Erik Erlandson created SPARK-27296:
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Summary: User Defined Aggregating Functions (UDAFs) have a major
efficiency problem
Key: SPARK-27296
URL: https://issues.apache.org/jira/browse/SPARK-27296
Project: Spark
Issue Type: Bug
Components: Spark Core, SQL, Structured Streaming
Affects Versions: 2.4.0, 2.3.3, 3.0.0
Reporter: Erik Erlandson
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:
[https://gist.github.com/erikerlandson/3c4d8c6345d1521d89e0d894a423046f]
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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