short answer: PySpark does not support UDAF (user defined aggregate
function) for now.
On Tue, Feb 9, 2016 at 11:44 PM, Viktor ARDELEAN <viktor0...@gmail.com>
wrote:
> Hello,
>
> I am using following transformations on RDD:
>
> rddAgg = df.map(lambda l: (Row(a = l.a, b= l.b, c = l.c), l))\
> .aggregateByKey([], lambda accumulatorList, value: accumulatorList
> + [value], lambda list1, list2: [list1] + [list2])
>
> I want to use the dataframe groupBy + agg transformation instead of map +
> aggregateByKey because as far as I know dataframe transformations are faster
> than RDD transformations.
>
> I just can't figure out how to use custom aggregate functions with agg.
>
> *First step is clear:*
>
> groupedData = df.groupBy("a","b","c")
>
> *Second step is not very clear to me:*
>
> dfAgg = groupedData.agg(<I should call here a UDF that transforms each row to
> a list and merges it?>)
>
> The agg documentations says the following:
> agg(**exprs*)
> <https://spark.apache.org/docs/1.3.1/api/python/pyspark.sql.html?highlight=min#pyspark.sql.GroupedData.agg>
>
> Compute aggregates and returns the result as a DataFrame
> <https://spark.apache.org/docs/1.3.1/api/python/pyspark.sql.html?highlight=min#pyspark.sql.DataFrame>
> .
>
> The available aggregate functions are avg, max, min, sum, count.
>
> If exprs is a single dict mapping from string to string, then the key is
> the column to perform aggregation on, and the value is the aggregate
> function.
>
> Alternatively, exprs can also be a list of aggregate Column
> <https://spark.apache.org/docs/1.3.1/api/python/pyspark.sql.html?highlight=min#pyspark.sql.Column>
> expressions.
> Parameters: *exprs* – a dict mapping from column name (string) to
> aggregate functions (string), or a list of Column
> <https://spark.apache.org/docs/1.3.1/api/python/pyspark.sql.html?highlight=min#pyspark.sql.Column>
> .
>
> Thanks for help!
> --
> Viktor
>
> *P* Don't print this email, unless it's really necessary. Take care of
> the environment.
>
