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.
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.
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.
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
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:
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.
Checkpoints have a few differences from savepoints. They
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).
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 checkpoints is a brand-new feature that currently has the following limitations:
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).
In the upcoming release(s), Flink will address these limitations and will provide a fine-grained way to trigger unaligned checkpoints only for the in-flight data that moves slowly with timeout mechanism. These options will decrease the pressure on I/O in the state backends and eventually allow unaligned checkpoints to become the default checkpointing.