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
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