After a recent reorganization at work, I became the technical lead on a project to rewrite an existing PHP application that consisted of tens of thousands of lines of code. My team of five decided that Python and Flask would be a good implementation choice as it would allow us to see results quickly. The group ranged from complete Python novices to Python journeymen.

They quickly became rockstars.

How did this happen? How was it possible that, in less than a month from the project's inception, these python beginners were writing beautiful, idiomatic code? That code reviews were simple, straightforward, and usually took almost no time? The answer may surprise you.

Enabling Your Developers

Continuous integration with deep integration with Python testing and static analysis tools, along with simple scripts to aid developers has been the key to the amazing productivity of the team. It may sound silly, and I certainly didn't expect this outcome, but I can't deny what I see: novice programmers writing expert code.

Why did I put so much emphasis on the build process and developer tools? Before I answer that question, a bit of background on the old system is in order.

The Legacy System

The legacy system was so difficult to develop against that even small changes took three times as long as they should have. Testing was a nightmare. Simply trying to run the unit tests was an exercise in futility; most required a connection to a sandbox MySQL instance that was shared among 20 developers and was never in the delicate state required for testing. Other tests were out of date and no longer matched the database schema, failing immediately.

There was no build process or continuous integration for the old project. Even if the tests worked perfectly, no one would run them. We would only be finding bugs after the buggy code had already been committed. And we had no sense of how comprehensive the tests were; code coverage reporting was non-existent.

The codebase was also horribly confusing. There was layer upon layer of abstraction and indirection, followed by giant functions spanning hundreds of lines with no documentation. The lack of coding conventions meant that each new file I opened in vim was written in a completely different style than the previous.

"Whatever they did, do the opposite"

I told myself that the guiding principle in the design and development of the new system would be "the new system is as straightforward, quick, and easy to develop against as the old system is frustrating." I know a lot of best practices when it comes to starting a large Python project (many of which are detailed in my post Open Sourcing a Python Program the Right Way). And so I set out to do exactly the opposite of everything the previous project did.

The Setup

Now for the meat of the article: the specific steps I took that inadvertently turned novices into rockstars. I'll list a number of areas I focused on and what I did in that space.

Design For Simplicity

First and foremost, I spent a good deal of time thinking the design of the system. My goal was to write the "scaffolding" code and then let my team's other developers add functionality. As such, I strived for a design that would make adding new functionality as straight-forward and error-free as possible.

My implementation of the scaffolding (or "skeleton") contained more than a simple class structure and set of interactions. It included a number of convenience functions and mechanisms to automate tasks that would be repeated often.

For example, service endpoints are written as classes. Ones that are POSTed to take their arguments as JSON data. Checking for the existence of required fields and sending returning an error if not present, therefore, would be a common task. For that reason, I included a mechanism that allowed the developer to simply list the required and optional JSON fields in the class implementation and they would automagically be extracted and added as attributes to the class. That meant that one could write:

1 2 3 4 5 6 7 8 9 10 11 12 class MyEndpoint ( BaseEndpoint ): """Endpoint class implementing the '/foo' service endpoint.""" __required_fields__ = [ 'date' , 'time' , 'event' ] __optional_fields__ = [ 'location' ] @extract_fields def post ( self , request ): if self . location : do_something ( self . date , self . time , self . event , self . location ) else : do_something_else ( self . date , self . time , self . event )

rather than:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 class MyEndpoint ( BaseEndpoint ): """Endpoint class implementing the '/foo' service endpoint.""" def post ( self , request ): data = request . get_json ( force = True , silent = True ) if 'date' not in data : return InvalidUsage ( 'date' is a required field if 'time' not in data : return InvalidUsage ( 'time' is a required field if 'event' not in data : return InvalidUsage ( 'event' is a required field date = data [ 'date' ] time = data [ 'time' ] event = data [ 'event' ] location = None if 'location' in data : location = data [ 'location' ] do_something ( self . date , self . time , self . event , self . location ) else : do_something_else ( self . date , self . time , self . event )

This type of convenience is important, as it allows developers to focus on what's important rather than forcing them to perform mundane bookkeeping tasks. It also prevents errors, as the developer can no longer accidentally forget to check for an optional field or take the wrong action if a field isn't present.

In short, reducing boilerplate should be a focus during the design phase. A design that, purposely or not, requires excess boilerplate code to add new features is, in my mind, a poor one. It frustrates developers, and frustrated developers don't write the quality code they're capable of producing.

Create a virtualenv

In terms of the actual project set up, first (as you would expect) I created a virtualenv and requirements.txt file, pegging our package requirements to specific versions. This allowed new developers to immediately get up and running via mkvirtualenv <project_name> -r requirements.txt . It also ensured that whatever packages an individual developer had on their machine didn't interfere with the packages required for the new system.

