Using external library in PySpark UDF pickle error

Question

I’m trying the following code:

import pandas as pd
from pymorphy2 import MorphAnalyzer
from pyspark.sql import SparkSession
from pyspark.sql import types as T
from pyspark.sql import functions as F

spark = SparkSession.builder.appName("udf").getOrCreate()

def gender(s):
    m = MorphAnalyzer()
    return m.parse(s)[0].tag.gender

gen = F.udf(gender, T.StringType())

df = spark.createDataFrame(pd.DataFrame({"name": ["кирилл", "вавила"]}))

df.select(gen("name").alias("gender")).show()

and more or less expectedly getting the following error message:

ERROR Executor: Exception in task 2.0 in stage 29.0 (TID 151)
net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for pyspark.cloudpickle.cloudpickle._make_skeleton_class). This happens when an unsupported/unregistered class is being unpickled that requires construction arguments. Fix it by registering a custom IObjectConstructor for this class.
    at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
    at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:759)
    at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:199)

What could be the easiest way to circumvent the error (if any) ?

Asked By: Sergey Bushmanov

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Source

Answer 1

The error means Spark can’t pickle(serialize) the object you passed. The class of that object is a custom type(TypedGrammeme) defined by Pymorphy2.
So, convert to a str like the following.

def gender(s):
    ...
    return str(m.parse(s)[0].tag.gender)

Also, you should avoid to do a resource intensive task in your UDF, such as creating a MorphAnalyzer. Create a single instance and reuse it using a global variable.

Answered By: relent95

Answer 2

You could use pandas_udf which is vectorized (more efficient compared to a regular udf).

import pandas as pd
from pymorphy2 import MorphAnalyzer
from pyspark.sql import SparkSession, types as T, functions as F

spark = SparkSession.builder.appName("udf").getOrCreate()

@F.pandas_udf(T.StringType())
def gender(s: pd.Series) -> pd.Series:
    return s.apply(lambda x: MorphAnalyzer().parse(x)[0].tag.gender)

df = spark.createDataFrame(pd.DataFrame({"name": ["кирилл", "вавила", "софия"]}))
df.withColumn("gender", gender("name")).show()
# +------+------+
# |  name|gender|
# +------+------+
# |кирилл|  masc|
# |вавила|  masc|
# | софия|  femn|
# +------+------+

In some systems, you may not need pyspark.sql.types library solely for pandas_udf, the function could be defined like this:

@F.pandas_udf('string')
def gender(s: pd.Series) -> pd.Series:
    return s.apply(lambda x: MorphAnalyzer().parse(x)[0].tag.gender)

Answered By: ZygD

Using external library in PySpark UDF pickle error

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