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From "Erik Erlandson (JIRA)" <>
Subject [jira] [Commented] (SPARK-27296) User Defined Aggregating Functions (UDAFs) have a major efficiency problem
Date Wed, 03 Jul 2019 00:05:00 GMT


Erik Erlandson commented on SPARK-27296:

The basic approach as described above appears to be working (see the linked PR). To obtain
the desired behavior I had to create a new API, which is fairly similar to UDAF, but inherits
from TypedImperativeAggregate. This new API supports UDT and Column instantiation, and so
I believe it offers feature parity with the original UDAF, with substantial performance improvements.

> User Defined Aggregating Functions (UDAFs) have a major efficiency problem
> --------------------------------------------------------------------------
>                 Key: SPARK-27296
>                 URL:
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core, SQL, Structured Streaming
>    Affects Versions: 2.3.3, 2.4.0, 3.0.0
>            Reporter: Erik Erlandson
>            Priority: Major
>              Labels: performance, usability
> Spark's UDAFs appear to be serializing and de-serializing to/from the MutableAggregationBuffer
for each row.  This gist shows a small reproducing UDAF and a spark shell session:
> []
> The UDAF and its compantion UDT are designed to count the number of times that ser/de
is invoked for the aggregator.  The spark shell session demonstrates that it is executing
ser/de on every row of the data frame.
> Note, Spark's pre-defined aggregators do not have this problem, as they are based on
an internal aggregating trait that does the correct thing and only calls ser/de at points
such as partition boundaries, presenting final results, etc.
> This is a major problem for UDAFs, as it means that every UDAF is doing a massive amount
of unnecessary work per row, including but not limited to Row object allocations. For a more
realistic UDAF having its own non trivial internal structure it is obviously that much worse.

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