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From "OlgaK (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (DATAFU-63) SimpleRandomSample by a fixed number
Date Tue, 21 Nov 2017 01:30:00 GMT

    [ https://issues.apache.org/jira/browse/DATAFU-63?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16260133#comment-16260133
] 

OlgaK commented on DATAFU-63:
-----------------------------

some mems to keep track (eventually can be added to the docs):
to build on <home> gradle version:
1.  export GRADLE_USER_HOME=/where/gradle/installed/
2.  edit gradle/wrapper/gradle-wrapper.properties adjust last lane to point out the installed
gradle version

Java 8 build fails, the code requires Java 7 
3. unset  JAVA_HOME pointing to Java 8 (my ancient system still has Java 7 as a default, while
it's already Java 9 time; has been surprised, my ancient system isn't  ancient enough for
full compatibility with this code )
4. now build as pointed in the docs: .`/gradlew clean assemble`

Appeared, DataBag has no remove or alike method: https://pig.apache.org/docs/r0.8.1/api/org/apache/pig/data/DataBag.html
Am I right? 

I can build the code with my added module, just need to figure out what to do in case one
can't remove elements, while sum of ( `ceil` or `(int) k / num_of_partitions` ) returns some
excess      



> SimpleRandomSample by a fixed number
> ------------------------------------
>
>                 Key: DATAFU-63
>                 URL: https://issues.apache.org/jira/browse/DATAFU-63
>             Project: DataFu
>          Issue Type: New Feature
>            Reporter: jian wang
>            Assignee: jian wang
>
> SimpleRandomSample currently supports random sampling by probability, it does not support
random sample a fixed number of items. ReserviorSample may do the work but since it relies
on an in-memory priority queue, memory issue may happen if we are going to sample a huge number
of items, eg: sample 100M from 100G data. 
> Suggested approach is to create a new class "SimpleRandomSampleByCount" that uses Manuver's
rejection threshold to reject items whose weight exceeds the threshold as we go from mapper
to combiner to reducer. The majority part of the algorithm will be very similar to SimpleRandomSample,
except that we do not use Berstein's theory to accept items and replace probability p = k
/ n,  k is the number of items to sample, n is the total number of items local in mapper,
combiner and reducer.
> Quote this requirement from others:
> "Hi folks,
> Question: does anybody know if there is a quicker way to randomly sample a specified
number of rows from grouped data? I’m currently doing this, since it appears that the SAMPLE
operator doesn’t work inside FOREACH statements:
> photosGrouped = GROUP photos BY farm;
> agg = FOREACH photosGrouped {
>   rnds = FOREACH photos GENERATE *, RANDOM() as rnd;
>   ordered_rnds = ORDER rnds BY rnd;
>   limitSet = LIMIT ordered_rnds 5000;
>   GENERATE group AS farm,
>            FLATTEN(limitSet.(photo_id, server, secret)) AS (photo_id, server, secret);
> };
> This approach seems clumsy, and appears to run quite slowly (I’m assuming the ORDER/LIMIT
isn’t great for performance). Is there a less awkward way to do this?
> Thanks,
> "



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