Checkpoints #

Overview #

Checkpoints make state in Flink fault tolerant by allowing state and the corresponding stream positions to be recovered, thereby giving the application the same semantics as a failure-free execution.

See Checkpointing for how to enable and configure checkpoints for your program.

Checkpoint Storage #

When checkpointing is enabled, managed state is persisted to ensure consistent recovery in case of failures. Where the state is persisted during checkpointing depends on the chosen Checkpoint Storage.

Available Checkpoint Storage Options #

Out of the box, Flink bundles these checkpoint storage types:

  • JobManagerCheckpointStorage
  • FileSystemCheckpointStorage
If a checkpoint directory is configured FileSystemCheckpointStorage will be used, otherwise the system will use the JobManagerCheckpointStorage.

The JobManagerCheckpointStorage #

The JobManagerCheckpointStorage stores checkpoint snapshots in the JobManager’s heap.

It can be configured to fail the checkpoint if it goes over a certain size to avoid OutOfMemoryError’s on the JobManager. To set this feature, users can instantiate a JobManagerCheckpointStorage with the corresponding max size:

new JobManagerCheckpointStorage(MAX_MEM_STATE_SIZE);

Limitations of the JobManagerCheckpointStorage:

  • The size of each individual state is by default limited to 5 MB. This value can be increased in the constructor of the JobManagerCheckpointStorage.
  • Irrespective of the configured maximal state size, the state cannot be larger than the Akka frame size (see Configuration).
  • The aggregate state must fit into the JobManager memory.

The JobManagerCheckpointStorage is encouraged for:

  • Local development and debugging
  • Jobs that use very little state, such as jobs that consist only of record-at-a-time functions (Map, FlatMap, Filter, …). The Kafka Consumer requires very little state.

The FileSystemCheckpointStorage #

The FileSystemCheckpointStorage is configured with a file system URL (type, address, path), such as “hdfs://namenode:40010/flink/checkpoints” or “file:///data/flink/checkpoints”.

Upon checkpointing, it writes state snapshots into files in the configured file system and directory. Minimal metadata is stored in the JobManager’s memory (or, in high-availability mode, in the metadata checkpoint).

If a checkpoint directory is specified, FileSystemCheckpointStorage will be used to persist checkpoint snapshots.

The FileSystemCheckpointStorage is encouraged for:

  • All high-availability setups.

It is also recommended to set managed memory to zero. This will ensure that the maximum amount of memory is allocated for user code on the JVM.

Retained Checkpoints #

Checkpoints are by default not retained and are only used to resume a job from failures. They are deleted when a program is cancelled. You can, however, configure periodic checkpoints to be retained. Depending on the configuration these retained checkpoints are not automatically cleaned up when the job fails or is canceled. This way, you will have a checkpoint around to resume from if your job fails.

CheckpointConfig config = env.getCheckpointConfig();

The ExternalizedCheckpointCleanup mode configures what happens with checkpoints when you cancel the job:

  • ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION: Retain the checkpoint when the job is cancelled. Note that you have to manually clean up the checkpoint state after cancellation in this case.

  • ExternalizedCheckpointCleanup.DELETE_ON_CANCELLATION: Delete the checkpoint when the job is cancelled. The checkpoint state will only be available if the job fails.

Directory Structure #

Similarly to savepoints, a checkpoint consists of a meta data file and, depending on the state backend, some additional data files. The meta data file and data files are stored in the directory that is configured via state.checkpoints.dir in the configuration files, and also can be specified for per job in the code.

The current checkpoint directory layout (introduced by FLINK-8531) is as follows:

        + --shared/
        + --taskowned/
        + --chk-1/
        + --chk-2/
        + --chk-3/

The SHARED directory is for state that is possibly part of multiple checkpoints, TASKOWNED is for state that must never be dropped by the JobManager, and EXCLUSIVE is for state that belongs to one checkpoint only.

The checkpoint directory is not part of a public API and can be changed in the future release.

Configure globally via configuration files #

state.checkpoints.dir: hdfs:///checkpoints/

Configure for per job on the checkpoint configuration #


Configure with checkpoint storage instance #

Alternatively, checkpoint storage can be set by specifying the desired checkpoint storage instance which allows for setting low level configurations such as write buffer sizes.

  new FileSystemCheckpointStorage("hdfs:///checkpoints-data/", FILE_SIZE_THESHOLD));

Difference to Savepoints #

Checkpoints have a few differences from savepoints. They

  • use a state backend specific (low-level) data format, may be incremental.
  • do not support Flink specific features like rescaling.

Resuming from a retained checkpoint #

A job may be resumed from a checkpoint just as from a savepoint by using the checkpoint’s meta data file instead (see the savepoint restore guide). Note that if the meta data file is not self-contained, the jobmanager needs to have access to the data files it refers to (see Directory Structure above).

$ bin/flink run -s :checkpointMetaDataPath [:runArgs]

Unaligned checkpoints #

Starting with Flink 1.11, checkpoints can be unaligned. Unaligned checkpoints contain in-flight data (i.e., data stored in buffers) as part of the checkpoint state, which allows checkpoint barriers to overtake these buffers. Thus, the checkpoint duration becomes independent of the current throughput as checkpoint barriers are effectively not embedded into the stream of data anymore.

You should use unaligned checkpoints if your checkpointing durations are very high due to backpressure. Then, checkpointing time becomes mostly independent of the end-to-end latency. Be aware unaligned checkpointing adds to I/O to the state backends, so you shouldn’t use it when the I/O to the state backend is actually the bottleneck during checkpointing.

Note that unaligned checkpointing is a new feature that currently has the following limitations:

  • Flink currently does not support concurrent unaligned checkpoints. However, due to the more predictable and shorter checkpointing times, concurrent checkpoints might not be needed at all. However, savepoints can also not happen concurrently to unaligned checkpoints, so they will take slightly longer.
  • Unaligned checkpoints break with an implicit guarantee in respect to watermarks during recovery:

Currently, Flink generates the watermark as a first step of recovery instead of storing the latest watermark in the operators to ease rescaling. In unaligned checkpoints, that means on recovery, Flink generates watermarks after it restores in-flight data. If your pipeline uses an operator that applies the latest watermark on each record, it will produce different results than for aligned checkpoints. If your operator depends on the latest watermark being always available, then the workaround is to store the watermark in the operator state. To support rescaling, watermarks should be stored per key-group in a union-state. We most likely will implement this approach as a general solution (didn’t make it into Flink 1.11.0).

After enabling unaligned checkpoints, you can also specify the alignment timeout via CheckpointConfig.setAlignmentTimeout(Duration) or execution.checkpointing.alignment-timeout in the configuration file. When activated, each checkpoint will still begin as an aligned checkpoint, but if the alignment time for some subtask exceeds this timeout, then the checkpoint will proceed as an unaligned checkpoint.

Back to top