We are trying to find the best way to approach that problem.
Say I work in a Python environment, with pip & setuptools.
I work in a normal git flow, or so I hope.
So:
Move to feature branch in some app, make changes.
Move to feature branch in a dependent lib - Develop thing.
Point the app, using "-e git+ssh" to the feature branch of the dependent lib.
Create a Pull Request.
When this is all done, I want to merge stuff to master, but I can't without making yet another final change to have the app (step 3 above) requirements.txt now point to the main branch of the feature.
Is there any good workflow for "micro services" or multiple dependent source codes in python that we are missing?
Python application workflow from development to deployment
It looks like you are in search for developing Python application, using git.
Following description is applicable to any kind of Python based application,
not only to Pyramid based web ones.
Requirements
Situation:
developing Python based solution using Pyramid web framework
there are multiple python packages, participating in final solution, packages might be dependent.
some packages come from public pypi, others might be private ones
source code controlled by git
Expectation:
proposed working style shall allow:
pull requests
shall work for situations, where packages are dependent
make sure, deployments are repeatable
Proposed solution
Concepts:
even the Pyramid application released as versioned package
for private pypi use devpi-server incl. volatile and release indexes.
for package creation, use pbr
use tox for package unit testing
test, before you release new package version
test, before you deploy
keep deployment configuration separate form application package
Pyramid web app as a package
Pyramid allows creation of applications in form of Python package. In
fact, whole initial tutorial (containing 21 stages) is using exactly this
approach.
Despite the fact, you can run the application in develop mode, you do not have
to do so in production. Running from released package is easy.
Pyramid uses nice .ini configuration files. Keep development.ini in the
package repository, as it is integral part for development.
On the other hand, make sure, production .ini files are not present as they
should not mix with application and belong to deployment stuff.
To make deployment easier, add into your package a command, which prints to
stdout typical deployment configuration. Name the script e.g. myapp_gen_ini.
Write unittests and configure tox.ini to run them.
Keep deployment stuff separate from application
Mixing application code with deployment configurations will make problem at
the moment, you will have to install to second instance (as you are likely to
change at least one line of your configuration).
In deployment repository:
keep here requirements.txt, which lists the application package and other
packages needed for production. Be sure you specify exact package version at
least for your application package.
keep here production.ini file. If you have more deployments, use one branch per deployment.
put here tox.ini
tox.ini shall have following content:
[tox]
envlist = py27
# use py34 or others, if your prefer
[testenv]
commands =
deps =
-rrequirements.txt
Expected use of deployment respository is:
clone it to the server
run tox, this will create virtualenv .tox/py27
activate the virtualenv by $ source .tox/py27/bin/activate
if production.ini does not exist in the repo yet, run command
$ myapp_gen_ini > production.ini to generate template for production
configuration
edit the production.ini as needed.
test, it works.
commit the production.ini changes to the repository
do other stuff needed to deploy the app (configure web server, supervisord etc.)
For setup.py use pbr package
To make package creation simpler, and to keep package versioning related to git
repository tags, use pbr. You will end up with setup.py being only 3 lines
long and all relevant stuff will be specified in setup.cfg in form of ini
file.
Before you build the first time, you have to have some files in git repository,
otherwise it will complain. As you use git, this shall be no problem.
To assign new package version, set $ git tag -a 0.2.0 and build it. This will
create the package with version 0.2.0.
As a bonus, it will create AUTHORS and ChangeLog based on your commit
messages. Keep these files in .gitignore and use them to create AUTHORS.rst
and ChangeLog.rst manually (based on autogenerated content).
When you push your commits to another git repository, do not forget to push the tags too.
Use devpi-server as private pypi
devpi-server is excellent private pypi, which will bring you following advantages:
having private pypi at all
cached public pypi packages
faster builds of virtual environments (as it will install from cached packages)
being able to use pip even without having internet connectivity
pushing between various types of package indexes: one for development
(published version can change here), one for deployment (released version will not change here).
simple unit test run for anyone having access to it, and it will even collect
the results and make them visible via web page.
For described workflow it will contribute as repository of python packages, which can be deployed.
Command to use will be:
$ devpi upload to upload developed package to the server
$ devpi test <package_name> to download, install, run unit test,
publish test results to devpi-server and clean up temporary installation.
$ devpi push ... to push released package to proper index on devpi-server or even on public pypi.
Note, that all the time it is easy to have pip command configured to consume
packages from selected index on devpi server for $ pip install <package>.
devpi-server is also ready for use in continuous integration testing.
How git fits into this workflow
Described workflow is not bound to particular style of using git.
On the other hand, git can play it's role in following situations:
commit: commit message will be part of autogenerated ChangeLog
tag: defines versions (recognized by setup.py based on pbr).
As git is distributed, having multiple repositories, branches etc.,
devpi-server allows similar distribution as each user can have it's own
working index to publish to. Anyway, finally there will be one git repository
with master branch to use. In devpi-server will be also one agreed
production index.
