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From "Josh Wills (JIRA)" <j...@apache.org>
Subject [jira] [Created] (CRUNCH-133) Add Aggregator support for combineValues ops on secondary keys via maps and collections
Date Thu, 13 Dec 2012 21:34:12 GMT
Josh Wills created CRUNCH-133:
---------------------------------

             Summary: 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


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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