Hi Sidney, there could be a couple of reasons where scaling actually hurts. Let's include them one by one.
First, you need to make sure that your source actually supports scaling. Thus, your Kafka topic needs at least as many partitions as you want to scale. So if you want to scale at some point to 66 parallel instances. Your kafka topic must have at least 66 partitions. Ofc, you can also read from less partitions, but then some source subtasks are idling. That's valid if your downstream pipeline is much more resource intensive. Also note that it's really hard to increase the number of Kafka partitions later, so please plan accordingly. Second, you have a Windowing operation that uses hashes. It's really important to check if the hashes are evenly distributed. So you first could have an issue that most records share the same key, but you could on top have the issue that different keys share the same hash. In these cases, most records are processed by the same subtask resulting in poor overall performance. (You can check for data skew incl. hash skew in Web UI). Third, make sure that there is actually enough data to be processed. Does your topic contain enough data? If you want to process historic data, did you choose the correct consumer setting? Can your Kafka cluster provide enough data to the Flink job? If your max data rate is 6k records from Kafka, then ofc the per slot throughput decreases on scaling up. Fourth, if you suspect that the clumping of used slots to one task manager may be an issue, try out the configuration cluster-evenly-spread-out-slots [1]. The basic idea is to use a TM fully first to allow easier scale-in. However, if for some reason your TM is more quickly saturated than it has slots, you may try to spread evenly. However, you may also want to check if you declare too many slots for each TM (in most cases slots = cores). [1] https://ci.apache.org/projects/flink/flink-docs-release-1.10/ops/config.html#cluster-evenly-spread-out-slots . On Tue, Nov 3, 2020 at 4:01 AM Yangze Guo <karma...@gmail.com> wrote: > Hi, Sidney, > > What is the data generation rate of your Kafka topic? Is it a lot > bigger than 6000? > > Best, > Yangze Guo > > Best, > Yangze Guo > > > On Tue, Nov 3, 2020 at 8:45 AM Sidney Feiner <sidney.fei...@startapp.com> > wrote: > > > > Hey, > > I'm writing a Flink app that does some transformation on an event > consumed from Kafka and then creates time windows keyed by some field, and > apply an aggregation on all those events. > > When I run it with parallelism 1, I get a throughput of around 1.6K > events per second (so also 1.6K events per slot). With parallelism 5, that > goes down to 1.2K events per slot, and when I increase the parallelism to > 10, it drops to 600 events per slot. > > Which means that parallelism 5 and parallelism 10, give me the same > total throughput (1.2x5 = 600x10). > > > > I noticed that although I have 3 Task Managers, all the all the tasks > are run on the same machine, causing it's CPU to spike and probably, this > is the reason that the throughput dramatically decreases. After increasing > the parallelism to 15 and now tasks run on 2/3 machines, the average > throughput per slot is still around 600. > > > > What could cause this dramatic decrease in performance? > > > > Extra info: > > > > Flink version 1.9.2 > > Flink High Availability mode > > 3 task managers, 66 slots total > > > > > > Execution plan: > > > > > > Any help would be much appreciated > > > > > > Sidney Feiner / Data Platform Developer > > M: +972.528197720 / Skype: sidney.feiner.startapp > > > > > -- Arvid Heise | Senior Java Developer <https://www.ververica.com/> Follow us @VervericaData -- Join Flink Forward <https://flink-forward.org/> - The Apache Flink Conference Stream Processing | Event Driven | Real Time -- Ververica GmbH | Invalidenstrasse 115, 10115 Berlin, Germany -- Ververica GmbH Registered at Amtsgericht Charlottenburg: HRB 158244 B Managing Directors: Timothy Alexander Steinert, Yip Park Tung Jason, Ji (Toni) Cheng
