flink-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Ufuk Celebi (JIRA)" <j...@apache.org>
Subject [jira] [Created] (FLINK-2976) Save and load checkpoints manually
Date Thu, 05 Nov 2015 11:25:27 GMT
Ufuk Celebi created FLINK-2976:

             Summary: Save and load checkpoints manually
                 Key: FLINK-2976
                 URL: https://issues.apache.org/jira/browse/FLINK-2976
             Project: Flink
          Issue Type: Improvement
          Components: Distributed Runtime
    Affects Versions: 0.10
            Reporter: Ufuk Celebi
             Fix For: 1.0

Currently, all checkpointed state is bound to a job. After the job finishes all state is lost.
In case of an HA cluster, jobs can live longer than the cluster, but they still suffer from
the same issue when they finish.

Multiple users have requested the feature to manually save a checkpoint in order to resume
from it at a later point. This is especially important for production environments. As an
example, consider upgrading your existing production Flink program. Currently, you loose all
the state of your program. With the proposed mechanism, it will be possible to save a checkpoint,
stop and update your program, and then continue your program with the  checkpoint.

The required operations can be simple:

saveCheckpoint(JobID) => checkpointID: long

loadCheckpoint(JobID, long) => void

For the initial version, I would apply the following restriction:
- The topology needs to stay the same (JobGraph parallelism, etc.)

A user can configure this behaviour via the environment like the checkpointing interval. Furthermore,
the user can trigger the save operation via the command line at arbitrary times and load a
checkpoint when submitting a job, e.g.

bin/flink checkpoint <JobID> => checkpointID: long 


bin/flink run --loadCheckpoint JobID [latest saved checkpoint]
bin/flink run --loadCheckpoint (JobID,long) [specific saved checkpoint]

As far as I can tell, the required mechanisms are similar to the ones implemented for JobManager
high availability. We need to make sure to persist the CompletedCheckpoint instances as a
pointer to the checkpoint state and to *not* remove saved checkpoint state.

On the client side, we need to give the job and its vertices the same IDs to allow mapping
the checkpoint state.

This message was sent by Atlassian JIRA

View raw message