Github user bowenli86 commented on a diff in the pull request:

    https://github.com/apache/flink/pull/5239#discussion_r167356422
  
    --- 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
    +only write task states to the distributed storage, but also keep *a 
secondary copy of the state snapshot in a storage that is local to the task*
    +(e.g. on local disk or in memory). Notice that the primary store for 
snapshots must still be the distributed store, because local storage does not
    +ensure durability under node failures abd also does not provide access for 
other nodes to redistribute state, this functionality still requires the
    +primary copy.
    +
    +However, for each task that can be rescheduled to the previous location 
for recovery, we can restore state from the secondary, local
    +copy and avoid the costs of reading the state remotely. Given that *many 
failures are not node failures and node failures typically only affect one
    +or very few nodes at a time*, it is very likely that in a recovery most 
tasks can return to their previous location and find their local state intact.
    +This is what makes local recovery effective in reducing recovery time.
    +
    +Please note that this can come at some additional costs per checkpoint for 
creating and storing the secondary local state copy, depending on the
    +chosen state backend and checkpointing strategy. For example, in most 
cases the implementation will simply duplicate the writes to the distributed
    +store to a local file.
    +
    +<img src="../../fig/local_recovery.png" class="center" width="80%" 
alt="Illustration of checkpointing with task-local recovery."/>
    +
    +### Relationship of primary (distributed store) and secondary (task-local) 
state snapshots
    +
    +Task-local state is always considered a secondary copy, the ground truth 
of the checkpoint state is the primary copy in the distributed store. This
    +has implications for problems with local state during checkpointing and 
recovery:
    +
    +- For checkpointing, the *primary copy must be successful* and a failure 
to produce the *secondary, local copy will not fail* the checkpoint. A 
checkpoint
    +will fail if the primary copy could not be created, even if the secondary 
copy was successfully created.
    +
    +- Only the primary copy is acknowledged and managed by the job manager, 
secondary copies are owned by task managers and their life cycle can be
    +independent from their primary copy. For example, it is possible to retain 
a history of the 3 latest checkpoints as primary copies and only keep
    +the task-local state of the latest checkpoint.
    +
    +- For recovery, Flink will always *attempt to restore from task-local 
state first*, if a matching secondary copy is available. If any problem occurs 
during
    +the recovery from the secondary copy, Flink will *transparently retry to 
recovery the task from the primary copy*. Recovery only fails, if primary
    --- End diff --
    
    to recover~y~


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