Hi, Thanks for your responses.
There is no fixed interval for the data being updated. It’s more like whenever you onboard a new product or there are any mandates that change will trigger the reference data to change. It’s not just the enrichment we are doing here. Once we have enriched the data we will be performing a bunch of aggregations using the enriched data. Which approach would you recommend? Regards, Harshvardhan On Tue, Jul 24, 2018 at 04:04 Jain, Ankit <ankit.j...@here.com> wrote: > How often is the product db updated? Based on that you can store product > metadata as state in Flink, maybe setup the state on cluster startup and > then update daily etc. > > > > Also, just based on this feature, flink doesn’t seem to add a lot of value > on top of Kafka. As Jorn said below, you can very well store all the events > in an external store and then periodically run a cron to enrich later since > your processing doesn’t seem to require absolute real time. > > > > Thanks > > Ankit > > > > *From: *Jörn Franke <jornfra...@gmail.com> > *Date: *Monday, July 23, 2018 at 10:10 PM > *To: *Harshvardhan Agrawal <harshvardhan.ag...@gmail.com> > *Cc: *<user@flink.apache.org> > *Subject: *Re: Implement Joins with Lookup Data > > > > For the first one (lookup of single entries) you could use a NoSQL db (eg > key value store) - a relational database will not scale. > > > > Depending on when you need to do the enrichment you could also first store > the data and enrich it later as part of a batch process. > > > On 24. Jul 2018, at 05:25, Harshvardhan Agrawal < > harshvardhan.ag...@gmail.com> wrote: > > Hi, > > > > We are using Flink for financial data enrichment and aggregations. We have > Positions data that we are currently receiving from Kafka. We want to > enrich that data with reference data like Product and Account information > that is present in a relational database. From my understanding of Flink so > far I think there are two ways to achieve this. Here are two ways to do it: > > > > 1) First Approach: > > a) Get positions from Kafka and key by product key. > > b) Perform lookup from the database for each key and then obtain > Tuple2<Position, Product> > > > > 2) Second Approach: > > a) Get positions from Kafka and key by product key. > > b) Window the keyed stream into say 15 seconds each. > > c) For each window get the unique product keys and perform a single lookup. > > d) Somehow join Positions and Products > > > > In the first approach we will be making a lot of calls to the DB and the > solution is very chatty. Its hard to scale this cos the database storing > the reference data might not be very responsive. > > > > In the second approach, I wish to join the WindowedStream with the > SingleOutputStream and turns out I can't join a windowed stream. So I am > not quite sure how to do that. > > > > I wanted an opinion for what is the right thing to do. Should I go with > the first approach or the second one. If the second one, how can I > implement the join? > > > > -- > > > *Regards, Harshvardhan Agrawal* > > -- Regards, Harshvardhan
