curcur commented on a change in pull request #18431:
URL: https://github.com/apache/flink/pull/18431#discussion_r791314908
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File path: docs/content/docs/ops/state/state_backends.md
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@@ -325,6 +325,126 @@ public class MyOptionsFactory implements
ConfigurableRocksDBOptionsFactory {
{{< top >}}
+## Enabling Changelog
+
+// todo: Chinese version of all changed docs
+
+// todo: mention in [large state tuning]({{< ref
"docs/ops/state/large_state_tuning" >}})? or 1.16?
+
+{{< hint warning >}} The feature is in experimental status. {{< /hint >}}
+
+{{< hint warning >}} Enabling Changelog may have a negative performance impact
on your application (see below). {{< /hint >}}
+
+### Introduction
+
+Changelog is a feature that aims to decrease checkpointing time, and therefore
end-to-end latency in exactly-once mode.
+
+Most commonly, checkpoint duration is affected by:
+
+1. Barrier travel time and alignment, addressed by
+ [Unaligned checkpoints]({{< ref
"docs/ops/state/checkpointing_under_backpressure#unaligned-checkpoints" >}})
+ and [Buffer debloating]({{< ref
"docs/ops/state/checkpointing_under_backpressure#buffer-debloating" >}})
+2. Snapshot creation time (so-called synchronous phase), addressed by
Asynchronous snapshots
+3. Snapshot upload time (asynchronous phase)
+
+The latter (upload time) can be decreased by [Incremental checkpoints]({{< ref
"#incremental-checkpoints" >}}). However,
+even with Incremental checkpoints, large deployments tend to have at least one
task in every checkpoint that uploads a
+lot of data (e.g. after compaction).
+
+With Changelog enabled, Flink uploads state changes continuously, forming a
changelog. On checkpoint, only the relevant
+part of this changelog needs to be uploaded. Independently, configured state
backend is checkpointed in the
+background periodically. Upon successful upload, changelog is truncated.
+
+As a result, asynchronous phase is reduced, as well as synchronous phase (in
particular, long-tail).
+
+On the flip side, resource usage is higher:
+
+- more files are created on DFS
+- more IO bandwidth is used to upload
+- more CPU used to serialize state changes
+- more memory used by Task Managers to buffer state changes
+- todo: more details after testing, maybe link to blogpost
+
+Recovery time is another thing to consider. Depending on the
`state.backend.changelog.periodic-materialize.interval`,
+changelog can become lengthy and replaying it may take more time. However,
recovery time combined with checkpoint
+duration will likely be still lower than in non-changelog setup, providing
lower end-to-end latency even in failover
+case.
Review comment:
Recovery time really depends on how much data to restore.
I also saw a lot of cases that recovery time increased by tens of seconds
but checkpoint duration does not decrease that much.
What we showed in the graph is a job instance with 196GB state size, and
there are a lot of cases that the above statement does not hold.
I do not think we should mix recovery time with checkpoint duration here.
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