right now, i'm using the colums-at-a-time mapping https://github.com/yupbank/tf-spark-serving/blob/master/tss/utils.py#L129
On Thu, Mar 7, 2019 at 4:00 PM Sean Owen <sro...@gmail.com> wrote: > Maybe, it depends on what you're doing. It sounds like you are trying > to do row-at-a-time mapping, even on a pandas DataFrame. Is what > you're doing vectorized? may not help much. > Just make the pandas Series into a DataFrame if you want? and a single > col back to Series? > > On Thu, Mar 7, 2019 at 2:45 PM peng yu <yupb...@gmail.com> wrote: > > > > pandas/arrow is for the memory efficiency, and mapPartitions is only > available to rdds, for sure i can do everything in rdd. > > > > But i thought that's the whole point of having pandas_udf, so my program > run faster and consumes less memory ? > > > > On Thu, Mar 7, 2019 at 3:40 PM Sean Owen <sro...@gmail.com> wrote: > >> > >> Are you just applying a function to every row in the DataFrame? you > >> don't need pandas at all. Just get the RDD of Row from it and map a > >> UDF that makes another Row, and go back to DataFrame. Or make a UDF > >> that operates on all columns and returns a new value. mapPartitions is > >> also available if you want to transform an iterator of Row to another > >> iterator of Row. > >> > >> On Thu, Mar 7, 2019 at 2:33 PM peng yu <yupb...@gmail.com> wrote: > >> > > >> > it is very similar to SCALAR, but for SCALAR the output can't be > struct/row and the input has to be pd.Series, which doesn't support a row. > >> > > >> > I'm doing tensorflow batch inference in spark, > https://github.com/yupbank/tf-spark-serving/blob/master/tss/serving.py#L108 > >> > > >> > Which i have to do the groupBy in order to use the apply function, > i'm wondering why not just enable apply to df ? > >> > > >> > On Thu, Mar 7, 2019 at 3:15 PM Sean Owen <sro...@gmail.com> wrote: > >> >> > >> >> Are you looking for SCALAR? that lets you map one row to one row, but > >> >> do it more efficiently in batch. What are you trying to do? > >> >> > >> >> On Thu, Mar 7, 2019 at 2:03 PM peng yu <yupb...@gmail.com> wrote: > >> >> > > >> >> > I'm looking for a mapPartition(pandas_udf) for a > pyspark.Dataframe. > >> >> > > >> >> > ``` > >> >> > @pandas_udf(df.schema, PandasUDFType.MAP) > >> >> > def do_nothing(pandas_df): > >> >> > return pandas_df > >> >> > > >> >> > > >> >> > new_df = df.mapPartition(do_nothing) > >> >> > ``` > >> >> > pandas_udf only support scala or GROUPED_MAP. Why not support > just Map? > >> >> > > >> >> > On Thu, Mar 7, 2019 at 2:57 PM Sean Owen <sro...@gmail.com> wrote: > >> >> >> > >> >> >> Are you looking for @pandas_udf in Python? Or just mapPartition? > Those exist already > >> >> >> > >> >> >> On Thu, Mar 7, 2019, 1:43 PM peng yu <yupb...@gmail.com> wrote: > >> >> >>> > >> >> >>> There is a nice map_partition function in R `dapply`. so that > user can pass a row to udf. > >> >> >>> > >> >> >>> I'm wondering why we don't have that in python? > >> >> >>> > >> >> >>> I'm trying to have a map_partition function with pandas_udf > supported > >> >> >>> > >> >> >>> thanks! >
