Testing PySpark#
This guide is a reference for writing robust tests for PySpark code.
To view the docs for PySpark test utils, see here.
Build a PySpark Application#
Here is an example for how to start a PySpark application. Feel free to skip to the next section, “Testing your PySpark Application,” if you already have an application you’re ready to test.
First, start your Spark Session.
[3]:
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
# Create a SparkSession
spark = SparkSession.builder.appName("Testing PySpark Example").getOrCreate()
Next, create a DataFrame.
[5]:
sample_data = [{"name": "John D.", "age": 30},
{"name": "Alice G.", "age": 25},
{"name": "Bob T.", "age": 35},
{"name": "Eve A.", "age": 28}]
df = spark.createDataFrame(sample_data)
Now, let’s define and apply a transformation function to our DataFrame.
[7]:
from pyspark.sql.functions import col, regexp_replace
# Remove additional spaces in name
def remove_extra_spaces(df, column_name):
# Remove extra spaces from the specified column
df_transformed = df.withColumn(column_name, regexp_replace(col(column_name), "\\s+", " "))
return df_transformed
transformed_df = remove_extra_spaces(df, "name")
transformed_df.show()
+---+--------+
|age| name|
+---+--------+
| 30| John D.|
| 25|Alice G.|
| 35| Bob T.|
| 28| Eve A.|
+---+--------+
Testing your PySpark Application#
Now let’s test our PySpark transformation function.
One option is to simply eyeball the resulting DataFrame. However, this can be impractical for large DataFrame or input sizes.
A better way is to write tests. Here are some examples of how we can test our code. The examples below apply for Spark 3.5 and above versions.
Note that these examples are not exhaustive, as there are many other test framework alternatives which you can use instead of unittest or pytest. The built-in PySpark testing util functions are standalone, meaning they can be compatible with any test framework or CI test pipeline.
Option 1: Using Only PySpark Built-in Test Utility Functions#
For simple ad-hoc validation cases, PySpark testing utils like assertDataFrameEqual and assertSchemaEqual can be used in a standalone context. You could easily test PySpark code in a notebook session. For example, say you want to assert equality between two DataFrames:
[10]:
import pyspark.testing
from pyspark.testing.utils import assertDataFrameEqual
# Example 1
df1 = spark.createDataFrame(data=[("1", 1000), ("2", 3000)], schema=["id", "amount"])
df2 = spark.createDataFrame(data=[("1", 1000), ("2", 3000)], schema=["id", "amount"])
assertDataFrameEqual(df1, df2) # pass, DataFrames are identical
[11]:
# Example 2
df1 = spark.createDataFrame(data=[("1", 0.1), ("2", 3.23)], schema=["id", "amount"])
df2 = spark.createDataFrame(data=[("1", 0.109), ("2", 3.23)], schema=["id", "amount"])
assertDataFrameEqual(df1, df2, rtol=1e-1) # pass, DataFrames are approx equal by rtol
You can also simply compare two DataFrame schemas:
[13]:
from pyspark.testing.utils import assertSchemaEqual
from pyspark.sql.types import StructType, StructField, ArrayType, DoubleType
s1 = StructType([StructField("names", ArrayType(DoubleType(), True), True)])
s2 = StructType([StructField("names", ArrayType(DoubleType(), True), True)])
assertSchemaEqual(s1, s2) # pass, schemas are identical
Option 2: Using Unit Test#
For more complex testing scenarios, you may want to use a testing framework.
One of the most popular testing framework options is unit tests. Let’s walk through how you can use the built-in Python unittest library to write PySpark tests.
First, you will need a Spark session. You can use the @classmethod decorator from the unittest package to take care of setting up and tearing down a Spark session.
[15]:
import unittest
class PySparkTestCase(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.spark = SparkSession.builder.appName("Testing PySpark Example").getOrCreate()
@classmethod
def tearDownClass(cls):
cls.spark.stop()
Now let’s write a unittest class.
