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https://issues.apache.org/jira/browse/FLINK-8360?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16359260#comment-16359260
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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_r167389639
  
    --- 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
    +and the (optional) secondary copy failed. In this case, depending on the 
configuration Flink could still fall back to an older checkpoint.
    +
    +- It is possible that the task-local copy contains only parts of the full 
task state (e.g. exception while writing one local file). In this case,
    +Flink will first try to recover local parts locally, non-local state is 
restored from the primary copy. Primary state must always be complete and is
    +a *superset of the task-local state*.
    +
    +- Task-local state can have a different format than the primary state, 
they are not required to be byte identical. For example, it could even possible 
that
    +the task-local state is an in-memory consisting of heap objects, and not 
stored in any files.
    +
    +- If a task manager is lost, the local state from all its task is lost.
    +
    +### Configuring task-local recovery
    +
    +Task-local recovery is *deactivated by default* and can be activated 
through Flink's configuration with the key `state.backend.local-recovery` as 
specified
    +in `CheckpointingOptions.LOCAL_RECOVERY`. Users have currently two choices:
    +
    +- `DISABLED`: Local recovery is disabled (default).
    +- `ENABLE_FILE_BASED`: Local recovery is activated, based on writing a 
secondary copy of the task state on local disk.
    +
    +### Details on task-local recovery for different state backends
    +
    +***Limitation**: Currently, task-local recovery only covers keyed state 
backends. Keyed state is typically by far the largest part of the state. In the 
near future, we will
    +also cover operator state and timers.*
    +
    +The following state backends can support task-local recovery.
    +
    +- FsStateBackend: task-local recovery is supported for keyed state. The 
implementation will duplicate the state to a local file. This can introduce 
additional write costs
    +and occupies local disk space. In the future, we might also offer an 
implementation that keeps task-local state in memory.
    +
    +- RocksDBStateBackend: task-local recovery is supported for keyed state. 
For *full checkpoints*, state is duplicated to a local file. This can introduce 
additional write costs
    +and occupies local disk space. For *incremental snapshots*, the local 
state is based on RocksDB's native checkpointing mechanism. This mechanism is 
also used as the first step
    +to create the primary copy, which means that in this case no additional 
cost is introduced for create the secondary copy. We simply keep the native 
checkpoint directory around
    +instead of deleting it after uploading to the distributed store. This 
local copy can share active files with the working directory of RocksDB (via 
hard links), so for active
    +files also no additional disk space is consumed for task-local recovery 
with incremental snapshots.
    +
    +### Allocation-preserving scheduling
    +
    +Task-local recovery assumes allocation-preserving task scheduling under 
failures, which was introduced as part of FLIP-6 and works as follows. Each 
task remembers its previous
    +allocation and *requests the exact same slot* to restart in recovery. If 
this slot is not available, the task will request a *new, fresh slot* from the 
resource manager. This way,
    +if a task manager is no longer available, a task that cannot return to its 
previous location *will not drive other recovering tasks out of their previous 
slots*. Our reasoning is
    +that the previous slot can only disappear when a task manager is no longer 
available, and in this case *some* task has to request a new slot anyways. With 
our scheduling strategy
    --- End diff --
    
    some task**s** **have** to 


> 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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