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Posted to commits@flink.apache.org by ch...@apache.org on 2018/04/25 21:52:37 UTC
flink git commit: [FLINK-9243][tests] fix flaky
SuccessAfterNetworkBuffersFailureITCase
Repository: flink
Updated Branches:
refs/heads/master fdfce98ab -> 0113ee2b3
[FLINK-9243][tests] fix flaky SuccessAfterNetworkBuffersFailureITCase
This closes #5915.
Project: http://git-wip-us.apache.org/repos/asf/flink/repo
Commit: http://git-wip-us.apache.org/repos/asf/flink/commit/0113ee2b
Tree: http://git-wip-us.apache.org/repos/asf/flink/tree/0113ee2b
Diff: http://git-wip-us.apache.org/repos/asf/flink/diff/0113ee2b
Branch: refs/heads/master
Commit: 0113ee2b33e3f86bb6a3de2ecf60d8e7bf554be2
Parents: fdfce98
Author: Nico Kruber <ni...@data-artisans.com>
Authored: Wed Apr 25 20:27:43 2018 +0200
Committer: zentol <ch...@apache.org>
Committed: Wed Apr 25 23:52:24 2018 +0200
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.../test/misc/SuccessAfterNetworkBuffersFailureITCase.java | 8 ++++++--
1 file changed, 6 insertions(+), 2 deletions(-)
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http://git-wip-us.apache.org/repos/asf/flink/blob/0113ee2b/flink-tests/src/test/java/org/apache/flink/test/misc/SuccessAfterNetworkBuffersFailureITCase.java
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diff --git a/flink-tests/src/test/java/org/apache/flink/test/misc/SuccessAfterNetworkBuffersFailureITCase.java b/flink-tests/src/test/java/org/apache/flink/test/misc/SuccessAfterNetworkBuffersFailureITCase.java
index 16159c1..c5c1882 100644
--- a/flink-tests/src/test/java/org/apache/flink/test/misc/SuccessAfterNetworkBuffersFailureITCase.java
+++ b/flink-tests/src/test/java/org/apache/flink/test/misc/SuccessAfterNetworkBuffersFailureITCase.java
@@ -134,13 +134,17 @@ public class SuccessAfterNetworkBuffersFailureITCase extends TestLogger {
// set number of bulk iterations for KMeans algorithm
IterativeDataSet<KMeans.Centroid> loop = centroids.iterate(20);
+ // add some re-partitions to increase network buffer use
DataSet<KMeans.Centroid> newCentroids = points
+ .rebalance()
// compute closest centroid for each point
.map(new KMeans.SelectNearestCenter()).withBroadcastSet(loop, "centroids")
- // count and sum point coordinates for each centroid
+ .rebalance()
+ // count and sum point coordinates for each centroid
.map(new KMeans.CountAppender())
.groupBy(0).reduce(new KMeans.CentroidAccumulator())
- // compute new centroids from point counts and coordinate sums
+ // compute new centroids from point counts and coordinate sums
+ .rebalance()
.map(new KMeans.CentroidAverager());
// feed new centroids back into next iteration