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From "Xiangrui Meng (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-10384) Univariate statistics as UDAFs
Date Tue, 01 Sep 2015 06:45:46 GMT
Xiangrui Meng created SPARK-10384:
-------------------------------------

             Summary: Univariate statistics as UDAFs
                 Key: SPARK-10384
                 URL: https://issues.apache.org/jira/browse/SPARK-10384
             Project: Spark
          Issue Type: Umbrella
          Components: ML, SQL
            Reporter: Xiangrui Meng
            Assignee: Burak Yavuz


It would be nice to define univariate statistics as UDAFs. This JIRA discusses general implementation
and tracks the process of subtasks. Univariate statistics include:

continuous: min, max, range, variance, stddev, median, quantiles, skewness, and kurtosis
categorical: number of categories, mode

If we define them as UDAFs, it would be quite flexible to use them with DataFrames, e.g.,

{code}
df.groupBy("key").agg(min("x"), min("y"), variance("x"), skewness("x"))
{code}

Note that some univariate statistics depend on others, e.g., variance might depend on mean
and count. It would be nice if SQL can optimize the sequence to avoid duplicate computation.



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