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ASF GitHub Bot commented on FLINK-1297: --------------------------------------- Github user tammymendt commented on the pull request: https://github.com/apache/flink/pull/605#issuecomment-138906837 I rebased with master and included the new clone() method. I had to include extra conditionals that check whether count distinct or heavy hitters are being tracked because otherwise the clone method would throw an exception in some cases in which it shouldn't. Also, I added some tests to the OperatorStatsAccumulatorTest class because I realized that I had not written tests for different configurations of the OperatorStatsConfig class. > Add support for tracking statistics of intermediate results > ----------------------------------------------------------- > > Key: FLINK-1297 > URL: https://issues.apache.org/jira/browse/FLINK-1297 > Project: Flink > Issue Type: Improvement > Components: Distributed Runtime > Reporter: Alexander Alexandrov > Assignee: Alexander Alexandrov > Fix For: 0.10 > > Original Estimate: 1,008h > Remaining Estimate: 1,008h > > One of the major problems related to the optimizer at the moment is the lack > of proper statistics. > With the introduction of staged execution, it is possible to instrument the > runtime code with a statistics facility that collects the required > information for optimizing the next execution stage. > I would therefore like to contribute code that can be used to gather basic > statistics for the (intermediate) result of dataflows (e.g. min, max, count, > count distinct) and make them available to the job manager. > Before I start, I would like to hear some feedback form the other users. > In particular, to handle skew (e.g. on grouping) it might be good to have > some sort of detailed sketch about the key distribution of an intermediate > result. I am not sure whether a simple histogram is the most effective way to > go. Maybe somebody would propose another lightweight sketch that provides > better accuracy. -- This message was sent by Atlassian JIRA (v6.3.4#6332)