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https://issues.apache.org/jira/browse/FLINK-1297?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14499816#comment-14499816
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ASF GitHub Bot commented on FLINK-1297:
---------------------------------------
Github user tammymendt commented on a diff in the pull request:
https://github.com/apache/flink/pull/605#discussion_r28593077
--- Diff: flink-core/pom.xml ---
@@ -63,6 +63,19 @@ under the License.
<artifactId>guava</artifactId>
<version>${guava.version}</version>
</dependency>
+
+ <dependency>
+ <groupId>com.clearspring.analytics</groupId>
+ <artifactId>stream</artifactId>
+ <version>2.7.0</version>
+ <exclusions>
+ <exclusion>
+ <groupId>it.unimi.dsi</groupId>
+ <artifactId>fastutil</artifactId>
--- End diff --
The library we are using from clearspring implements a series of streaming
algorithms. We are only using HyperLogLog and CountMinSketch to estimate count
distinct and heavy hitters respectively. As far as I could see fastutil is used
only in the implementation of a separate streaming algorithm to track
histograms (QDigest). Since we are not using this particular algorithm, it
should be ok to exclude fastutil from the dependency.
> 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: 0.9
>
> 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.
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