Xiangrui Meng created SPARK-10384:
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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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