Run unit tests on each commit via Jenkins

AppNexus uses Jenkins for continuous integration. I immediately set up py.test and wrote some quick unit tests against the scaffolding code I had written. Most importantly, I included a number of tests that mocked out the database connection and checked to ensure the queries we expected to run actually ran. These tests would serve as examples to other developers required to test database interaction (a somewhat tricky subject).

I also installed pytest-cov , which gives py.test code coverage capabilities. I integrated this with Jenkins by using the --junitxml flag, producing test results in junit-style XML. If the coverage drops below a certain percent, the build fails, plain and simple.

Using make to simplify everything

Next I created a Makefile to automatically create and activate the virtualenv , run the tests, and also clean the environment by deleting the virtualenv . Now, make test installed the required packages and ran the unit tests straight from a fresh git clone . As a developer, that's a nice convenience. Making the process to run your automated tests as simple as possible is very important. If it's too difficult or takes to long, developers won't run them.

pylint and pep8 , CI style

After writing a quick coding conventions document, I installed pylint and pep8. For pylint , I generated a .pylintrc file (using the awesome --generate-rcfile flag. Seriously, why don't more tools have this?) to hold project specific settings. I setup pylint to run with the --rcfile=.pylintrc flag and followed a similar process for pep8 . Then I promptly added them to the Makefile to run during the tests and produce output that Jenkins could use to create reports.

I now had a project where unit tests were run on every commit and test results, test coverage, coding conventions, and "bad code" reports were generated. These reports are saved, and Jenkins produces graphs that track these metrics over time. More importantly, they impacted whether or not the build itself actually succeeded.

I set up Jenkins to fail the build if the number of pylint and pep8 violations passed some threshold. This was an important step, as it made it clear that writing idiomatic, properly formatted code was something to be taken seriously. More importantly, it took the burden of remembering to use the tools off of the developer. If a developer "forgets" to run pylint or pep8 before committing, the build process has their back.

Documentation

Needless to say, documentation was a focus for the new system. I set up a Sphinx build to automatically generate documentation for the project (using sphinx-apidoc ) and added it to the Makefile as a new target. I also enabled documentation coverage. The coding conventions mandated docstrings for all modules, classes, and functions. Sphinx (and pylint ) now enforce this automatically and fail the build if coverage isn't 100%.

Developer-friendly Scripts

Perhaps most important of all, I spent a good deal of time on an oft-neglected topic: writing scripts to make my developers' lives easier. For this project, I created the following:

A schema file and script that created the database from scratch

A dump script to fill the database with test data The script first cleaned up the database to make sure it was in a known, easily recreatable state

A script that chose sensible default configuration values and started the server, giving the developer the option to run against a real MySQL database or an SQLite in-memory database. It also took care of sending stdout and stderr to a log file (in addition to the syslog logging the system performs.

and to a log file (in addition to the logging the system performs. A script aware of the pre-populated database data that curled a request with JSON data to the server, then checked the database to make sure the expected changes were present.

A script called should_i_commit_this.sh. It runs pylint and pep8 with the project-specific configuration and determines if the code receives poor scores from either. If it does, the script says not to commit the code, gives the score assigned by the tool that complained, and prints that tool's output.

Especially with the last script, my goal was to make it as easy as possible to answer the question, "Did I just write good code?". Starting up the database and server, sending a test request, and performing static analysis on the code are each one command away. Another way of looking at it would be to say I tried to make it as difficult as possible to write (and commit) bad code.

The Results

With all of these tools and conveniences in place, my team's developers took the reigns. Within a week, each team member had written the code for a non-trivial endpoint. The code they produced was truly impressive. They made excellent use of the utility code I had written, wrote extensive unit tests, documented everything, and had code that precisely followed PEP-8. One of the most telling signs we had succeeded was the fact that, after looking over the code, a member of another team thought it was all written by a single person. "The style is identical," they said (after hearing five people worked on it).

The clearest indicator of success, though, has been code reviews. All of my career, code reviews have been tedious wastes of time. Reviewers always focused on style rather than substance. Now, code reviews are a source of interest rather than frustration. We never have to say, "Please add a space after the colon on line 14." Reviews are focused on the logic and soundness of the approach rather than nitpicking style issues.

Looking Back

So there you have it. My secret for making your team of Python developers produce great code, regardless of skill level: focus on catching as much as possible in your build process and afford developers convenience through scripts, Makefiles, and easy-to-create/use development environments.

Got any other suggestions for supercharging a team of Python developers? Let me know in the comments, on Twitter (@jeffknupp), or via email (jeff@jeffknupp.com).