Base classes to use when writing tests with Spark.

You've written an awesome program in Spark and now its time to write some tests. Only you find yourself writing the code to setup and tear down local mode Spark in between each suite and you say to your self: This is not my beautiful code.

So you include com.holdenkarau.spark-testing-base [spark_version]_0.3.3 and extend one of the classes and write some simple tests instead. For example to include this in a project using Spark 1.6.1:

"com.holdenkarau" %% "spark-testing-base" % "1.6.1_0.3.3"

or

    <dependency>
        <groupId>com.holdenkarau</groupId>
        <artifactId>spark-testing-base_2.10</artifactId>
        <version>${spark.version}_0.3.3</version>
        <scope>test</scope>
    </dependency>

How to use it inside your code? have a look at the wiki page.

Note that new versions (0.0.8+) are built against Spark 1.3.0+ for simplicity, but if you need an old version file an issue and I will re-enable cross-builds for older versions.

The spark-packages page for spark-testing-base lists the releases available.

This package is can be cross compiled against scala 2.10.4 and 2.11.6 in the traditional manner.

The default SBT testing java options are too small to support running many of the tests due to the need to launch Spark in local mode. To increase the amount of memory in a build.sbt file you can add:

    javaOptions ++= Seq("-Xms512M", "-Xmx2048M", "-XX:MaxPermSize=2048M", "-XX:+CMSClassUnloadingEnabled")

If using surefire you can add:

    <argLine>-Xmx2048m -XX:MaxPermSize=2048m</argLine>

Note: the specific memory values are examples only (and the values used to run spark-testing-base's own tests)

Make sure to disable parallel execution

In sbt you can add:

    parallelExecution in Test := false

In surefire make sure that forkCount is set to 1 and reuseForks is true.

Much of this code is a stripped down version of the test suite bases that are in Apache Spark but are not accessible. Other parts are also inspired by ssbase (scalacheck generators for Spark).

Other parts of this are implemented on top of the test suite bases to make your life even easier.