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In this tutorial, I will explain how to get started with test writing for your Spark project.

There is no doubt that testing is a crucial step in any software development project. However, when you are only getting started with test writing, it may seem to be a time-consuming and not a very pleasant activity. For that reason, many developers choose to avoid them in order to go faster and this degrades the quality of the delivered app. But if you include tests into your list of programming habits, they eventually stop being that mind-wrecking and you start gathering benefits from them.

Part 1: Basic Example

As an example, let us take a simple function that filters Spark data frame by value in the specific column age. Here is the content of the file main.py that contains the function we would like to test:

The basic test for this function will consist of the following parts: initialization of Spark context, input and output data frames creation, assertion of expected and actual outputs, closing Spark context:

The major stumbling block arises at the moment when you assert the equality of the two data frames. Using only PySpark methods, it is quite complicated to do and for this reason, it is always pragmatic to move from PySpark to Pandas framework. However, while comparing two data frames the order of rows and columns is important for Pandas. Pandas provides such function like pandas.testing.assert_frame_equal with the parameter check_like=True to ignore the order of columns. However, it does not have a built-in functionality to ignore the order of rows. Therefore, to make the two data frames comparable we will use the created method get_sorted_data_frame.

To launch the example, in your terminal simply type pytest at the root of your project that contains main.py and test_main.py. Make sure you have set all the necessary environment variables. To run this tutorial on Mac you will need to set PYSPARK_PYTHON and JAVA_HOME environment variables.

Part 2: Refactoring of Spark Context

This tutorial demonstrates the basics of test writing. However, your real project will probably contain more than one test and you would not want to initialize resource-intensive Spark Context over and over again. For that reason, with Pytest you can create conftest.py that launches a single Spark session for all of your tests and when all of them were run, the session is closed. In order to make the session visible for tests, you should decorate the functions with Pytest fixtures. Here is the content of conftest.py:

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It is important that conftest.py has to be placed at the root of your project! Afterwards, you just need to pass sql_context parameterinto your test function.

Here is how test_filter_spark_data_frame looks like after the refactoring:

I hope you enjoyed this tutorial and happy test writing with Pytest and Spark!

Thanks to Pierre Marcenac, Nicolas Jean, Raphaël Meudec, and Louis Nicolle.

  • Compiling from source

The simplest way to get the latest pandoc release is to use the installer.

For alternative ways to install pandoc, see below under the heading for your operating system.

Windows

There is a package installer at pandoc’s download page. This will install pandoc, replacing older versions, and update your path to include the directory where pandoc’s binaries are installed.

If you prefer not to use the msi installer, we also provide a zip file that contains pandoc’s binaries and documentation. Simply unzip this file and move the binaries to a directory of your choice.

Alternatively, you can install pandoc using Chocolatey:

Chocolatey can also install other software that integrates with Pandoc. For example, to install rsvg-convert (from librsvg, covering formats without SVG support), Python (to use Pandoc filters), and MiKTeX (to typeset PDFs with LaTeX):

By default, Pandoc creates PDFs using LaTeX. We recommend installing it via MiKTeX.

macOS

There is a package installer at pandoc’s download page. If you later want to uninstall the package, you can do so by downloading this script and running it with perl uninstall-pandoc.pl.

Alternatively, you can install pandoc using Homebrew:

Homebrew can also install other software that integrates with Pandoc. For example, to install librsvg (its rsvg-convert covers formats without SVG support), Python (to use Pandoc filters), and BasicTeX (to typeset PDFs with LaTeX):

Note: On unsupported versions of macOS (more than three releases old), Homebrew installs from source, which takes additional time and disk space for the ghc compiler and dependent Haskell libraries.

We also provide a zip file containing the binaries and man pages, for those who prefer not to use the installer. Simply unzip the file and move the binaries and man pages to whatever directory you like.

By default, Pandoc creates PDFs using LaTeX. Because a full MacTeX installation uses four gigabytes of disk space, we recommend BasicTeX or TinyTeX and using the tlmgr tool to install additional packages as needed. If you receive errors warning of fonts not found:

Linux

Check whether the pandoc version in your package manager is not outdated. Pandoc is in the Debian, Ubuntu, Slackware, Arch, Fedora, NiXOS, openSUSE, gentoo and Void repositories.

To get the latest release, we provide a binary package for amd64 architecture on the download page.

The executable is statically linked and has no dynamic dependencies or dependencies on external data files. Note: because of the static linking, the pandoc binary from this package cannot use lua filters that require external lua modules written in C.

Both a tarball and a deb installer are provided. To install the deb:

where $DEB is the path to the downloaded deb. This will install the pandoc executable and man page.

If you use an RPM-based distro, you may be able to install the deb from our download page using alien.

On any distro, you may install from the tarball into $DEST (say, /usr/local/ or $HOME/.local) by doing

where $TGZ is the path to the downloaded zipped tarball. For Pandoc versions before 2.0, which don’t provide a tarball, try instead

You can also install from source, using the instructions below under Compiling from source. Note that most distros have the Haskell platform in their package repositories. For example, on Debian/Ubuntu, you can install it with apt-get install haskell-platform.

