Josh Wills created CRUNCH-133:
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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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