Hi All,
I have a UDAF that seems to perform poorly when its input is skewed. I have
been debugging the UDAF implementation but I don't see any code that is
causing the performance to degrade. More details on the data and the
experiments I have run.
DataSet: Assume 3 columns, column1 being the key.
Column1 Column2 Column3
a 1 x
a 2 x
a 3 x
a 4 x
a 5 x
a 6 z
5 million row for a
....
a 1000000 y
b 9 y
b 9 y
b 10 y
3 million rows for b
...
more rows
total rows is 100 million
a has 5 million rows.Column2 for a has 1 million unique values.
b has 3 million rows. Column2 for b has 800000 unique values.
Column 3 has just 100s of unique values not in the order of millions, for
both a and b.
Say totally there are 100 million rows as the input to a UDAF aggregation.
And the skew in data is for the keys a and b. All other rows can be ignored
and do not cause any performance issue/ hot partitions.
The code does a dataSet.groupBy("Column1").agg(udaf("Column2", "Column3").
I commented out the UDAF implementation for update and merge methods, so
essentially the UDAF was doing nothing.
With this code (empty updated and merge for UDAF) the performance for a
mircro-batch is 16 minutes per micro-batch, micro-batch containing 100
million rows, with 5million rows for a and 1 million unique values for
Column2 for a.
But when I pass empty values for Column2 with nothing else change,
effectively reducing the 1 million unique values for Column2 to just 1
unique value, empty value. The batch processing time goes down to 4 minutes.
So I am trying to understand why is there such a big performance
difference? What in UDAF causes the processing time to increase in orders
of magnitude when there is a skew in the data as observed above?
Any insight from spark developers, contributors, or anyone else who has a
deeper understanding of UDAF would be helpful.
Thanks,
Bharath
