Yun Tang created FLINK-19125: -------------------------------- Summary: Avoid memory fragmentation when running flink docker image Key: FLINK-19125 URL: https://issues.apache.org/jira/browse/FLINK-19125 Project: Flink Issue Type: Improvement Components: Deployment / Kubernetes, Runtime / State Backends Affects Versions: 1.11.1 Reporter: Yun Tang
This ticket tracks the problem of memory fragmentation when launching default Flink docker image. In FLINK-18712, user reported if he submits job with rocksDB state backend on a k8s session cluster again and again once it finished, the memory usage of task manager grows continuously until OOM killed. I reproduce this problem with official Flink docker image no matter how we use rocksDB (whether to enable managed memory). I dig into the problem and found this is due to the memory fragmentation caused by {{glibc}}, which would not return memory to kernel gracefully (please refer to [glibc bugzilla|https://sourceware.org/bugzilla/show_bug.cgi?id=15321] and [glibc manual|https://www.gnu.org/software/libc/manual/html_mono/libc.html#Freeing-after-Malloc]) I found if limiting MALLOC_ARENA_MAX to 2 could mitigate this problem (please refer to [choose-for-malloc_arena_max|https://devcenter.heroku.com/articles/tuning-glibc-memory-behavior#what-value-to-choose-for-malloc_arena_max] for more details). And if we choose to use jemalloc to allocate memory via rebuilding another docker image, the problem would be gone. {code:java} apt-get -y install libjemalloc-dev ENV LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so {code} Jemalloc intends to [emphasize fragmentation avoidance|https://github.com/jemalloc/jemalloc /wiki/Background#intended-use] and we might consider to re-factor our Dockerfile to base on jemalloc to avoid memory fragmentation. -- This message was sent by Atlassian Jira (v8.3.4#803005)