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From Deenar Toraskar <deenar.toras...@db.com>
Subject Efficient hierarchical aggregation in Spark
Date Tue, 24 Dec 2013 08:32:30 GMT
Classification: Public
Hi

I have a requirement to aggregate a large data set in Spark across a multi level (25 levels)
hierarchy. The data model (simplified) is as follows

Measures
leafNode           Long
measureType   String
measureValue Array[Float]

Hierarchy (expanded) - a typical organisation hierarchy.
leafNode             Long /* Account also called level0Node */
level1Node        Long /* Portfolio */
level2Node        Long /* Sub Fund */
level3Node        Long /* Fund */
level4Node        Long
                ...
                ...
level25Node      Long /* organisation*/

alternative representation
node
parentNode
hierarchylevel

Output Format
Level                     Int                   /* 0-25*/
Node                     Long
measureType     String
measureValue    Array[Float]

I can do the aggregation by joining both the RDDs together and aggregating each level one
at a time. I was wondering if there was a more efficient way of doing this in spark? Maybe
a recursive algorithm that traverses the tree?
Currently the measures data set is loaded in a batch fashion, but I am working on getting
incremental feeds of measures using Spark streaming.

Deenar

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