For PDF output, you’ll need LaTeX. We recommend installing TeX Live via your package manager. (On Debian/Ubuntu, apt-get install texlive.)

Chrome OS

On Chrome OS, pandoc can be installed using the chromebrew package manager with the command:

This will automatically build and configure pandoc for the specific device you are using.

BSD

Pandoc is in the NetBSD and FreeBSD ports repositories.

Docker

The official Docker images for pandoc can be found at https://github.com/pandoc/dockerfiles and at dockerhub.

The pandoc/core image contains pandoc.

The pandoc/latex image also contains the minimal LaTeX installation needed to produce PDFs using pandoc.

To run pandoc using Docker, converting README.md to README.pdf:

GitHub Actions

Pandoc can be run through GitHub Actions. For some examples, see https://github.com/pandoc/pandoc-action-example.

Compiling from source

If for some reason a binary package is not available for your platform, or if you want to hack on pandoc or use a non-released version, you can install from source.

Getting the pandoc source code

Source tarballs can be found at https://hackage.haskell.org/package/pandoc. For example, to fetch the source for version 1.17.0.3:

Or you can fetch the development code by cloning the repository:

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Note: there may be times when the development code is broken or depends on other libraries which must be installed separately. Unless you really know what you’re doing, install the last released version.

Quick stack method

The easiest way to build pandoc from source is to use stack:

  1. Install stack. Note that Pandoc requires stack >= 1.7.0.

  2. Change to the pandoc source directory and issue the following commands:

    stack setup will automatically download the ghc compiler if you don’t have it. stack install will install the pandoc executable into ~/.local/bin, which you should add to your PATH. This process will take a while, and will consume a considerable amount of disk space.

Quick cabal method

  1. Install the Haskell platform. This will give you GHC and the cabal-install build tool. Note that pandoc requires GHC >= 7.10 and cabal >= 2.0.

  2. Update your package database:

  3. Check your cabal version with

    If you have a version less than 2.0, install the latest with:

  4. Use cabal to install pandoc and its dependencies:

    This procedure will install the released version of pandoc, which will be downloaded automatically from HackageDB.

    If you want to install a modified or development version of pandoc instead, switch to the source directory and do as above, but without the ‘pandoc’:

  5. Make sure the $CABALDIR/bin directory is in your path. You should now be able to run pandoc:

  6. By default pandoc uses the “i;unicode-casemap” method to sort bibliography entries (RFC 5051). If you would like to use the locale-sensitive unicode collation algorithm instead, specify the icu flag (which affects the dependency citeproc):

    Note that this requires the text-icu library, which in turn depends on the C library icu4c. Installation directions vary by platform. Here is how it might work on macOS with Homebrew:

  7. The pandoc.1 man page will be installed automatically. cabal shows you where it is installed: you may need to set your MANPATH accordingly. If MANUAL.txt has been modified, the man page can be rebuilt: make man/pandoc.1.

Custom cabal method

This is a step-by-step procedure that offers maximal control over the build and installation. Most users should use the quick install, but this information may be of use to packagers. For more details, see the Cabal User’s Guide. These instructions assume that the pandoc source directory is your working directory. You will need cabal version 2.0 or higher.

  1. Install dependencies: in addition to the Haskell platform, you will need a number of additional libraries. You can install them all with

  2. Configure:

    All of the options have sensible defaults that can be overridden as needed.

    FLAGSPEC is a list of Cabal configuration flags, optionally preceded by a - (to force the flag to false), and separated by spaces. Pandoc’s flags include:

    • embed_data_files: embed all data files into the binary (default no). This is helpful if you want to create a relocatable binary.

    • https: enable support for downloading resources over https (using the http-client and http-client-tls libraries).

  3. Build:

  4. Build API documentation:

  5. Copy the files:

    The default destdir is /.

  6. Register pandoc as a GHC package:

    Package managers may want to use the --gen-script option to generate a script that can be run to register the package at install time.

Creating a relocatable binary

It is possible to compile pandoc such that the data files pandoc uses are embedded in the binary. The resulting binary can be run from any directory and is completely self-contained. With cabal, add -fembed_data_files to the cabal configure or cabal install commands.

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With stack, use --flag pandoc:embed_data_files.

Running tests

Pandoc comes with an automated test suite. To run with cabal, cabal test; to run with stack, stack test.

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To run particular tests (pattern-matching on their names), use the -p option:

Or with stack:

It is often helpful to add -j4 (run tests in parallel) and --hide-successes (don’t clutter output with successes) to the test arguments as well.

If you add a new feature to pandoc, please add tests as well, following the pattern of the existing tests. The test suite code is in test/test-pandoc.hs. If you are adding a new reader or writer, it is probably easiest to add some data files to the test directory, and modify test/Tests/Old.hs. Otherwise, it is better to modify the module under the test/Tests hierarchy corresponding to the pandoc module you are changing.

Running benchmarks

To build and run the benchmarks:

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or with stack:

To use a smaller sample size so the benchmarks run faster:

To run just the markdown benchmarks:





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