Abdeali Kothari created SPARK-25591:
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Summary: 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
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}
######## Output 1 - with SHUFFLE=False
...
# >>> Check7 100.0
######## Output 2 - with SHUFFLE=True
...
# >>> Check7 101.0
Basically looks like:
- Accumulator works only for last UDF before a shuffle-like operation
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