Your 2 examples are doing different things. The Pandas UDF is doing a grouped map, whereas your Python UDF is doing an aggregate.
I think you want your Pandas UDF to be PandasUDFType.GROUPED_AGG? Is your result the same? From: Lian Jiang <jiangok2...@gmail.com> Date: Sunday, April 5, 2020 at 3:28 AM To: user <user@spark.apache.org> Subject: pandas_udf is very slow 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
