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https://issues.apache.org/jira/browse/FLINK-27504?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17533607#comment-17533607
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Alexis Sarda-Espinosa edited comment on FLINK-27504 at 5/9/22 4:57 AM:
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Hi Yun, thanks for the info, I will try that configuration.
I couldn't find much information in the RocksDB documentation that described
how many L0 files may be expected, I just found a comment stating that the
memtable size is relevant{^}1{^}. Is there a way I can better understand the
level-0 trigger number in the context of Flink?
Speaking of base level target size, I imagine knowing the number of levels is
also important, right? Is that documented by Flink?
I have been collecting the state size metric with a bash script that uses curl
to query Flink's REST API, extracting the value from the response from
/v1/jobs/<id>/checkpoints (the script is also on GitHub).
1 [https://m.facebook.com/groups/rocksdb.dev/permalink/1359028344195676/]
was (Author: asardaes):
Hi Yun, thanks for the info, I will try that configuration.
I couldn't find much information in the RocksDB documentation that described
how many L0 files may be expected, I just found a comment stating that the
memtable size is relevant{^}1{^}. Is there a way I can better understand the
level-0 trigger number in the context of Flink?
Speaking of base level target size, I imagine knowing the number of levels is
also important, right? Is that documented by Flink?
I have been collecting the state size metric with a bash script that uses curl
to query Flink's REST API, extracting the value from the response from
/v1/jobs/<id>/checkpoits (the script is also on GitHub).
1 [https://m.facebook.com/groups/rocksdb.dev/permalink/1359028344195676/]
> State compaction not happening with sliding window and incremental RocksDB
> backend
> ----------------------------------------------------------------------------------
>
> Key: FLINK-27504
> URL: https://issues.apache.org/jira/browse/FLINK-27504
> Project: Flink
> Issue Type: Bug
> Components: Runtime / State Backends
> Affects Versions: 1.14.4
> Environment: Local Flink cluster on Arch Linux.
> Reporter: Alexis Sarda-Espinosa
> Priority: Major
> Attachments: duration_trend_52ca77c.png, duration_trend_67c76bb.png,
> image-2022-05-06-10-34-35-007.png, size_growth_52ca77c.png,
> size_growth_67c76bb.png
>
>
> Hello,
> I'm trying to estimate an upper bound for RocksDB's state size in my
> application. For that purpose, I have created a small job with faster timings
> whose code you can find on GitHub:
> [https://github.com/asardaes/flink-rocksdb-ttl-test]. You can see some of the
> results there, but I summarize here as well:
> * Approximately 20 events per second, 10 unique keys for partitioning are
> pre-specified.
> * Sliding window of 11 seconds with a 1-second slide.
> * Allowed lateness of 11 seconds.
> * State TTL configured to 1 minute and compaction after 1000 entries.
> * Both window-specific and window-global state used.
> * Checkpoints every 2 seconds.
> * Parallelism of 4 in stateful tasks.
> The goal is to let the job run and analyze state compaction behavior with
> RocksDB. I should note that global state is cleaned manually inside the
> functions, TTL for those is in case some keys are no longer seen in the
> actual production environment.
> I have been running the job on a local cluster (outside IDE), the
> configuration YAML is also available in the repository. After running for
> approximately 1.6 days, state size is currently 2.3 GiB (see attachments). I
> understand state can retain expired data for a while, but since TTL is 1
> minute, this seems excessive to me.
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