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https://issues.apache.org/jira/browse/FLINK-19008?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Flink Jira Bot updated FLINK-19008:
-----------------------------------
Labels: auto-deprioritized-major perfomance stale-minor usability (was:
auto-deprioritized-major perfomance usability)
I am the [Flink Jira Bot|https://github.com/apache/flink-jira-bot/] and I help
the community manage its development. I see this issues has been marked as
Minor but is unassigned and neither itself nor its Sub-Tasks have been updated
for 180 days. I have gone ahead and marked it "stale-minor". If this ticket is
still Minor, please either assign yourself or give an update. Afterwards,
please remove the label or in 7 days the issue will be deprioritized.
> Flink Job runs slow after restore + downscale from an incremental checkpoint
> (rocksdb)
> --------------------------------------------------------------------------------------
>
> Key: FLINK-19008
> URL: https://issues.apache.org/jira/browse/FLINK-19008
> Project: Flink
> Issue Type: Improvement
> Components: Runtime / State Backends
> Reporter: Jun Qin
> Priority: Minor
> Labels: auto-deprioritized-major, perfomance, stale-minor,
> usability
>
> A customer runs a Flink job with RocksDB state backend. Checkpoints are
> retained and done incrementally. The state size is several TB. When they
> restore + downscale from a retained checkpoint, although the downloading of
> checkpoint files took ~20min, the job throughput returns to the expected
> level only after 3 hours.
> I do not have RocksDB logs. The suspicion for those 3 hours is due to heavy
> RocksDB compaction and/or flush. As it was observed that checkpoint could not
> finish faster enough due to long {{checkpoint duration (sync)}}. How can we
> make this restoring phase shorter?
> For compaction, I think it is worth to check the improvement of:
> {code:c}
> CompactionPri compaction_pri = kMinOverlappingRatio;{code}
> which has been set to default in RocksDB 6.x:
> {code:c}
> // In Level-based compaction, it Determines which file from a level to be
> // picked to merge to the next level. We suggest people try
> // kMinOverlappingRatio first when you tune your database.
> enum CompactionPri : char {
> // Slightly prioritize larger files by size compensated by #deletes
> kByCompensatedSize = 0x0,
> // First compact files whose data's latest update time is oldest.
> // Try this if you only update some hot keys in small ranges.
> kOldestLargestSeqFirst = 0x1,
> // First compact files whose range hasn't been compacted to the next level
> // for the longest. If your updates are random across the key space,
> // write amplification is slightly better with this option.
> kOldestSmallestSeqFirst = 0x2,
> // First compact files whose ratio between overlapping size in next level
> // and its size is the smallest. It in many cases can optimize write
> // amplification.
> kMinOverlappingRatio = 0x3,
> };
> ...
> // Default: kMinOverlappingRatio
> CompactionPri compaction_pri = kMinOverlappingRatio;{code}
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