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https://issues.apache.org/jira/browse/TEZ-1649?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14166525#comment-14166525
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Bikas Saha commented on TEZ-1649:
---------------------------------
Not quite sure what this if-stmt is doing with maxSrcFraction
{code}+ if ((numSourceTasks > 0 && slowStartMaxSrcCompletionFraction > 0)
&& completedTasks.isEmpty()) {
+ LOG.info("Defer scheduling tasks for vertex:" +
getContext().getVertexName()
+ + " as completed tasks is empty for " + sourceVertex);
+ return false;
+ }
+{code}
This is probably not covering the case where there is another edge of type
custom/broadcast and the slow start calculation ends up starting this vertex
tasks before the custom/broadcast source vertex tasks have started.
Unrelated to the out of order starting case, we should probably track bipartite
edge task completions and output sizes per edge. When enough stats have been
obtained per edge then we can extrapolate the output size per edge and then sum
over all edges to get the total extrapolated output size. This would improve
the accuracy of the extrapolation. Thoughts?
> ShuffleVertexManager auto reduce parallelism can cause jobs to hang
> indefinitely (with ScatterGather edges)
> -----------------------------------------------------------------------------------------------------------
>
> Key: TEZ-1649
> URL: https://issues.apache.org/jira/browse/TEZ-1649
> Project: Apache Tez
> Issue Type: Bug
> Reporter: Rajesh Balamohan
> Assignee: Rajesh Balamohan
> Attachments: TEZ-1649.1.patch, TEZ-1649.2.patch, TEZ-1649.png
>
>
> Consider the following DAG
> M1, M2 --> R1
> M2, M3 --> R2
> R1 --> R2
> All edges are Scatter-Gather.
> 1. Set R1's (1000 parallelism) min/max setting to 0.25 - 0.5f
> 2. Set R2's (21 parallelism) min/max setting to 0.2 and 0.3f
> 3. Let M1 send some data from HDFS (test.txt)
> 4. Let M2 (50 parallelism) generate some data and send it to R2
> 5. Let M3 (500 parallelism) generate some data and send it to R2
> - Since R2's min/max can get satisfied by getting events from M3 itself, R2
> will change its parallelism quickly than R1.
> - In the mean time, R1 changes its parallelism from 1000 to 20. This is not
> propagated to R2 and it would keep waiting.
> Tested this on a small scale (20 node) cluster and it happens consistently.
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