pytest-spark

pytest-spark ############

.. image:: https://travis-ci.org/malexer/pytest-spark.svg?branch=master :target: https://travis-ci.org/malexer/pytest-spark

pytest_ plugin to run the tests with support of pyspark (Apache Spark_).

This plugin will allow to specify SPARK_HOME directory in pytest.ini and thus to make "pyspark" importable in your tests which are executed by pytest.

You can also define "spark_options" in pytest.ini to customize pyspark, including "spark.jars.packages" option which allows to load external libraries (e.g. "com.databricks:spark-xml").

pytest-spark provides session scope fixtures spark_context and spark_session which can be used in your tests.

Note: no need to define SPARK_HOME if you've installed pyspark using pip (e.g. pip install pyspark) - it should be already importable. In this case just don't define SPARK_HOME neither in pytest (pytest.ini / --spark_home) nor as environment variable.

Install

.. code-block:: shell

$ pip install pytest-spark

Usage

Set Spark location

To run tests with required spark_home location you need to define it by using one of the following methods:

1. Specify command line option "--spark_home"::

   $ pytest --spark_home=/opt/spark

2. Add "spark_home" value to pytest.ini in your project directory::

   [pytest] spark_home = /opt/spark

3. Set the "SPARK_HOME" environment variable.

pytest-spark will try to import pyspark from provided location.

.. note:: "spark_home" will be read in the specified order. i.e. you can override pytest.ini value by command line option.

Customize spark_options

Just define "spark_options" in your pytest.ini, e.g.::

[pytest]
spark_home = /opt/spark
spark_options =
    spark.app.name: my-pytest-spark-tests
    spark.executor.instances: 1
    spark.jars.packages: com.databricks:spark-xml_2.12:0.5.0

Using the spark_context fixture

Use fixture spark_context in your tests as a regular pyspark fixture. SparkContext instance will be created once and reused for the whole test session.

Example::

def test_my_case(spark_context):
    test_rdd = spark_context.parallelize([1, 2, 3, 4])
    # ...

Using the spark_session fixture (Spark 2.0 and above)

Use fixture spark_session in your tests as a regular pyspark fixture. A SparkSession instance with Hive support enabled will be created once and reused for the whole test session.

Example::

def test_spark_session_dataframe(spark_session):
    test_df = spark_session.createDataFrame([[1,3],[2,4]], "a: int, b: int")
    # ...

Overriding default parameters of the spark_session fixture

By default spark_session will be loaded with the following configurations :

Example::

{
    'spark.app.name': 'pytest-spark',
    'spark.default.parallelism': 1,
    'spark.dynamicAllocation.enabled': 'false',
    'spark.executor.cores': 1,
    'spark.executor.instances': 1,
    'spark.io.compression.codec': 'lz4',
    'spark.rdd.compress': 'false',
    'spark.sql.shuffle.partitions': 1,
    'spark.shuffle.compress': 'false',
    'spark.sql.catalogImplementation': 'hive',
}

You can override some of these parameters in your pytest.ini. For example, removing Hive Support for the spark session :

Example::

[pytest]
spark_home = /opt/spark
spark_options =
    spark.sql.catalogImplementation: in-memory

Development

Tests

Run tests locally::

$ docker-compose up --build

.. _pytest: http://pytest.org/ .. _Apache Spark: https://spark.apache.org/