Hi Leon, please refer to this link: https://docs.databricks.com/spark/latest/spark-sql/udf-python-pandas.html
I have found using GROUP MAP to be a bit tricky, please refer to the statement: "All data for a group is loaded into memory before the function is applied. This can lead to out of memory exceptions, especially if the group sizes are skewed. The configuration for maxRecordsPerBatch <https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html#setting-arrow-batch-size> is not applied on groups and it is up to you to ensure that the grouped data will fit into the available memory." Pandas UDF is definitely faster than the general Python UDFs'. But once again all depends on the data volume against which you are testing, and the way the UDF has been written. Thanks and Regards, Gourav On Sun, Apr 5, 2020 at 8:28 AM Lian Jiang <jiangok2...@gmail.com> wrote: > Hi, > > I am using pandas udf in pyspark 2.4.3 on EMR 5.21.0. pandas udf is > favored over non pandas udf per > https://www.twosigma.com/wp-content/uploads/Jin_-_Improving_Python__Spark_Performance_-_Spark_Summit_West.pdf. > > > My data has about 250M records and the pandas udf code is like: > > def pd_udf_func(data): > return pd.DataFrame(["id"]) > > pd_udf = pandas_udf( > pd_udf_func, > returnType=("id int"), > functionType=PandasUDFType.GROUPED_MAP > ) > df3 = df.groupBy("id").apply(pd_udf) > df3.explain() > """ > == Physical Plan == > FlatMapGroupsInPandas [id#9L], pd_udf_func(id#9L, txt#10), [id#30] > +- *(2) Sort [id#9L ASC NULLS FIRST], false, 0 > +- Exchange hashpartitioning(id#9L, 200) > +- *(1) Project [id#9L, id#9L, txt#10] > +- Scan ExistingRDD[id#9L,txt#10] > """ > > As you can see, this pandas udf does nothing but returning a row having a > pandas dataframe having None values. In reality, this pandas udf has > complicated logic (e.g. iterate through the pandas dataframe rows and do > some calculation). This simplification is to reduce noise caused by > application specific logic. This pandas udf takes hours to run using 10 > executors (14 cores and 64G mem each). On the other hand, below non-pandas > udf can finish in minutes: > > def udf_func(data_list): > return "hello" > > udf = udf(udf_func, StringType()) > df2 = > df.groupBy("id").agg(F.collect_list('txt').alias('txt1')).withColumn('udfadd', > udf('txt1')) > df2.explain() > """ > == Physical Plan == > *(1) Project [id#9L, txt1#16, pythonUDF0#24 AS udfadd#20] > +- BatchEvalPython [udf_func(txt1#16)], [id#9L, txt1#16, pythonUDF0#24] > +- ObjectHashAggregate(keys=[id#9L], functions=[collect_list(txt#10, 0, > 0)]) > +- Exchange hashpartitioning(id#9L, 200) > +- ObjectHashAggregate(keys=[id#9L], > functions=[partial_collect_list(txt#10, 0, 0)]) > +- Scan ExistingRDD[id#9L,txt#10] > """ > > The physical plans show pandas udf uses sortAggregate (slower) while > non-pandas udf uses objectHashAggregate (faster). > > Below is what I have tried to improve the performance of pandas udf but > none of them worked: > 1. repartition before groupby. For example, df.repartition(140, > "id").groupBy("id").apply(pd_udf). 140 is the same as > spark.sql.shuffle.partitions. > I hope groupby can benefit from the repartition but according to the > execution plan the repartition seems to be ignored since groupby will do > partitioning itself. > > 2. although this slowness is more likely caused by pandas udf instead of > groupby, I still played with shuffle settings such as > spark.shuffle.compress=True, > spark.shuffle.spill.compress=True. > > 3. I played with serDe settings such as > spark.serializer=org.apache.spark.serializer.KryoSerializer. > Also I tried pyarrow settings such as spark.sql.execution.arrow.enabled=True > and spark.sql.execution.arrow.maxRecordsPerBatch=100000 > > 4. I tried to replace the solution of "groupby + pandas udf " with > combineByKey, reduceByKey, repartition + mapPartition. But it is not easy > since the pandas udf has complicated logic. > > My questions: > > 1. why pandas udf is so slow? > 2. is there a way to improve the performance of pandas_udf? > 3. in case it is a known issue of pandas udf, what other remedy I can use? > I guess I need to think harder on combineByKey, reduceByKey, repartition + > mapPartition. But want to know if I missed anything obvious. > > Any clue is highly appreciated. > > Thanks > Leon > > > > >
