How to import pyspark UDF into main class

user_5617110 0
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I have two files. functions.py has a function and creates a pyspark udf from that function. main.py attempts to import the udf. However, main.py seems to have trouble accessing the function in functions.py.

functions.py:

from pyspark.sql.functions import udf
from pyspark.sql.types import StringType

def do_something(x):
    return x + 'hello'

sample_udf = udf(lambda x: do_something(x), StringType())

main.py:

from functions import sample_udf, do_something
df = spark.read.load(file)
df.withColumn("sample",sample_udf(col("text")))

This results in an error:

17/10/03 19:35:29 WARN TaskSetManager: Lost task 0.0 in stage 3.0 (TID 6, ip-10-223-181-5.ec2.internal, executor 3): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/usr/lib/spark/python/pyspark/worker.py", line 164, in main
    func, profiler, deserializer, serializer = read_udfs(pickleSer, infile)
  File "/usr/lib/spark/python/pyspark/worker.py", line 93, in read_udfs
    arg_offsets, udf = read_single_udf(pickleSer, infile)
  File "/usr/lib/spark/python/pyspark/worker.py", line 79, in read_single_udf
    f, return_type = read_command(pickleSer, infile)
  File "/usr/lib/spark/python/pyspark/worker.py", line 55, in read_command
    command = serializer._read_with_length(file)
  File "/usr/lib/spark/python/pyspark/serializers.py", line 169, in _read_with_length
    return self.loads(obj)
  File "/usr/lib/spark/python/pyspark/serializers.py", line 454, in loads
    return pickle.loads(obj)
AttributeError: 'module' object has no attribute 'do_something'

If I bypass the do_something function and just put it inside the udf, eg: udf(lambda x: x + ' hello', StringType()), the UDF imports fine - but my function is a little longer and it would be nice to have it encapsulated in a separate function. What's the correct way to achieve this?

pyspark apache-spark user-defined-functions python
2 Answers
user_47251970

I think a cleaner solution would be to use the udf decorator to define your udf function :

import pyspark.sql.functions as F
from pyspark.sql.types import StringType

@F.udf
def sample_udf(x):
     return x + 'hello'

With this solution, the udf does not reference any other function and you don't need the sc.addPyFile in your main code.

from functions import sample_udf, do_something
df = spark.read.load(file)
df.withColumn("sample",sample_udf(col("text")))
# It works :)

For some older versions of spark, the decorator doesn't support typed udf some you might have to define a custom decorator as follow :

import pyspark.sql.functions as F
import pyspark.sql.types as t

# Custom udf decorator which accept return type
def udf_typed(returntype=t.StringType()):
    def _typed_udf_wrapper(func):
        return F.udf(func, returntype)
    return _typed_udf_wrapper

@udf_typed(t.IntegerType())
def my_udf(x)
    return int(x)
user_94350990
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