[17]:
from pyspark.testing.utils import assertDataFrameEqual
class TestTranformation(PySparkTestCase):
def test_single_space(self):
sample_data = [{"name": "John D.", "age": 30},
{"name": "Alice G.", "age": 25},
{"name": "Bob T.", "age": 35},
{"name": "Eve A.", "age": 28}]
# Create a Spark DataFrame
original_df = spark.createDataFrame(sample_data)
# Apply the transformation function from before
transformed_df = remove_extra_spaces(original_df, "name")
expected_data = [{"name": "John D.", "age": 30},
{"name": "Alice G.", "age": 25},
{"name": "Bob T.", "age": 35},
{"name": "Eve A.", "age": 28}]
expected_df = spark.createDataFrame(expected_data)
assertDataFrameEqual(transformed_df, expected_df)
When run, unittest will pick up all functions with a name beginning with “test.”
Option 3: Using Pytest#
We can also write our tests with pytest, which is one of the most popular Python testing frameworks.
Using a pytest fixture allows us to share a spark session across tests, tearing it down when the tests are complete.
[20]:
import pytest
@pytest.fixture
def spark_fixture():
spark = SparkSession.builder.appName("Testing PySpark Example").getOrCreate()
yield spark
We can then define our tests like this:
[22]:
import pytest
from pyspark.testing.utils import assertDataFrameEqual
def test_single_space(spark_fixture):
sample_data = [{"name": "John D.", "age": 30},
{"name": "Alice G.", "age": 25},
{"name": "Bob T.", "age": 35},
{"name": "Eve A.", "age": 28}]
# Create a Spark DataFrame
original_df = spark_fixture.createDataFrame(sample_data)
# Apply the transformation function from before
transformed_df = remove_extra_spaces(original_df, "name")
expected_data = [{"name": "John D.", "age": 30},
{"name": "Alice G.", "age": 25},
{"name": "Bob T.", "age": 35},
{"name": "Eve A.", "age": 28}]
expected_df = spark_fixture.createDataFrame(expected_data)
assertDataFrameEqual(transformed_df, expected_df)
When you run your test file with the pytest command, it will pick up all functions that have their name beginning with “test.”
Putting It All Together!#
Let’s see all the steps together, in a Unit Test example.
[25]:
# pkg/etl.py
import unittest
from pyspark.sql import SparkSession
from pyspark.sql.functions import col
from pyspark.sql.functions import regexp_replace
from pyspark.testing.utils import assertDataFrameEqual
# Create a SparkSession
spark = SparkSession.builder.appName("Sample PySpark ETL").getOrCreate()
sample_data = [{"name": "John D.", "age": 30},
{"name": "Alice G.", "age": 25},
{"name": "Bob T.", "age": 35},
{"name": "Eve A.", "age": 28}]
df = spark.createDataFrame(sample_data)
# Define DataFrame transformation function
def remove_extra_spaces(df, column_name):
# Remove extra spaces from the specified column using regexp_replace
df_transformed = df.withColumn(column_name, regexp_replace(col(column_name), "\\s+", " "))
return df_transformed
[26]:
# pkg/test_etl.py
import unittest
from pyspark.sql import SparkSession
# Define unit test base class
class PySparkTestCase(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.spark = SparkSession.builder.appName("Sample PySpark ETL").getOrCreate()
@classmethod
def tearDownClass(cls):
cls.spark.stop()
# Define unit test
class TestTranformation(PySparkTestCase):
def test_single_space(self):
sample_data = [{"name": "John D.", "age": 30},
{"name": "Alice G.", "age": 25},
{"name": "Bob T.", "age": 35},
{"name": "Eve A.", "age": 28}]
# Create a Spark DataFrame
original_df = spark.createDataFrame(sample_data)
# Apply the transformation function from before
transformed_df = remove_extra_spaces(original_df, "name")
expected_data = [{"name": "John D.", "age": 30},
{"name": "Alice G.", "age": 25},
{"name": "Bob T.", "age": 35},
{"name": "Eve A.", "age": 28}]
expected_df = spark.createDataFrame(expected_data)
assertDataFrameEqual(transformed_df, expected_df)
[27]:
unittest.main(argv=[''], verbosity=0, exit=False)
Ran 1 test in 1.734s
OK
[27]:
<unittest.main.TestProgram at 0x174539db0>