zhuzhurk commented on a change in pull request #7255: [FLINK-10945] Use
InputDependencyConstraint to avoid resource dead…
URL: https://github.com/apache/flink/pull/7255#discussion_r246435193
##########
File path:
flink-runtime/src/main/java/org/apache/flink/runtime/executiongraph/ExecutionVertex.java
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@@ -726,6 +730,41 @@ void sendPartitionInfos() {
}
}
+ /**
+ * Check whether the InputDependencyConstraint is satisfied for this
vertex.
+ *
+ * @return whether the input constraint is satisfied
+ */
+ public boolean checkInputDependencyConstraints() {
+ if (getExecutionGraph().getInputDependencyConstraint() ==
InputDependencyConstraint.ANY) {
Review comment:
I've move `InputDependencyConstraint ` to JobVertex. And the job wide
default value can be configured in `ExecutionConfig`. But I haven't make it
configurable through DataSet/DataStream API yet.
I agree we should support the constraint configurable for each operator. But
I'm not quite sure whether we should support it with DataSet API or later for
the stream/batch unified StreamGraph/Transformation API? Could you share your
suggestion?
In our production experience, a job-wide configured input constraint
satisfies most users, together with `BATCH_FORCED` execution mode, to ensure a
batch job can finish with limited resources.
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