI - Atlanta) < turar.sandyba...@coxautoinc.com> wrote: > Hi, > > > > What is the best practice recommendation for the following use case? We > need to match a stream against a set of “rules”, which are essentially a > Flink DataSet concept. Updates to this “rules set" are possible but not > frequent. Each stream event must be checked against all the records in > “rules set”, and each match produces one or more events into a sink. Number > of records in a rule set are in the 6 digit range. > > > > Currently we're simply loading rules into a local List of rules and using > flatMap over an incoming DataStream. Inside flatMap, we're just iterating > over a list comparing each event to each rule. > > > > To speed up the iteration, we can also split the list into several > batches, essentially creating a list of lists, and creating a separate > thread to iterate over each sub-list (using Futures in either Java or > Scala). > > > > Questions: > > 1. Is there a better way to do this kind of a join? > > 2. If not, is it safe to add additional parallelism by creating > new threads inside each flatMap operation, on top of what Flink is already > doing? > > > > Thanks in advance! > > Turar > > > -- Garvit Sharma github.com/garvitlnmiit/ No Body is a Scholar by birth, its only hard work and strong determination that makes him master.
