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From "Paris Carbone (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1284) Uniform random sampling operator over windows
Date Thu, 08 Jan 2015 16:55:35 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1284?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14269546#comment-14269546
] 

Paris Carbone commented on FLINK-1284:
--------------------------------------

Hi Cao. That is right, 'sample' will be a function under WindowedDataStream. The window operator
gets as a parameter a Policy function (there are helpers for the Policies, check the examples)
that defines which tuples are included in each window. This is always followed by an 'every'
operator which again takes a policy as a parameter that defines on which tuples we trigger
the next operations (eg. sample). You can read more at the streaming guide under docs. If
you want to give it a try I can assign it to you.

> Uniform random sampling operator over windows
> ---------------------------------------------
>
>                 Key: FLINK-1284
>                 URL: https://issues.apache.org/jira/browse/FLINK-1284
>             Project: Flink
>          Issue Type: New Feature
>          Components: Streaming
>            Reporter: Paris Carbone
>            Priority: Minor
>
> It would be useful for several use cases to have a built-in uniform random sampling operator
in the streaming API that can operate on windows. This can be used for example for online
machine learning operations, evaluating heuristics or continuous visualisation of representative
values.
> The operator could be given a field and a number of random samples needed, following
a window statement as such:
> mystream.window(..).sample(fieldID,#samples)
> Given that pre-aggregation is enabled, this could perhaps be implemented as a binary
reduce operator or a combinable groupreduce that pre-aggregates the empiricals of that field.



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