# How to Convert Python Functions into PySpark UDFs

We have a Spark dataframe and want to apply a specific transformation to a column/a set of columns. In Pandas, we can use the map() and apply() functions. The Spark equivalent is the udf (user-defined function). A user defined function is generated in two steps. In step one, we create a normal python function, which is then in step two converted into a udf, which can then be applied to the data frame.

This post shows how to code and use a udf. First, we take a look at how to proceed in the simplest case: a function with one input and one output variable. Afterwards we level up our udf abilities and use a function with multiple in- and output variables. The code has been tested for Spark 2.1.1.

A general remark: When dealing with udfs, it is important to be aware of the type of output that your function returns. If you get the output data types wrong, your udf will return only nulls.

For both of the examples we need to import the following modules:

from pyspark.sql.functions import udf, struct, col
from pyspark.sql.types import *
import pyspark.sql.functions as func


## Level 0: One-In-One-Out

### Step 1: Define your function

I was recently recoding binned ages into numeric format. This is an abbreviated version of a function that takes a string, compares it to several options and finally returns a float.

def extractAge(mystring):
if mystring.strip() == 'age 18-25':
return 21.5
if mystring.strip() == 'age 26-35':
return 30.5
else:
return None


### Step 2: Create the udf (user-defined function)

The function extractAge() takes a single input and returns a single output of type float. The udf-syntax therefore is:

extract_age_udf = udf(lambda row: extractAge(row), FloatType())


The return type (here FloatType) can be any of the standard Spark datatypes

### Step 3: Usage

Create a test dataframe:

df = sc.parallelize([[1., 'age 18-25'], [2., 'age 100+']]).toDF(["f1","age"])
df.show()
>>>
+---+---------+
| f1|      age|
+---+---------+
|1.0|age 18-25|
|2.0| age 100+|
+---+---------+


Apply function:

df_new = df.withColumn('age_n', extract_age_udf(col('age')))
df_new.show()
>>>
+---+---------+-----+
| f1|      age|age_n|
+---+---------+-----+
|1.0|age 18-25| 21.5|
|2.0| age 100+| null|
+---+---------+-----+


## Levelling up: Many-In-Many-Out

### Step 1: Define your function

Let’s assume we want to create a function which takes as input two columns and returns the sum and the difference of the two columns.

def sum_diff(f1, f2):
return [f1 + f2, f1-f2]


### Step 2: Create the udf

Since the function now returns a vector, we can’t just use the FloatType() data type anymore, we need to first assemble the schema of the output. Both the elements are of type float, so the schema looks like this:

schema = StructType([
StructField("sum", FloatType(), False),
StructField("diff", FloatType(), False)
])


Having defined the schema, we can define the udf as follows:

sum_diff_udf = udf(lambda row: sum_diff(row[0], row[1]), schema)


Alternatively, we can define function and udf as

def sum_diff(row):
return [row[0] + row[1], row[0]-row[1]]

sum_diff_udf = udf(lambda row: sum_diff(row), schema)


and still get the same output.

### Step 3: Usage

Create a test dataframe:

df = spark.createDataFrame(pd.DataFrame([[1., 2.], [2., 4.]], columns=['f1', 'f2']))
df.show()
>>>
+---+---+
| f1| f2|
+---+---+
|1.0|2.0|
|2.0|4.0|
+---+---+


Apply function:

df_new = test_data.withColumn("sum_diff", sum_diff_udf(struct([col('f1'), col('f2')])))\
.select('*', 'sum_diff.*')
df_new.show()

>>>
+---+---+----------+---+----+
| f1| f2|  sum_diff|sum|diff|
+---+---+----------+---+----+
|1.0|2.0|[3.0,-1.0]|3.0|-1.0|
|2.0|4.0|[6.0,-2.0]|6.0|-2.0|
+---+---+----------+---+----+



Update: I just found this post commenting on execution plans for the * expansion. It suggests, wrapping the results in an array and then exploding the array. While the exploding has some drawbacks, it means, that you only need to execute the udf once, which is good, since udfs are inherently slow to execute. Adapting this idea for the example above leads to a code like this:

df_new = (
test_data
.select(
'*', func.explode(
func.array(
sum_diff_udf(struct([col('f1'), col('f2')]))
)
).alias('sum_diff')
)
.select('*', col('sum_diff.*'))
)
df_new.show()


## Example of What Happens if you get your Output Data Type Wrong

As mentioned above, if you get your output data type wrong, your udf will return nulls. Here is a modified version of the one-in-one-out example above. If we assume the return to be float, but in fact, the function returns an integer, the udf returns nulls. Code as above, but modify the function to return an integer while we keep on telling the udf that the function should return a float.

def extractAge(mystring):
if mystring.strip() == 'age 18-25':
return 21
if mystring.strip() == 'age 26-35':
return 30
else:
return None

extract_age_udf = udf(lambda row: extractAge(row), FloatType())


Applying the udf will lead to the output:

df_new = df.withColumn('age_n', extract_age_udf(col('age')))
df_new.show()
+---+---------+-----+
| f1|      age|age_n|
+---+---------+-----+
|1.0|age 18-25| null|
|2.0| age 100+| null|
+---+---------+-----+


Note, that it does not matter, which are the data types. As long as they are not consistent, the udf will return nulls.

## Summary and Conclusion

In this post we have taken a look at how to convert a Python function into a PySpark UDF. We have looked at the cases of a simple One-In-One-Out situation and at a situation where our function has multiple input and output variables.

I hope this post has been useful for you!

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