I'm thinking about how to decide whether it's better to encapsulate my work behind well-named function names, or to expose it - which will help developers understand what's going on more quickly? Is there a name for the study of this sort of problem?

Specifically, if I'm running a bunch of bash commands ultimately, but I have significantly complex logic around those commands, at what point does it make sense to write this in a high-level language like Python, even though this obfuscates the actual bash commands being run?

Detailed problem

Currently I'm trying to write a Jenkins build script for my project with roughly the following steps:

  • Pull my code from github
  • Compile sass files into CSS
  • Pull down a sub-folder from a different github project
  • Zip up the project
  • Upload it to an object store with a unique ID

I'm thinking about how to write this to be as easy for future developers as possible (this code is never going to be seen by end users). These developers are likely, but not definitely, going to be fairly good at Python. They will definitely have a passing familiarity with the command-line, but are likely to be unfamiliar with more complex bash scripting.

The first iteration of this build script was just a list of sequential commands, something like:

git clone git@github.com:username/project.git
git clone git@github.com:username/sub-project.git project/sub-project
sass --update project/css
tar -czf project.tgz project
swift upload my-container project.tgz --object-name=project-`sha1sum project.tgz`.tgz

However, this set of commands quickly became more complex as I started to do things like only clone the git project if it wasn't already there, otherwise update it - to speed up the build. Before I knew it I had 50 lines and a fair few conditionals.

So the first thing I did was encapsulate these into bash functions, e.g. update_git_dir, so my build script looks more like this:

#!/usr/bin/env bash

source helper_functions.sh

update_git_dir project git@github.com:username/project.git
build_sass project/css
create_archive project project.tgz
upload_to_swift project.tgz

This is one level of encapsulation. Now the developer, who would have understood the git clone etc. commands directly, can't actually see what's going on. They have to look in helper_functions.sh.

However, as time went on I realised that many of my helper functions now consisted of more conditional statements, variable assignments and function calls than actual commands. These conditional statements can be quite opaque to someone not familiar with bash scripting:

function create_archive {

    # Get revision ids
    dependencies_requirements_revision=$(cat ${project_name}/sub-project/requirements-revision.txt)

    requirements_dir=$(dirname ${requirements_context})
    if [ "${requirements_dir}" != "${project_name}" ]; then
    latest_revision=$(git-revision-hash ${project_name})


So I started migrating my code into Python. So now my build script looks like this:

#!/usr/bin/env python

from builders import GitProjectBuilder

builder = GitProjectBuilder(

# Compress and upload
upload_location = builder.upload_archive_to_swift(archive='archive.tgz')
print upload_location

Now, when you look in builders.py, it's much easier to understand the logic - if statements and function calls are much more readable - but now we're even further away from the real shell commands. In my python code the closest I get to directly running shell commands looks like this:

def build_archive(self, archive):
    print subprocess.check_output(
            'tar --exclude-vcs --create --file '
            '{archive_filename}.tar {project_dir}'

If the developer needs to work out exactly which commands are being run, it's now much more difficult.

Wrap up

So how do I decide which is the best architecture to maximise transparency while encapsulating complexity?

This problem seems similar to when I'm working with dependency injection where the more dependencies I inject rather than encapsulate, the more complex my initialisation code gets - and I have a similar problem drawing the line.

Is there a name for this field of study?

  • Reading The Dark Side of Encapsulation let me to think that what I should focus on is which parts are actively useful to encapsulate - rather than encapsulating for readability's sake, I should in this case think about doing it for the sake of function. If my encapsulated methods and objects could be usefully reused purely in the abstract, that's good. If on the other hand people would actually want to reuse the underlying bash commands, I should expose them instead. Sep 4, 2015 at 15:18
  • Searching the Interwebs for "python build tool" gives you a lot of options. Pick a commonly used tool in the language your team uses and use that. PyBuilder might be a good one: pybuilder.github.io Sep 4, 2015 at 17:50
  • Thanks it @GregBurghardt is it always useful to discover new python projects. However the "python build tool" search term is extremely generic, and I can't see that pybuilder solves any of the problems I'm struggling with here. I may be able to force my Python scripts into its structure, but I think it would be significantly more work. There may be a tool out there that I haven't found that would help (xonsh is the best suggestion so far), but in general I'd rather you trust that I already know how to use Google, and try to answer the question I actually asked rather than one that I didn't. Sep 5, 2015 at 9:38
  • I have a build system built out of a large number of bash scripts. Each has a fair number of conditionals dealing with things like determining if the kernel should be rebuilt. It works as long as things are mostly straight through as bash is very difficult to debug. The best approach is to have a set of scripts that each make sense on their own that are called by a larger automation script. But as soon as you start to have any complexity in your build automation I would move to an actual language such as Python while keeping the individual functions the developers need in bash. Sep 5, 2015 at 14:52

4 Answers 4


I'd give xonsh a go, it's a clever mix of shell and python.

xonsh is a Python-ish, BASHwards-compatible shell language and command prompt. The language is a superset of Python 3.4 with additional shell primitives that you are used to from BASH and IPython. xonsh is meant for the daily use of experts and novices alike.