Summary
Described process is not simple, but the complexity is relevant to complexity of the task.
It is based on tools:
tox
devpi-server
pbr (Python package)
git
Proposed solution allows:
managing python packages incl. release management
unit testing and continuous integration testing
any style of using git
deployment and development having clearly defined scopes and interactions.
Your question assumes multiple repositories. Proposed solution allows decoupling multiple repositories by means of well managed package versions, published to devpi-server.
We ended up using git dependencies and not devpi.
I think when git is used, there is no need to add another package repository as long as pip can use this.
The core issue, where the branch code (because of a second level dependency) is different from the one merged to master is not solved yet, instead we work around that by working to remove that second level dependency.
Related
I'm investigating how Python applications can also use a CI pipeline, but I'm not sure how to create the standard work-flow.
Jenkins is used to do the initial repository clone, and then initiates tox. Basically this is where maven, and/or msbuild, would get dependency packages and build.... which tox does via pip, so all good here.
But now for the confusing part, the last part of the pipeline is creating and uploading packages. Devs would likely upload created packages to a local pip repository, BUT then also possibly create a deployment package. In this case it would need to be an RPM containing a virtualenv of the application. I have made one manually using rpmvenev, but regardless of how its made, how what such a step be added to a tox config? In the case if rpmvenv, it creates its own virtualenv, a self contained command so to speak.
I like going with the Unix philosophy for this problem. Have a tool that does one thing incredibly well, then compose other tools together. Tox is purpose built to run your tests in a bunch of different python environments so using it to then build a deb / rpm / etc for you I feel is a bit of a misuse of that tool. It's probably easier to use tox just to run all your tests then depending on the results have another step in your pipeline deal with building a package for what was just tested.
Jenkins 2.x which is fairly recent at the time of this writing seems to be much better about building pipelines. BuildBot is going through a decent amount of development and already makes it fairly easy to build a good pipeline for this as well.
What we've done at my work is
Buildbot in AWS which receives push notifications from Github on PR's
That kicks off a docker container that pulls in the current code and runs Tox (py.test, flake8, as well as protractor and jasmine tests)
If the tox step comes back clean, kick off a different docker container to build a deb package
Push that deb package up to S3 and let Salt deal with telling those machines to update
That deb package is also just available as a build artifact, similar to what Jenkins 1.x would do. Once we're ready to go to staging, we just take that package and promote it to the staging debian repo manually. Ditto for rolling it to prod.
Tools I've found useful for all this:
Buildbot because it's in Python thus easier for us to work on but Jenkins would work just as well. Regardless, this is the controller for the entire pipeline
Docker because each build should be completely isolated from every other build
Tox the glorious test runner to handle all those details
fpm builds the package. RPM, DEB, tar.gz, whatever. Very configurable and easy to script.
Aptly makes it easy to manage debian repositories and in particular push them up to S3.
I am quite a Django n00b, and figured using Aldryn for my first real django site would be a good idea!
I have successfully installed and implementer Aldryn News & Blog.
Now I would like to install Aldryn Search that is not accessible from the "Manage Addons" under the Aldryn control panel.
I very confused on how to install an addon like Aldryn Search that is not accessible from within "Manage Addons". Should I somehow use the "Add custom Addon" and register the package as a new custom addon.
Or should I create a local development environment and somehow install the addon and upload it? (does it exist a tutorial for this?)
Thank you!
There are various ways in which to install arbitrary Django packages into an Aldryn project.
The quick, easy way
The easiest, quickest way is simply to place the module(s) you need into the project directory, thus placing them on the Python path. You need then to make sure that your settings.py, urls.py and so on are appropriately configured. Then you can push these changes to Aldryn itself. This is described in Adding a new application to your Aldryn project - the quick and easy way.
The create-an-Addon way
A more involved way to do it, that has benefits for long-term use and re-use, is to turn the package into a private or public Aldryn Addon. This is described in Developing an Addon application for Aldryn.
A middle way
Another way is somewhere between the two. Add the package to the project's requirements.in - you can do this in various ways, for example:
# standard install from PyPI
some-package==1.2.3
# install from an archive
https://example.com/some-package1.2.3.tar.gz#egg=some-package==1.2.3
# install from a GitHub repository
git+http://git#github.com/some-org/some-package.git#egg=some-package==1.2.3
You will need to make sure that your settings.py, urls.py and so on are appropriately configured.
Run aldryn project update. This in effect redeploys your project locally, except for:
docker-compose run --rm web python manage.py migrate - you need to run any migrations manually, unlike on Aldryn.
Finally, git add, commit and push your changes to your project, and redeploy it on Aldryn.
This method isn't yet documented in the Aldryn support system, but will be soon.
That's a very valid question in my opinion since add-ons are wrapped into an additional directory that makes the django app inside invisible to Django's INSTALLED_APPS.
If you add them to addons-dev they are ignored by git.
