Docker containers are great and can be scaled out in Kubernetes, Nomad or other orchestration engines. Some may instead run several services on the same environment, on the same machine if their workloads are smaller or more consistent. Remember to gather your output and monitor your instances or clusters.
For real workloads: Go for a Dockerized environment if possible – async task queues are usually nice and services could scale up and down for keeping up with incoming demand; if you require network access like HTTP from users or API clients directly to the service, then it's usually preferred to put some kind of ingress (nginx, haproxy or other type of load balancer) to proxy requests to the service pods. Let the ingress then handle public TLS, http2 / http3, client facing keep-alives and WebSocket protocol upgrades and let the service instead take care of the business logic.
There are a few examples in the
https://github.com/kalaspuff/tomodachi/blob/master/examplesfolder, including using
tomodachiin an example Docker environment (with or without docker-compose).
There are examples to publish events / messages to an AWS SNS topic and subscribe to an AWS SQS queue. There's also a similar code available of how to work with pub/sub for RabbitMQ via the AMQP transport protocol.
tomodachiis a perfect place to start when experimenting with your architecture or trying out a concept for a new service. It may not have all the features you desire and it may never do, but I believe it's great for bootstrapping microservices in async Python.
Sweet! Please send me a PR with your ideas. There's now automatic tests that are running as GitHub actions to verify linting and regressions. Get started by cloning the repo and install the dev dependencies.
- Contribution guide: https://github.com/kalaspuff/tomodachi/blob/master/CONTRIBUTING.rst
There are some projects and organizations that already are running services based on
tomodachiin production. The library is provided as is with an unregular release schedule, and as with most software, there will be unfortunate bugs or crashes. Consider this currently as beta software (with an ambition to be stable enough for production). Would be great to hear about other use-cases in the wild!
Another good idea is to drop in Sentry or other exception debugging solutions. These are great to catch errors if something wouldn't work as expected in the internal routing or if your service code raises unhandled exceptions.
My name is Carl Oscar Aaro and I'm a coder from Sweden.
When I started writing the first few lines of this library back in 2016, my intention was just to learn more about Python's
asyncio, the event loop, event sourcing and message queues. A lot has happened since – now running services in both production and development clusters, while also using microservices for quick proof of concepts and experimentation. 🎉
Updated about 2 years ago