[jira] [Commented] (SPARK-25591) PySpark Accumulators with multiple PythonUDFs

["Dongjoon Hyun (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-25591?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16732556#comment-16732556 ] 

Dongjoon Hyun commented on SPARK-25591:
---------------------------------------

Hi, [~viirya], [~hyukjin.kwon].

This is only in branch-2.4. Can we backport this to older branches like branch-2.3 and branch-2.2?

cc [~AbdealiJK]

> PySpark Accumulators with multiple PythonUDFs
> ---------------------------------------------
>
>                 Key: SPARK-25591
>                 URL: https://issues.apache.org/jira/browse/SPARK-25591
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 2.3.2
>            Reporter: Abdeali Kothari
>            Assignee: Liang-Chi Hsieh
>            Priority: Blocker
>              Labels: correctness
>             Fix For: 2.4.0
>
>
> When having multiple Python UDFs - the last Python UDF's accumulator is the only accumulator that gets updated.
> {code:python}
> import pyspark
> from pyspark.sql import SparkSession, Row
> from pyspark.sql import functions as F
> from pyspark.sql import types as T
> from pyspark import AccumulatorParam
> spark = SparkSession.builder.getOrCreate()
> spark.sparkContext.setLogLevel("ERROR")
> test_accum = spark.sparkContext.accumulator(0.0)
> SHUFFLE = False
> def main(data):
>     print(">>> Check0", test_accum.value)
>     def test(x):
>         global test_accum
>         test_accum += 1.0
>         return x
>     print(">>> Check1", test_accum.value)
>     def test2(x):
>         global test_accum
>         test_accum += 100.0
>         return x
>     print(">>> Check2", test_accum.value)
>     func_udf = F.udf(test, T.DoubleType())
>     print(">>> Check3", test_accum.value)
>     func_udf2 = F.udf(test2, T.DoubleType())
>     print(">>> Check4", test_accum.value)
>     data = data.withColumn("out1", func_udf(data["a"]))
>     if SHUFFLE:
>         data = data.repartition(2)
>     print(">>> Check5", test_accum.value)
>     data = data.withColumn("out2", func_udf2(data["b"]))
>     if SHUFFLE:
>         data = data.repartition(2)
>     print(">>> Check6", test_accum.value)
>     data.show()  # ACTION
>     print(">>> Check7", test_accum.value)
>     return data
> df = spark.createDataFrame([
>     [1.0, 2.0]
> ], schema=T.StructType([T.StructField(field_name, T.DoubleType(), True) for field_name in ["a", "b"]]))
> df2 = main(df)
> {code}
> {code:python}
> ######## Output 1 - with SHUFFLE=False
> ...
> # >>> Check7 100.0
> ######## Output 2 - with SHUFFLE=True
> ...
> # >>> Check7 101.0
> {code}
> Basically looks like:
>  - Accumulator works only for last UDF before a shuffle-like operation



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