Coyote Testing Tool

All about Coyote, the open source tool that automates testing of our data streaming technologies. For more, visit Coyote’s Github repository

Marios Andreopoulos
Aug 20, 2016
Coyote Testing Tool

A few days ago we open source’d Coyote, a tool we created in order to automate testing of our data streaming technologies.

Coyote does one simple thing: it takes a .yml file with a list of commands to setup, run and check their exit code and/or output. It has some other functionality too, but its essence is this. The source code is short, I don’t expect any praise for it; coyote is a tool, a useful one.

We use it for environment and operations testing, as well as runtime meta-testing.

Environment and operations testing is to verify that an environment is set up and working as expected, such as having access to a specific port or software, or running a command and getting certain results, like compiling a program that requires tools and libraries present and set-up or a command that needs some environment variables set. Performance testing can also be seen as a subset of environment and operations testing.

Runtime meta-testing is a shortcut to proper software-run-tests. If you trust your software to be robust enough, then instead of verifying the actual results (e.g entries in a database), you can scan the output of the software for (un)expected logs. Of course this kind of tests should not be used without awareness of the underlying dangers.


Coyote has two important outputs; (a) an HTML report with the commands ran, their exit status, stdout, stderr and some statistics, (b) its exit code which up to 254 indicates the number of errors occurred and at 255 is like saturation arithmetic and means that 255 or more errors occurred. The HTML report is for humans and the exit code is for machines. We use it with Jenkins CI where, amongst other things, we need a quick visibility of failures and verbose output for debugging.

lenses coyote tests


Test configuration is set via a YAML file, partially inspired by Ansible. Let’s see a real world example before we delve into specifics. Below are two of our Box tests, a basic HDFS/Hadoop test and a Spark test. I’ve add some comments to explain what happens for the non-intuitive parts.

In practice you write your tests once or a few times and run them many times. The person running the tests and/or evaluating the results may be not the one that wrote them. Even the creator after a couple months can forget the purpose of each command. In we also use the coyote configuration files as reference examples for both newcomers and ourselves.

Currently coyote supports this settings for each command:

  • workdir: run the command in this directory

  • stdin: pass this as standard input to the command

  • nolog: do not count, nor log this command, usually we use it for small cleanup


  • env: array of environment variables to make available to the command

  • timeout: if the command hasn’t completed in this time, kill it. It takes

    Go time duration strings, such as “30s”,”2m15s”,”1h”. There is a global

    default timeout of 5 minutes, which you can override globally and/or per command

  • stdout_has, stdout_not_has, stderr_has, stderr_not_has:

    arrays with regular expressions to check against standard output and standard


  • ignore_exit_code: do not fail the test if exit code is not zero, it still may

    fail from a timeout or a stdout/stderr check

  • skip: if set to true, skip this test (or the group of tests if “skip” is at the

    group level), useful for automating test runs by using sed to select tests

Also it supports some special strings:

  • %UNIQUE%: if this string is used inside a command, stdin, or env variable,

    it will be replaced by a unique numeric string at runtime

  • %UNIQUE_[0-9A-Za-z_-]+%: if such a string (e.g: %UNIQUE_VAR1%, %UNIQUE_1-B%)

    is used, all its instances inside commands, stdin and env variables will be

    replaced by a common unique numeric string at runtime

Some features you may miss are:

  • loop constructs (such as Ansible’s “with”)

  • global variables / templating

  • abort if a test fails —this is a design choice for now, we always run all


Jenkins Integration

Most of the time it is Jenkins that runs our tests.

lenses jenkins ci

We’ve set it to use Coyote’s exit code not only to mark the build as failed, but to add the number of errors to the job name, to grant us quick visibility of the current status. We also keep a history of test reports.

job history

Enabling or disabling tests is easy, add a boolean variable for each test group and then a single bash line to set its skip flag to true:

select tests lenses

Final Words

Although code testing is such a renowned practice, our research didn’t reveal many testing tools for the latter part of the DevOps pipeline, such as the deployment and execution phase. Coyote’s turnaround has been great until now and this is the reason we wanted to share it.

Of course, as it is with every such effort, we are bound to re-invent the wheel for some part. The codebase is kept small and Go makes it easy to add features as we go.

For the time being we don’t have a release plan, just grab the latest coyote commit from master, it works:

go get

To use it:

coyote -c configuration.yml -out test.html

To make changes to the source code:

Should we make any breaking changes to the configuration format, we will explore how to communicate about it and protect Coyote’s current users.

If you are interested in examples or extending the code, visit Coyote’s Github repository.

Thank you for your time, Marios.

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