I was recently trying to figure out how likely a bunch of end-to-end tests were to be flaky, and wanted to gather some stats about their pass/fail rates on my local machine before including them in a broader test suite.

These tests run for a long time, as they execute extensive scenarios against a live service over HTTP. In this post I’ll share the approach I ended up with using GNU Parallel.

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A quick aside: If you wanna follow along and run the upcoming examples in your own terminal, use this command to generate some test files. They’ll emulate a flaky test by sleeping between 5-15 seconds, then randomly exiting with a failure (exit code 1) or success (exit code 0):

parallel "echo 'sleep \$((\$RANDOM%10+5)) && [ \$((\$RANDOM%2)) = 1 ] \
  && printf PASS \
  || (printf FAIL && exit 1)'" \
  '>' potentially_flaky_{1}.sh ::: {1..5}

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Typically to gather flakiness stats I’d use a couple of nested loops, one for each test I want to run, and another for each attempt. I like doing this kind of stuff in bash for its simplicity/portability:

tests=(
  potentially_flaky_1.sh
  potentially_flaky_2.sh
  potentially_flaky_3.sh
  potentially_flaky_4.sh
  potentially_flaky_5.sh
)

# For each test
for test in "${tests[@]}"; do
  # For each attempt of that test (1 through to 10)
  for attempt in $(seq 1 10); do
    # Capture timestamp for when the test started
    start=$(date "+%s")
    # Run the test and capture its exit code
    bash "$test" > /dev/null
    status=$?
    # Calculate duration of the test run
    end=$(date "+%s")
    duration=$((end - start))

    # Print results
    printf "$test attempt $attempt took ${duration}s "
    if [[ status -eq 0 ]]; then
      printf "PASS\n"
    else
      printf "FAIL\n"
    fi
  done
done

This approach ended up being tediously slow though… since the tests take a while to execute, running them sequentially wasn’t gonna cut it.

I knew about GNU Parallel, but had never used it before. $ man parallel and 15 minutes later, I was “living life in the parallel lane” (as the GNU Parallel book encourages you to do!)

Rewriting the above to work using parallel ended up looking like:

tests=(
  potentially_flaky_1.sh
  potentially_flaky_2.sh
  potentially_flaky_3.sh
  potentially_flaky_4.sh
  potentially_flaky_5.sh
)

parallel --progress --jobs 5 --delay 2 --timeout 3600 --shuf --results out.csv \
  bash {1} ::: ${tests[@]} ::: {1..10}

The joy of finding the right tool for the job can’t be beat - more performance and functionality, with less code!

Let’s go into a bit more detail…

Passing inputs

In GNU Parallel, you specify a command that is to be executed in parallel. In the example provided, the command is bash {1}. The {1} is a placeholder that gets replaced with each input value (if you have more than one input you can use {2}, {3} etc).

The inputs to the command are specified after the ::: operator. In this case, the inputs are the array of test scripts (${tests[@]}) and a sequence of numbers from 1 to 10 ({1..10}). These inputs are provided to the command in all possible combinations.

So in this case, we have 5 test scripts and we want to run each one 10 times, parallel will execute each command 50 times in total.

Controlling concurrency

parallel provides a number of options that can be used to avoid resource contention, here are a few that I found useful for my tests:

• --jobs 5: Caps the number of concurrent jobs to 5 (by default parallel will try to execute as many jobs as you have CPU cores).
• --delay 2: Ensures each job waits for 2 seconds before starting, preventing a thundering herd problem.
• --timeout 3600: Terminates any jobs that have been running for over an hour.
• --shuf: Runs the jobs in a shuffled order.

Capturing output

By default the output of your command will be printed to your terminal, however in this case since I wanted to capture stats - using parallel’s capability to output a CSV file instead was very helpful:

• --results out.csv: Outputs job completion results to the given file which includes duration, exit codes, and captured stdout/stderr.
• --progress prints live progress as the jobs are executing.

The CSV file ends up looking like this (only including the first lines for brevity):

Seq,Host,Starttime,JobRuntime,Send,Receive,Exitval,Signal,Command,V1,V2,Stdout,Stderr
4,:,1692491267.732,6.025,0,4,1,0,"bash potentially_flaky_5.sh",potentially_flaky_5.sh,9,FAIL,
2,:,1692491263.646,12.025,0,4,0,0,"bash potentially_flaky_3.sh",potentially_flaky_3.sh,3,PASS,
1,:,1692491261.604,14.067,0,4,1,0,"bash potentially_flaky_5.sh",potentially_flaky_5.sh,2,FAIL,
5,:,1692491269.779,6.023,0,4,1,0,"bash potentially_flaky_1.sh",potentially_flaky_1.sh,3,FAIL,
3,:,1692491265.686,11.055,0,4,1,0,"bash potentially_flaky_4.sh",potentially_flaky_4.sh,8,FAIL,

It’d be trivial to use this output to aggregate/chart stats.

Exploring further…

This is barely scratching the surface of what parallel can do. I strongly recommend the excellent, free, and funny book by Parallel’s author Ole Tange. The first chapter takes 15 minutes to get through and covers 80% of what you’re likely going to use.

The book covers things such as:

• Distributing jobs across different hosts using SSH, a powerful feature for leveraging multiple machines.
• Monitoring the mean time for job completion and setting timeouts for jobs based on a percentage of the mean, providing more control over long-running tasks.
• Retrying if jobs are known to be failure prone.
• Resuming jobs if parallel execution stops midway, ensuring you don’t lose progress.
• Limiting amount of jobs that can run based on CPU utilization (or other signals).
• Limiting concurrency with a semaphore (and an excellent analogy about toilets).

Happy paralleling!