A possible solution (even if maybe not the cleanest) would be to unignore addons-dev by adding !/addons-dev to the .gitignore in the project's root directory and then add -e /app/addons-dev/aldryn-package-name to requirements.in (outside the section generated/overwritten by Aldryn). That's what aldryn project develop aldryn-package-name does (for the local environment).
Similarly, if you have a git repository that contains the code (like aldryn-search) you would use -e git+https://github.com/aldryn/aldryn-search.git in requirements.in
In case you need to apply changes to the addon code best practise would be forking the original repository and then checking out your fork as per above instructions.
I have django website in testing server and i am confused with how should the deployement procedure goes.
Locally i have these folders
code
virtualenv
static
static/app/bower_components
node_modules
Current on git i only have code folder in there.
My Initial thought was to do this on production server
git clone repo
pip install
npm install
bower install
colectstatic
But i had this problem that sometimes some components in pip or npm or bowel fail to install and then production deployemnet fails.
I was thinking of put everything in static, bower, npm etc inside git so that i can fetch all in prodcution.
Is that the right way to do. i want to know the right way to tackle that problem
But i had this problem that sometimes some components in pip or npm or
bowel fail to install and then production deployment fails.
There is no solution for this other than to find out why things are failing in production (or a way around would be to not install anything in production, just copy stuff over).
I would caution against the second option because Python virtual environments are not designed to be portable. If you have components such as PIL/Pillow or database drivers, these need system libraries to be installed and compiled against at build time.
Here is what I would recommend, which is in-line with the deployment section in the documentation:
Create an updated requirements file (pip freeze > requirements.txt)
Run collectstatic on your testing environment.
Move the static directory to your frontend/proxy machine, and map it to STATIC_URL. Confirm this works by browsing the static URL (for example: http://example.com/static/images/logo.png)
Clone/copy your codebase to the production server.
Create a blank virtual environment.
Install dependencies with pip install -r requirements.txt
Make sure you run through the deployment checklist, which includes security tips and settings you need to enable for production.
After this point, you can bring up your django server using your favorite method.
There are many, many guides on deploying django and many are customized for particular environments (for example, AWS automation, Heroku deployment tips, Digital Ocean, etc.) You can browse those for ideas (I usually pick out any automation tips) but be careful adopting one strategy without making sure it works with your particular environment/requirements.
In addition this might be helpful for some guidelines on deployment.
I'm a long-time Django developer and have just started using Ansible, after using Vagrant for the last 18 months. Historically I've created a single VM for development of all my projects, and symlinked the reusable Django apps (Python packages) I create, to the site-packages directory.
I've got a working dev box for my latest Django project, but I can't really make changes to my own reusable apps without having to copy those changes back to a Git repo. Here's my ideal scenario:
I checkout all the packages I need to develop as Git submodules within the site I'm working on
I have some way (symlinking or a better method) to tell Ansible to setup the box and install my packages from these Git submodules
I run vagrant up or vagrant provision
It reads requirements.txt and installs the remaining packages (things like South, Pillow, etc), but it skips my set of tools because it knows they're already installed
I hope that makes sense. Basically, imagine I'm developing Django. How do I tell Vagrant (via Ansible I assume) to find my local copy of Django, rather than the one from PyPi?
Currently the only way I can think of doing this is creating individual symlinks for each of those packages I'm developing, but I'm sure there's a more sensible model.
Thanks!
You should probably think of it slightly differently. You create a Vagrant file which specifies Ansible as a provisioner. In that Vagrant file you also specify what playbook to use for your vagrant provision portion.
If your playbooks are written in an idempotent way, running them multiple times will skip steps that already match the desired state.
You should also think about what your desired end-state of a VM should look like and write playbooks to accomplish that. Unless I'm misunderstanding something, all your playbook actions should be happening inside of VM, not directly on your local machine.
My goal is to be able to deploy a Django application to one of two environments (DEV or PROD) based on the Git branch that was committed and pushed to a repository. This repository is hosted on the same server as the Django applications are being run on.
Right now, I have two virtualenvs set up. One for each environment. They are identical. I envision them only changing if the requirements.txt is modified in my repository.
I've seen tutorials around the internet that offer deployments via git by hosting the repository directly in the location where the application will be deployed. This doesn't work for my architecture. I'm using RhodeCode to host/manage the repository. I'd like to be able to use a post-receive (or other if it's more appropriate) hook to trigger the update to the appropriate environment.
Something similar to this answer will allow me to narrow down which environment I want to focus on.
When I put source activate command in an external script (ie. my hook), the script stops at that command. The virtualenv is started appropriately, but any further actions in the script (ie. pip install -r requirements.txt or ./manage.py migrate) aren't executed.
My question, is how can I have that hook run the associated virtualenv? Or, if it is already running, update it appropriately with the new requirements.txt, South migrations, and application code?
Is this work flow overly complicated? Theoretically, it should be as simple as git push to the appropriate branch.