Hey guys, Sorry for confusion it turned out that I had a bug in my code, when I was not clearing this list in my batch object on each apply call. Forgot it has to be added since its different than fold. Which led to so high throughput. When I fixed this I was back to 160k per sec. I'm still investigating how I can speed it up.
As a side note its quite interesting that hbase was able to do 2millions puts per second. But most of them were already stored with previous call so perhaps internally he is able to distinguish in memory if a put was stored or not. Not sure. Anyway my claim about window vs fold performance difference was wrong. So forget about it ;) On Wed, Mar 29, 2017 at 12:21 PM, Timo Walther <twal...@apache.org> wrote: > Hi Kamil, > > the performance implications might be the result of which state the > underlying functions are using internally. WindowFunctions use ListState or > ReducingState, fold() uses FoldingState. It also depends on the size of > your state and the state backend you are using. I recommend the following > documentation page. The FoldingState might be deprecated soon, once a > better alternative is available: https://ci.apache.org/ > projects/flink/flink-docs-release-1.2/dev/stream/state. > html#using-managed-keyed-state > > I hope that helps. > > Regards, > Timo > > Am 29/03/17 um 11:27 schrieb Kamil Dziublinski: > > Hi guys, > > I’m using flink on production in Mapp. We recently swapped from storm. > Before I have put this live I was doing performance tests and I found > something that “feels” a bit off. > I have a simple streaming job reading from kafka, doing window for 3 > seconds and then storing into hbase. > > Initially we had this second step written with a fold function, since I > thought performance and resource wise it’s a better idea. > But I couldn’t reach more than 120k writes per second to HBase and I > thought hbase sink is a bottlenck here. But then I tried doing the same > with window function and my performance jumped to 2 millions writes per > second. Just wow :) Comparing to storm where I had max 320k per second it > is amazing. > > Both fold and window functions were doing the same thing, taking together > all the records for the same tenant and user (key by is used for that) and > putting it in one batched object with arraylists for the mutations on user > profile. After that passing this object to the sink. I can post the code if > its needed. > > In case of fold I was just adding profile mutation to the list and in case > of window function iterating over all of it and returning this batched > entity in one go. > > I’m wondering if this is expected to have 20 times slower performance just > by using fold function. I would like to know what is so costly about this, > as intuitively I would expect fold function being a better choice here > since I assume that window function is using more memory for buffering. > > Also my colleagues when they were doing PoC on flink evaluation they were > seeing very similar results to what I am seeing now. But they were still > using fold function. This was on flink version 1.0.3 and now I am using > 1.2.0. So perhaps there is some regression? > > Please let me know what you think. > > Cheers, > Kamil. > > >
