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ASF GitHub Bot commented on FLINK-8360: --------------------------------------- Github user StefanRRichter commented on a diff in the pull request: https://github.com/apache/flink/pull/5239#discussion_r168752445 --- 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 --- End diff -- 👍 > 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. -- This message was sent by Atlassian JIRA (v7.6.3#76005)