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datastreamservicelib

AsyncIO eventloop helpers and Abstract Base Classes for making services that use ZMQ nice, easy and DRY

Usage

Use the CookieCutter template at https://gitlab.com/advian-oss/python-datastreamserviceapp_template

You can also take a look at src/datastreamservicelib/console.py for some very simple test examples.

Docker

For more controlled deployments and to get rid of “works on my computer” -syndrome, we always make sure our software works under docker.

It’s also a quick way to get started with a standard development environment.

SSH agent forwarding

We need buildkit:

export DOCKER_BUILDKIT=1

And also the exact way for forwarding agent to running instance is different on OSX:

export DOCKER_SSHAGENT="-v /run/host-services/ssh-auth.sock:/run/host-services/ssh-auth.sock -e SSH_AUTH_SOCK=/run/host-services/ssh-auth.sock"

and Linux:

export DOCKER_SSHAGENT="-v $SSH_AUTH_SOCK:$SSH_AUTH_SOCK -e SSH_AUTH_SOCK"

Creating a development container

Build image, create container and start it:

docker build --ssh default --target devel_shell -t datastreamservicelib:devel_shell .
docker create --name datastreamservicelib_devel -v `pwd`":/app" -it -v /tmp:/tmp `echo $DOCKER_SSHAGENT` datastreamservicelib:devel_shell
docker start -i datastreamservicelib_devel

pre-commit considerations

If working in Docker instead of native env you need to run the pre-commit checks in docker too:

docker exec -i datastreamservicelib_devel /bin/bash -c "pre-commit install"
docker exec -i datastreamservicelib_devel /bin/bash -c "pre-commit run --all-files"

You need to have the container running, see above. Or alternatively use the docker run syntax but using the running container is faster:

docker run -it --rm -v `pwd`":/app" datastreamservicelib:devel_shell -c "pre-commit run --all-files"

Test suite

You can use the devel shell to run py.test when doing development, for CI use the “tox” target in the Dockerfile:

docker build --ssh default --target tox -t datastreamservicelib:tox .
docker run -it --rm -v `pwd`":/app" `echo $DOCKER_SSHAGENT` datastreamservicelib:tox

Production docker

There’s a “production” target as well for quick running of testsubscriber and testpublisher

docker build –ssh default –target production -t datastreamservicelib:latest . docker run –rm -it -v /tmp:/tmp datastreamservicelib:latest testpublisher -s ipc:///tmp/test_pub.sock -t foo docker run –rm -it -v /tmp:/tmp datastreamservicelib:latest testsubscriber -s ipc:///tmp/test_pub.sock -t foo

Note that on non-linux platforms the IPC sockets may not as expected between host and container over volume mounts.

Local Development

TLDR:

  • Create and activate a Python 3.9 virtualenv (assuming virtualenvwrapper):

    mkvirtualenv -p `which python3.9` my_virtualenv
  • change to a branch:

    git checkout -b my_branch
  • install Poetry: https://python-poetry.org/docs/#installation

  • Install project deps and pre-commit hooks:

    poetry install
    pre-commit install
    pre-commit run --all-files
  • Ready to go, try the following:

    testpublisher --help
    testsubscriber --help

Use Python 3.9 for development since it’s the lowest supported version so you don’t accidentally use features only available in higher versions and then have to re-do everything when CI tests fail on 3.9.

Remember to activate your virtualenv whenever working on the repo, this is needed because pylint and mypy pre-commit hooks use the “system” python for now (because reasons).

Running “pre-commit run –all-files” and “py.test -v” regularly during development and especially before committing will save you some headache.

Metadata

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