BTW, We got around bootstrap problem for similar use case using a “nohup” topic as input stream. Our CICD pipeline currently passes an initialize option to app IF there is a need to bootstrap and waits for X minutes before taking a savepoint and restart app normally listening to right topic(s). I believe there is work underway to handle this gracefully using Side Input as well. Other than determining X minutes for initialization to complete, we havent had any issue with this solution - we have over 40 million states refreshes daily and close to 200Mbps input streams being joined to states. Hope this helps!
- Ashish On Tuesday, July 24, 2018, 11:37 AM, Elias Levy <fearsome.lucid...@gmail.com> wrote: Alas, this suffer from the bootstrap problem. At the moment Flink does not allow you to pause a source (the positions), so you can't fully consume the and preload the accounts or products to perform the join before the positions start flowing. Additionally, Flink SQL does not support materializing an upset table for the accounts or products to perform the join, so yo have to develop your own KeyedProcessFunction, maintain the state, and perform the join on your own if you only want to join against the latest value for each key. On Tue, Jul 24, 2018 at 7:27 AM Till Rohrmann <trohrm...@apache.org> wrote: Yes, using Kafka which you initialize with the initial values and then feed changes to the Kafka topic from which you consume could be a solution. On Tue, Jul 24, 2018 at 3:58 PM Harshvardhan Agrawal <harshvardhan.ag...@gmail.com> wrote: Hi Till, How would we do the initial hydration of the Product and Account data since it’s currently in a relational DB? Do we have to copy over data to Kafka and then use them? Regards,Harsh On Tue, Jul 24, 2018 at 09:22 Till Rohrmann <trohrm...@apache.org> wrote: Hi Harshvardhan, I agree with Ankit that this problem could actually be solved quite elegantly with Flink's state. If you can ingest the product/account information changes as a stream, you can keep the latest version of it in Flink state by using a co-map function [1, 2]. One input of the co-map function would be the product/account update stream which updates the respective entries in Flink's state and the other input stream is the one to be enriched. When receiving input from this stream one would lookup the latest information contained in the operator's state and join it with the incoming event. [1] https://ci.apache.org/projects/flink/flink-docs-master/dev/stream/operators/[2] https://ci.apache.org/projects/flink/flink-docs-master/api/java/org/apache/flink/streaming/api/functions/co/CoMapFunction.html Cheers,Till On Tue, Jul 24, 2018 at 2:15 PM Harshvardhan Agrawal <harshvardhan.ag...@gmail.com> wrote: 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 -- Regards, Harshvardhan
