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https://issues.apache.org/jira/browse/FLINK-8360?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16359252#comment-16359252
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ASF GitHub Bot commented on FLINK-8360:
---------------------------------------
Github user bowenli86 commented on a diff in the pull request:
https://github.com/apache/flink/pull/5239#discussion_r167355160
--- Diff: docs/ops/state/large_state_tuning.md ---
@@ -234,4 +234,97 @@ Compression can be activated through the
`ExecutionConfig`:
**Notice:** The compression option has no impact on incremental snapshots,
because they are using RocksDB's internal
format which is always using snappy compression out of the box.
+## Task-Local Recovery
+
+### Motivation
+
+In Flink's checkpointing, each task produces a snapshot of its state that
is then written to a distributed store. Each task acknowledges
+a successful write of the state to the job manager by sending a handle
that describes the location of the state in the distributed store.
+The job manager, in turn, collects the handles from all tasks and bundles
them into a checkpoint object.
+
+In case of recovery, the job manager opens the latest checkpoint object
and sends the handles back to the corresponding tasks, which can
+then restore their state from the distributed storage. Using a distributed
storage to store state has two important advantages. First, the storage
+is fault tolerant and second, all state in the distributed store is
accessible to all nodes and can be easily redistributed (e.g. for rescaling).
+
+However, using a remote distributed store has also one big disadvantage:
all tasks must read their state from a remote location, over the network.
+In many scenarios, recovery could reschedule failed tasks to the same task
manager as in the previous run (of course there are exceptions like machine
+failures), but we still have to read remote state. This can result in
*long recovery times for large states*, even if there was only a small failure
on
+a single machine.
+
+### Approach
+
+Task-local state recovery targets exactly this problem of long recovery
times and the main idea is the following: for every checkpoint, we do not
--- End diff --
'we' refers to 'each task'? Better to be explicit about it
> Implement task-local state recovery
> -----------------------------------
>
> Key: FLINK-8360
> URL: https://issues.apache.org/jira/browse/FLINK-8360
> Project: Flink
> Issue Type: New Feature
> Components: State Backends, Checkpointing
> Reporter: Stefan Richter
> Assignee: Stefan Richter
> Priority: Major
> Fix For: 1.5.0
>
>
> This issue tracks the development of recovery from task-local state. The main
> idea is to have a secondary, local copy of the checkpointed state, while
> there is still a primary copy in DFS that we report to the checkpoint
> coordinator.
> Recovery can attempt to restore from the secondary local copy, if available,
> to save network bandwidth. This requires that the assignment from tasks to
> slots is as sticky is possible.
> For starters, we will implement this feature for all managed keyed states and
> can easily enhance it to all other state types (e.g. operator state) later.
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