datastreamservicelib 1.14.0

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

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.