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]({{ site.baseurl }}/dev/stream/state/checkpointing.html) 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.
{% highlight java %} CheckpointConfig config = env.getCheckpointConfig(); config.enableExternalizedCheckpoints(ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION); {% endhighlight %}
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.
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.
{% highlight yaml %} state.checkpoints.dir: hdfs:///checkpoints/ {% endhighlight %}
{% highlight java %} env.setStateBackend(new RocksDBStateBackend(“hdfs:///checkpoints-data/”); {% endhighlight %}
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).
{% highlight shell %} $ bin/flink run -s :checkpointMetaDataPath [:runArgs] {% endhighlight %}
Previously, if we want to resume from retained checkpoint, we must provide the specific directory, which contains the checkpoint meta file such above., to resume checkpoint:
{% highlight shell %} $ bin/flink run -s {user-defined-checkpoint-dir}/{job-id}/chk-x {% endhighlight %}
However, if we just want to resume from the last latest checkpoint, this usage is not so friendly for two reasons below:
chk-x folder must contain the valid checkpoint meta file, otherwise the Flink job would not submit successfully.job-id, so that we could resume from the last latest checkpoint. However, job-id is not so readable and we need other solution to judge which is the last running job.We introduce a more friendly way to resume job from checkpoint automatically. First of all, we add new command to resume from latest checkpoint of given job with specific job-id directory: {% highlight shell %} $ bin/flink run -r {user-defined-checkpoint-dir}/{job-id} {% endhighlight %} Please pay attention that, if no checkpoint could be found in the given directory, the newly submitted job would run from scratch.
We also introduce a new configuraiton named state.checkpoints.create-subdirs in flink-conf.yaml, which has default value as true. If no multi jobs with the same checkpoint path would run simultaneously, which could be ensured through external job-manage system. By means of setting this configuraiton as false, the sub-directory of job-id would not be created, which would be more friendly for usage: {% highlight shell %} bin/flink run -r {user-defined-checkpoint-dir} {% endhighlight %}
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