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https://issues.apache.org/jira/browse/FLINK-4391?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15643996#comment-15643996
]
ASF GitHub Bot commented on FLINK-4391:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/2629#discussion_r86762586
--- Diff:
flink-streaming-java/src/main/java/org/apache/flink/streaming/runtime/tasks/StreamTask.java
---
@@ -540,15 +540,12 @@ private boolean performCheckpoint(CheckpointMetaData
checkpointMetaData) throws
synchronized (lock) {
if (isRunning) {
+ checkpointState(checkpointMetaData);
- // Since both state checkpointing and
downstream barrier emission occurs in this
- // lock scope, they are an atomic operation
regardless of the order in which they occur.
- // Given this, we immediately emit the
checkpoint barriers, so the downstream operators
- // can start their checkpoint work as soon as
possible
+ // broadcast barriers after snapshot operators'
states.
operatorChain.broadcastCheckpointBarrier(
-
checkpointMetaData.getCheckpointId(), checkpointMetaData.getTimestamp());
-
- checkpointState(checkpointMetaData);
+
checkpointMetaData.getCheckpointId(), checkpointMetaData.getTimestamp()
+ );
--- End diff --
I think the important performance characteristic is not how the locks
perform when there is only a single thread trying to acquire the lock but if
there is some kind of lock contention by multiple threads. If you change your
benchmark so that you have multiple threads running at the same time, you will
see that the `ReentrantLock` and the `ReentrantReadWriteLock` are much more
expensive than the `Object`.
> Provide support for asynchronous operations over streams
> --------------------------------------------------------
>
> Key: FLINK-4391
> URL: https://issues.apache.org/jira/browse/FLINK-4391
> Project: Flink
> Issue Type: New Feature
> Components: DataStream API
> Reporter: Jamie Grier
> Assignee: david.wang
>
> Many Flink users need to do asynchronous processing driven by data from a
> DataStream. The classic example would be joining against an external
> database in order to enrich a stream with extra information.
> It would be nice to add general support for this type of operation in the
> Flink API. Ideally this could simply take the form of a new operator that
> manages async operations, keeps so many of them in flight, and then emits
> results to downstream operators as the async operations complete.
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