Take advantage of Python(3)'s abstraction and package system, coupled with nice conditionals, but write what needs to be in shell as just shell.


#!/usr/bin/env xonsh

def exists(filename):
    return filename in $(ls)

if exists(".git"):
    git checkout master
    git pull
    git clone $GITURL

Note that only a little bit of ugliness $() is required to in-line shell inside of python, and it Just Works (TM) if you are splitting things clearly by line (eg the if statement lines)

Lots more detail (including embedding python in shell lines with @() ) in the tutorial http://xonsh.org/tutorial.html

You can use it as your system shell. But just because you can doesn't mean you should :-)


I will not provide with an immediate yes/no answer, but some thoughts on the situation.

Build scripts since many people depend on them should be the most easy to understand area of your code. I would argue that a long "boring" bash should not be an issue as long as it is easily understood. I'd add a hint towards the "configure && build && build install" of the C in various unices.

Your bash script seems to be doing variable initialization and defaults assignment, none of those would get into a deep if-then-else structure.

An estimation of bash scripts size (although it is surely debatable) is that if it is more than 100 lines of code than it might need to be written into a "proper" program. Although the previous sentence is a matter of opinion.

If you decide not to go through the bash route, then you need to get into build tools who are made just for this purpose. Ant / Maven / Gradle in the Java world and many others for different platforms. I can kind of see your example as a series of build targets for some tools I've used in the past, as a sequence of Rake tasks or bazel ones.

I assume that if you go that route then it should be one in the most used language of the project if possible (easier to maintain).

I do not know if there is a name for the field, but someone doing this on day to day basis is called "integration engineer" in some companies.

  • Well we already use Make in the project, but those sort of build tools, where the build scripts are embedded in the project, lend themselves to projects that need to be built in the same way in many different places. Gulp is for developers to pull down a Javascript project and get it running locally, Make is classically for compiling binaries from source code on the client's machine. Conversely, this is where I want to build many different project in a similar way in one place - Jenkins. So it doesn't really belong in the project, but rather needs its own build project. Sep 4, 2015 at 14:43
  • +1 thanks for the suggestion that long sequential bash isn't necessarily a problem. I can see, that, especially if it's commented well. I'll have a play with it. I'll wait a bit before accepting anything to see if anyone elaborates on whether there's a name for this field of study though. Sep 4, 2015 at 14:50
  • Update: Integration engineering as field suggestion. Sep 4, 2015 at 15:06

If I were in your position, I would use http://paver.github.io/paver/ It's basically Rake for python.

  • Interesting. I didn't know about Paver. Do you think it's better than Fabric? I'll look into it, see if it might be helpful in the future. However, as I mentioned to dimitris, it's not a solution for this problem. Instead of storing a bunch of build tasks inside my project, to be run by clients using the project, I'm trying to create a generic build system in one place - on my jenkins server - that can be applied to many different similarly structured projects. For this, Paver and Fabric will be no more useful than a custom python script. Sep 4, 2015 at 20:53
  • Paver is basically for building projects, running your unit tests etc. Fabric shines when you're performing tasks over remote notes.
    – Bon Ami
    Sep 4, 2015 at 21:08
  • 1
    I wonder if you've looked at Ansible. It comes with a concept of modules - which encapsulates a lot of the functionality in your snippets above e.g. creating tarballs, uploading deployment units, copying stuff from A to B etc. You might want to look at it for inspiration on how to define your abstractions.
    – Bon Ami
    Sep 4, 2015 at 21:13
  • Yeah I've used ansible a little bit. I particularly dislike the way it overloads YAML, but other than that it does seem quite powerful. I'll give it another look see if I can get any good ideas. Thanks for the suggestion. Sep 4, 2015 at 21:17

Rather than fret about how much abstraction is too much for a build/integration tool, instead opt for a tool commonly used by Python developers to do this sort of thing. You'll get far more use out of a well known, but highly abstracted build tool than you will from the clearest written, simplest build tool that does the same thing.

What is more important to understand: Knowing the gritty details about how a gzip file is created in Bash, or that these files are gzipped together and all manners of chaos and panic will occur if it is omitted?

I would recommend searching for Python build tools. Ask the python community what they recommend and choose a open source project. It probably has more features than you've built, and it probably has been tested more completely.

  • That's all very good general advice. I completely agree with using existing solutions, and building on existing open source work (and contributing back). However, there's nothing Python specific about creating a tarball of a project and uploading it somewhere, or the other things I'm trying to do here (build sass, pull in another github project). And I really don't think it's generic enough to have a generic tool for doing it, beyond the tools I'm using (tar, swift etc.). If you think otherwise, could you please suggest some examples? Sep 4, 2015 at 20:46

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