The Commandments of Good Code according to Zach™

July 15th, 2015 Comments Off

    1. Treat your code the way you want others’ code to treat you
    2. All (ok most) programming languages are simultaneously good and bad
    3. Good code is easily read and understood, in part and in whole
    4. Good code has a well thought out layout & architecture to make managing state obvious
    5. Good code doesn’t reinvent the wheel, it stands on the shoulders of giants
    6. Don’t cross the streams!

 

Treat your code the way you want others’ code to treat you

I’m far from the first person to write that the primary audience for your code is not the compiler/computer, but whomever next has to read the code (which could be you 6 months from now!) Any engineer can produce code that ‘works’, what distinguishes crap from those that are capable of writing maintainable code efficiently that supports a business long term is an understanding of design patterns, and the experience to know how to solve problems simply and in a clear and maintainable way. The rest of these commandments are all supporting lemmas of this thesis.

 

All (ok most) programming languages are simultaneously good and bad

In (almost) any programming language it is possible to write good code or bad code ergo, assuming we judge a programming language by how easy it is to write good code (it should at least be one of the top criteria, anyway), nearly any programming language can be “good” or “bad” depending on how it is used (or abused).

An example of a language that by many is considered ‘clean’ and readable is Python. Many organizations will enforce a universal coding standard (i.e. PEP8), the language itself enforces some level of white space discipline and the built in APIs are plentiful and fairly consistent. That said, it’s possible to create unspeakable monsters. For example, one can define a class and define/redefine/undefine any and every method on that class at runtime. This naturally leads to at best an inconsistent API and at worse an impossible to debug monster. Yes, one might naively think, but nobody does that! Unfortunately that is untrue, and it doesn’t take long browsing pypi before you run into substantial (and popular!) libraries that (ab)use monkeypatching extensively as the core of their APIs. I recently used a networking library (xmppy) whose entire API changes depending on the network state of an object. Imagine calling “client.connect()” and getting a MethodDoesNotExist error instead of HostNotFound or NetworkUnavailable.

As an example of a language that by many is considered ‘dirty’ but can be quite pleasant is perl. Now, I know the previous sentence will cause a lot of controversy, so rather than fend off the pitchforks myself, I’ll refer you to Dave Cross, a proper perl expert who very eloquently discusses this very topic.

 

Good code is easily read and understood, in part and in whole

Good code is easily read and understood, in part and in whole, by others as well as the author in the future (Trying to avoid the “Did I really write that?” syndrome). By in part I mean that if I open up some module or function in the code I should be able to understand what it does without having to also read the entire rest of the codebase. Code that constantly references minute details that affect behavior from other (seemingly irrelevant) portions of the codebase is like reading a book where you have to reference the footnotes or an appendix at the end of every sentence. You’d never get through the first page! Some other thoughts on “local” readability:

    • Well encapsulated code tends to be more readable, separating concerns at every level.
    • Names matter. Activate system 2 and put some actual thought into names, the few extra seconds will pay dividens,
    • Cleverness is the enemy. When using fancy syntaxes such as list comprehensions or ternary operators be careful to use them in a way that makes your code more readable, not just shorter.
    • Consistency in style, both in terms of how you place braces but also in terms of operations improves readability greatly.
    • Separation of concerns. A given project manages an innumerable number of locally important assumptions at various points in the codebase. Expose each part of the codebase to as few of those concerns as possible. Say you had some kind of people management system where a person object may sometimes have a null last name. To somebody writing code in a page that displays person objects, that could be really awkward! And unless you maintain a handbook of “Awkward and non obvious assumptions our codebase has” (I know I don’t) your display page programmer is not going to know last names can be null and is probably going to write code with a null pointer exception in it when the last name-being null case shows up. Instead handle these cases with well thought out APIs that different pieces of your codebase use to interact with each other.

 

Good code has a well thought out layout & architecture to make managing state obvious

Sublemma: State is the enemy. It is the single most complex part of any application and needs to be dealt with very intentionally. Common problems include database inconsistencies, partial UI updates where new data isn’t reflected everywhere, out of order operations, or just mind numbingly complex code with if statements and branches everywhere leading to difficult to read and even harder to maintain code. Putting state on a pedestal and being extremely consistent and deliberate in how state is accessed and modified dramatically simplifies your codebase. Some languages, Haskell for example, enforce this at a programmatic level. You’d be amazed how much the clarity if your codebase can improve if you have libraries of pure functions that access no external state, and then a small surface area of stateful code which references the outside pure functionality.

 

Good code doesn’t reinvent the wheel, it stands on the shoulders of giants

In 2015 we have a wealth of tools available to solve common problems — depend on them as much as possible so you can focus on solving the interesting part of your application. Think ahead of time of somebody has solved some portion of the problem you’re trying to solve. Is there something on the interwebs/github (with an appropriate license) that you can reuse? Yes, expect to make heavy modifications, but more often than not this is a time saver. Things you should not be reinventing in 2015 in your project (unless these ARE your project)

Databases.

I don’t care what your requirements are, it exists. Figure out which of CAP you need for your project, then chose the database with the right properties. Database doesn’t just mean relational databases (e.g. MySQL) anymore, you can chose from any one of a huge number of data storage models:

    • Key Value Stores e.g. Redis, Memcache
    • “No-SQL” e.g. MongoDB, Cassandra/
    • Hosted DBs: AWS RDS / DynamoDB / AppEngine Datastore
    • Map Reduce engines: Amazon EMR / Hadoop (Hive/Pig) / Google Big Query
    • Even less traditional: Erlang’s Mnesia, iOS’s Core Data

Data abstraction layers

You should, in most circumstances, not be writing raw queries to whatever database you happen to chose to use. There exists a library to sit in between the DB and your application code, separating the concerns of managing concurrent database sessions and details of the schema from your main code. At the very least you should never have raw queries or SQL inline in the middle of your application code, please wrap it in a function and centralize all the functions in a file called “queries.py” or something else equally obvious. A line like users = load_users() is infinitely easier to read than users = db.query(“SELECT username, foo, bar from users LIMIT 10 ORDER BY ID”) etc. Centralization also makes it much easier to have consistent style in your queries, and limits the amount of places to go to change the queries should the schema change.

Other

    • Between S3, EBS, HDFS, Dropbox, Google Drive, etc. you really shouldn’t spend much effort or mental energy on storage now a days.
    • Take your pick of queing services provider — ZeroMQ/RabbitMQ/Amazon SQS

 

Don’t cross the streams!

There are many good models for programming design, pub/sub, actors, MVC etc. Choose whichever you like best, and stick to it. Different kinds of logic dealing with different kinds of data should be physically isolated in the codebase (again, this separation of concerns concept and reducing cognitive load on the future-reader). The code which updates your UI should be physically distinct from the code that calculates what goes into the UI.

 

Conclusion

This is by no means an exhaustive or the perfect list of Good Coding Commandments. That said, if every codebase I ever had to pickup in the future followed even half of the concepts in this list I will have many fewer gray hairs and add an extra 5 years on the end of my life and the world would be a better place.

Credit to Scott Kyle (appden) for assistance reviewing the material in this post. Have you gotten Current For Mac yet?