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From bowenli86 <...@git.apache.org>
Subject [GitHub] flink pull request #5239: [FLINK-8360] Implement task-local state recovery
Date Sat, 10 Feb 2018 05:05:26 GMT
Github user bowenli86 commented on a diff in the pull request:

    --- 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
    +- 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
    +- 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
    +### 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 


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