[jira] [Updated] (CRUNCH-133) Add Aggregator support for combineValues ops on secondary keys via maps and collections

["Josh Wills (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/CRUNCH-133?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Josh Wills updated CRUNCH-133:
------------------------------

    Attachment: CRUNCH-133.patch

First pass at this, will send it out on ReviewBoard as well.
                
> Add Aggregator support for combineValues ops on secondary keys via maps and collections
> ---------------------------------------------------------------------------------------
>
>                 Key: CRUNCH-133
>                 URL: https://issues.apache.org/jira/browse/CRUNCH-133
>             Project: Crunch
>          Issue Type: Bug
>            Reporter: Josh Wills
>         Attachments: CRUNCH-133.patch
>
>
> Sawzall has a neat trick where you can do aggregations on secondary keys via maps, which is useful in cases where you might want to aggregate some data at (for example) both a country and at a city level within a single MapReduce job. We had a thread on crunch-user about this pattern:
> http://mail-archives.apache.org/mod_mbox/incubator-crunch-user/201212.mbox/%3CCAH29n6O-aHXTPHCRpSuAkAGUjvDR%3D56%3D-OLq9K9mZje%2BwVB4-Q%40mail.gmail.com%3E
> The pattern ends up looking something like this:
> // Define a table that has long values at both the K and the <K, String> levels.
> PTable<K, Pair<Long, Map<String, Long>>> in = ...;
> // Define and apply an Aggregator that can handle sums at both levels within a single MR job.
> Aggregator<Pair<Long, Map<String, Long>>> a = pairAggregator(SUM_LONGS(), map(Aggregators.SUM_LONGS()));
> PTable<K, Pair<Long, Map<String, Long>>> out = in.groupByKey().combineValues(a);
> ...which would run substantially faster than executing two dependent MR jobs, one that did the city aggregation and then a second follow-up job that did the country aggregation.

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