Motivation

I will start from the very basics of dependency management and will go very slow, so if you feel bored, just scroll to the next section.

Suppose you have a python project with following direct dependencies:

click
pip-tools

(Yes I took pip-compile-multi as an example). Let’s save them as-is in requirements/base.in. Those are unpinned libraries. It means that whenever developer runs

pip install -r requirements/base.in

they will get some version of these libraries. And the chances are that if several developers do the same over some period, some will have different dependency versions than others. Also, if the project is online service, one day it may stop working after redeployment because some of the dependencies had backward incompatible release. These backward incompatible changes are relatively common.

To avoid this problem, Python developers are hard-pinning (aka locking) their dependencies. So instead of a list of libraries, they have something like:

click==6.7
pip-tools==1.11.0

(To keep things neat let’s put this into requirements/base.txt) That’s good for a starter. But there are two significant drawbacks:

  1. Developers have to do non-trivial operations if they want to keep up with newer versions (that have bug fixes and performance improvements).

  2. Indirect dependencies (that is dependencies of dependencies) may still have backward-incompatible releases, that break everything.

Let’s put aside point 1 and fight point 2. Let’s do

pip freeze > requirements/base.txt

Now we have full hierarchy of dependencies hard-pinned:

click==6.7
first==2.0.1
pip-tools==1.11.0
six==1.11.0

That’s great, and solves the main problem - service will be deployed exactly 1 the same every single time and all developers will have same environments.

1

That’s not true. Someone could re-upload broken package under existing version on PyPI. For 100% reproducible builds use hashes.

This case is so common that there already are some tools to solve it. Two worth mentioning are:

  1. Pip Tools - a mature package that is enhanced by pip-compile-multi.

  2. PipEnv - a fresh approach that is going to become the “official” Python way of locking dependencies some day.

But what if the project uses some packages that are not required by the service itself? For example pytest, that is needed to run unit tests, but should never be deployed to a production site. Or flake8 - syntax checking tool. If they are installed in the current virtual environment, they will get into pip freeze output. That’s no good. And removing them manually from requirements/base.txt is not an option. But still, these packages must be pinned to ensure, that tests are running the same way on all development machines (and build server).

So let’s get hands dirty and put all the testing stuff into requirements/test.in:

-r base.in

prospector
pylint
flake8
mock
six

Note, how I put -r base.in in the beginning, so that test dependencies are installed along with the base.

Now installation command is

pip install -e requirements/test.in

For one single time (exceptionally to show how unacceptable is this task) let’s manually compose requirements/test.txt. After installation, run freeze to bring the whole list of all locked packages:

$ pip freeze
astroid==1.6.0
click==6.7
dodgy==0.1.9
first==2.0.1
flake8==3.5.0
flake8-polyfill==1.0.2
isort==4.2.15
lazy-object-proxy==1.3.1
mccabe==0.6.1
mock==2.0.0
pbr==3.1.1
pep8-naming==0.5.0
pip-tools==1.11.0
prospector==0.12.7
pycodestyle==2.0.0
pydocstyle==2.1.1
pyflakes==1.6.0
pylint==1.8.1
pylint-celery==0.3
pylint-common==0.2.5
pylint-django==0.7.2
pylint-flask==0.5
pylint-plugin-utils==0.2.6
PyYAML==3.12
requirements-detector==0.5.2
setoptconf==0.2.0
six==1.11.0
snowballstemmer==1.2.1
wrapt==1.10.11

Wow! That’s quite a list! But we remember what goes into base.txt:

  1. click

  2. first

  3. pip-tools

  4. six

Good, everything else can be put into requirements/test.txt. But wait, six is included in test.in and is missing in test.txt. That feels wrong. Ah, it’s because we’ve moved six to the base.txt. It’s good that we didn’t forget, that it should be in base. We might forget next time though.

Why don’t we automate it? That’s what pip-compile-multi is for.

Managing dependency versions in multiple environments

Let’s rehearse. Example service has two groups of dependencies (or, as I call them, environments):

$ cat requirements/base.in
click
pip-tools

$ cat requirements/test.in
-r base.in
prospector
pylint
flake8
mock
six

To make automation even more appealing, let’s add one more environment. I’ll call it local - things that are needed during development, but are not required by tests, or service itself.

$ cat requirements/local.in
-r test.in
tox

Now we want to put all base dependencies along with all their recursive dependencies in base.txt, all recursive test dependencies except for base into test.txt, and all recursive local dependencies except for base and test into local.txt.

$ pip-compile-multi
Locking requirements/base.in to requirements/base.txt. References: []
Locking requirements/test.in to requirements/test.txt. References: ['base']
Locking requirements/local.in to requirements/local.txt. References: ['base', 'test']

Yes, that’s right. All the tedious dependency versions management job done with a single command that doesn’t even have options.

Now you can run git diff to review the changes and git commit to save them. To install the new set of versions run:

pip install -Ur requirements/local.txt

It’s a perfect time to run all the tests and make sure, that updates were backward compatible enough for your needs. More often than I’d like in big projects, it’s not so. Let’s say the new version of pylint dropped support of old Python version, that you still need to support. Than you open test.in and soft-pin it with descriptive comment:

$ cat requirements/test.in
-r base.in
prospector
pylint<1.8  # Newer versions dropped support for Python 2.4
flake8
mock
six

I know, this example is made up. But you get the idea. That re-run pip-compile-multi to compile new test.txt and check new set.

Benefits

I want to summarise, why pip-compile-multi might be a good addition to your project. Some of the benefits are achievable with other methods, but I want to be general:

  1. Production will not suddenly break after redeployment because of backward incompatible dependency release.

  2. Every development machine will have the same package versions.

  3. Service still uses most recent versions of packages. And fresh means best here.

  4. Dependencies are upgraded when the time is suitable for the service, not whenever they are released.

  5. Different environments are separated into different files.

  6. *.in files are small and manageable because they store only direct dependencies.

  7. *.txt files are exhaustive and precise (but you don’t need to edit them).