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Maelstrom is a fast Rust, Go, and Python test runner that runs every test in its own container. Tests are either run locally or distributed to a clustered job runner.

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Maelstrom is a suite of tools for running tests in isolated micro-containers locally on your machine or distributed across arbitrarily large clusters. Maelstrom currently has test runners for Rust, Go, and Python, with more on the way. You might use Maelstrom to run your tests because:

  • It's easy. Maelstrom provides drop-in replacements for cargo test, go test, and pytest. In most cases, it just works with your existing tests with minimal configuration.
  • It's reliable. Maelstrom runs every test isolated in its own lightweight container, eliminating confusing errors caused by inter-test or implicit test-environment dependencies.
  • It's scalable. Maelstrom can be run as a cluster. You can add more worker machines to linearly increase test throughput.
  • It's clean. Maelstrom has built a rootless container implementation (not relying on Docker or RunC) from scratch, in Rust, optimized to be low-overhead and start quickly.
  • It's fast. In most cases, Maelstrom is faster than cargo test or go test, even without using clustering. Maelstrom’s test-per-process model is inherently slower than pytest’s shared-process model, but Maelstrom provides test isolation at a low performance cost.

While our focus thus far has been on running tests, Maelstrom's underlying job execution system is general-purpose. We provide a command line utility to run arbitrary commands, as well a gRPC-based API and Rust bindings for programmatic access and control.

The project is currently Linux-only (x86 and ARM), as it relies on namespaces to implement containers.

See the book for more information.

Provide Feedback

We want to learn how you want to use Maelstrom! Your feedback helps us prioritize features, expand the languages we support, and improve the overall experience for developers like you. Share your insight with this quick survey.

Getting Started

Installing Pre-Built Binaries

To run your tests using Maelstrom, you need a test runner binary. The easiest way to get it is using cargo-binstall:

For Rust tests:

cargo binstall cargo-maelstrom

For Go tests:

cargo binstall maelstrom-go-test

For Python tests:

cargo binstall maelstrom-pytest

This will install a pre-built binary from the github releases page.

If you don't have cargo-binstall, you can download the binaries manually.

Check out the book for more ways to get Maelstrom.

Running cargo-maelstrom

To run your Rust tests, use cargo-maelstrom:

cargo maelstrom

This runs in "standalone" mode, meaning all tests are run locally. Each test is run in its own container, configured with a few common dependencies. It may work for your project without any further configuration.

If some tests fail, however, it likely means those tests have dependencies on their execution environment that aren't packaged in their containers. You can remedy this by adding directives to the cargo-maelstrom.toml file. To do this, run:

cargo maelstrom --init

Then edit the created cargo-maelstrom.toml file as described in the book.

Running maelstrom-go-test

To run your Go tests, use maelstrom-go-test:

maelstrom-go-test

This runs in "standalone" mode, meaning all tests are run locally. Each test is run in its own container, configured with a few common dependencies. It may work for your project without any further configuration.

If some tests fail, however, it likely means those tests have dependencies on their execution environment that aren't packaged in their containers. You can remedy this by adding directives to the maelstrom-go-test.toml file. To do this, run:

maelstrom-go-test --init

Then edit the created maelstrom-go-test.toml file as described in the book.

Running maelstrom-pytest

Before running tests, we need to do a little setup.

Choosing a Python Image

First generate a maelstrom-pytest.toml file

maelstrom-pytest --init

Then update the image in the file to have the version of Python you desire.

[[directives]]
image = "docker://python:3.11-slim"

The default configuration and our example uses an image from Docker

Including Your Project Python Files

So that your tests can be run from the container, your project's python must be included. Update the added_layers in the file to make sure it includes your project's Python.

added_layers = [ { glob = "**.py" } ]

This example just adds all files with a .py extension. You may also need to include .pyi files or other files.

Including pip Packages

If you have an image named "python", maelstrom-pytest will automatically include pip packages for you as part of the container. It expects to read these packages from a test-requirements.txt file in your project directory. This needs to at a minimum include the pytest package

test-requirements.txt

pytest==8.1.1

Now we are ready to try to run tests. Just invoke maelstrom-pytest:

maelstrom-pytest

This runs in "standalone" mode, meaning all tests are run locally. Each test is run in its own container.

Running Tests

Once you have finished the configuration, you only need invoke maelstrom-pytest to run all the tests in your project. It must be run from an environment where pytest is in the Python path. If you are using virtualenv for your project make sure to source that first.

Setting Up a Cluster

To get even more out of Maelstrom, you can set up a cluster to run your tests on. You will need to run one copy of the broker (maelstrom-broker) somewhere, and one copy of the worker (maelstrom-worker) on each node of the cluster.

You can install these using multiple methods, including cargo-binstall:

cargo binstall maelstrom-worker maelstrom-broker

Then you can start the broker:

maelstrom-broker --port=1234

Then a few workers:

maelstrom-worker --broker=broker-host:1234

And then run cargo-maelstrom or maelstrom-pytest against the cluster:

cargo maelstrom --broker=broker-host:1234
maelstrom-pytest --broker=broker-host:1234

Learn More

Find our complete documentation in the book.

Licensing

This project is available under the terms of either the Apache 2.0 license or the MIT license.