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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-2976) Save and load checkpoints manually
Date Mon, 07 Dec 2015 18:26:11 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2976?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15045410#comment-15045410

ASF GitHub Bot commented on FLINK-2976:

Github user uce commented on a diff in the pull request:

    --- Diff: flink-streaming-java/src/main/java/org/apache/flink/streaming/api/graph/StreamingJobGraphGenerator.java
    @@ -440,4 +478,228 @@ private void configureExecutionRetryDelay() {
     		long executionRetryDelay = streamGraph.getExecutionConfig().getExecutionRetryDelay();
    +	// ------------------------------------------------------------------------
    +	/**
    +	 * Returns a map with a hash for each {@link StreamNode} of the {@link
    +	 * StreamGraph}. The hash is used as the {@link JobVertexID} in order to
    +	 * identify nodes across job submissions if they didn't change.
    +	 *
    +	 * <p>The complete {@link StreamGraph} is traversed. The hash is either
    +	 * computed from the transformation's user-specified id (see
    +	 * {@link StreamTransformation#getUid()}) or generated in a deterministic way.
    +	 *
    +	 * <p>The generated hash is deterministic with respect to:
    +	 * <ul>
    +	 * <li>node-local properties (like parallelism, UDF, node ID),
    +	 * <li>chained output nodes, and
    +	 * <li>input nodes hashes
    +	 * </ul>
    +	 *
    +	 * @return A map from {@link StreamNode#id} to hash as 16-byte array.
    +	 */
    +	private Map<Integer, byte[]> traverseStreamGraphAndGenerateHashes() {
    +		// The hash function used to generate the hash
    +		final HashFunction hashFunction = Hashing.murmur3_128(0);
    +		final Map<Integer, byte[]> hashes = new HashMap<>();
    +		Set<Integer> visited = new HashSet<>();
    +		Queue<StreamNode> remaining = new ArrayDeque<>();
    +		// We need to make the source order deterministic. This depends on the
    +		// ordering of the sources in the Environment, e.g. if a source X is
    +		// added before source Y, X will have a lower ID than Y (assigned by a
    +		// static counter).
    --- End diff --
    I think this is not even necessary if we don't include the StreamNode ID in the hash for
the sources.

> 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: 0.10.0
>            Reporter: Ufuk Celebi
>             Fix For: 1.0.0
> 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 
> and
> 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.

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