12 Factor Apps
The 12 factor app sets guidelines for designing software for deployment pipelines. They're a great starting point that can be improved upon. See why in this episode.
Hey everyone. Welcome to the next episode of Small Batches. It’s me Adam Hawkins, coming to you from sunny and warm Hawaii. Just like all of us, I’m quarantined inside so there's not much for me to do besides look out my window and ponder the craft of software engineering. As a result, this episode is longer than usual, so nurse that coffee while I serve up a small batch of software engineering theory and best practices. Today’s topic is 12 factor apps.
The 12 factor app describes how to build software for deployment pipelines. These guidelines make software easier to run in development, staging, production, and any future environment. Heroku members wrote “The 12 Factor App” back in 2012. The authors tried to enumerate best practices for running applications on their platform. These guidelines were especially helpful since they coincided with an industry shift towards DevOps, microservices, and continuous delivery.
The 12 factor app guidelines are still relevant today since many software teams are building distributed systems, which require careful attention to detail as to how services are coupled, configured, and deployed. The 12 factor app is a wonderful starting point for best practices. In my experience though they omit a few facets and leave room for antipatterns in certain facets.
My goal with this podcast is to share knowledge and practices for building, deploying, and operating software. The 12 factor app triangulates all three of these areas. This is partially why I love the 12 factor app so much. It’s a rare piece of work that hits on so many themes of this podcast. So, in this episode let’s review the 12 factor app and flag future discussion points.
Codebase, the first factor, defines the relation between code, apps, and deployments. The gist here is that there is a single codebase (i.e. a git repo) equates to a single deployed app (or service). Different versions of a codebase may be deployed to different environments. This implies that if there are multiple codebases, it’s not an app; It’s a distributed system. Each component in a distributed system is also an app, which should also comply with twelve-factor guidelines. Let’s flag this for future episodes since we need to adopt a distributed systems first perspective instead of the other way around.
Dependencies, the second factor, states a twelve-factor app never relies on system-wide packages. Instead, apps must leverage tools like Ruby’s bundler or Python’s virtual env to manage their own dependencies. This is where the second factor ends. Separating out application dependencies is just part of the problem. You must also isolate the app’s runtime. Admittedly this is more relevant for Ruby, Python, or Nodejs apps which rely on managed runtimes. The sticking point is the same for statically complied applications, for example, they might be written in Go. Just to reiterate, the “dependencies” factor focuses on application dependencies rather than application runtimes or execution environments. So where do those come from and what can deployment pipelines expect from dependencies? Let’s flag this for future episodes.
Config, the third factor, states the applications should read configuration from environment variables. Now, this makes it possible to deploy changes to production without altering code, that's good! A litmus test for whether an app has all config correctly factored out of the code is whether the codebase could be made open source at any moment, without compromising any credentials. Separating config and code is a great start but just not enough. Personally, I find the third factor the most wanting. There are great ways to work with config and also horrible anti-patterns, neither are discussed in the third factor. Let’s stick a big flag in this factor for discussion in future episodes.
Backing services, the fourth factor, states applications make no distinction between local and third-party services. Now for example an application’s database and connection to external APIs should be treated in the same way. The idea promotes loose coupling between apps and services. The 12 factor app uses an example of switching a local SMTP service for a third-party service with only config changes and no code changes. Again, this is a good starting point but requires some clarifications and to the config factor and future dev/prod parity factor.
Build, release, run, or the fifth factor, states apps use strict separation between the build, run, and release stages. The build stage converts code into an executable. The release stage combines the executable and config into something runnable. In other words, a “deploy” is the combination of code and config. It’s not possible to change one without creating a new release.
Processes, the sixth factor, states apps execute as one more stateless processes using a shared-nothing architecture. If data needs to persist across restarts or releases, then it must be stored in a stateful backing service such as a database. I like this factor because it surfaces the idea that apps are composed of multiple processes such as a web server, maybe a background job worker, or a cron process. This model scales up to distributed systems which are composed of multiple apps interacting across various processes. This factor also implies the deployment pipeline must handle that releases contain 1-N process which may require different semantics. In other words, the deployment pipeline cannot assume a single process or process type.
Port binding, the seventh factor, states an app is completely self-contained and exposes itself through ports. This seems obvious but it’s a good architectural principle to state outright. Given services are exposed through ports, then it’s possible to configure others by providing the hostname and port of relevant processes.
Processes, the eighth factor, state that processes are first-class citizen. The stateless share nothing model naturally promotes horizontal scaling. Just start more processes. Scale vertically by allocating more resources to processes that needed. The corollary here is processes should never daemonize or write PID files. Instead, they are controlled by process manager like system d. In other words: write your apps that start processes in the foreground; use a process manager to scale, start, and stop processes.
Disposability, the ninth factor, states processes should be ready to start or stop at a moment’s notice. Fast startup times encourage smooth scaling. Conversely, processes must gracefully handle the SIGTERM signal. Severs should stop listening on the relevant port, finish processing any requests, then exit. This approach ensures new releases or other infrastructure events do not impact user-facing requests.
Dev/Prod parity, the tenth factor, states that apps are designed for continuous deployment by minimizing the gap between development and production. The original guideline states: the twelve-factor developer resists the urge to use different backing services between development and production. I think this makes sense when applied to distributed systems databases but has negative implications when applied to distributed systems. Does this mean a development environment for one service in a large distributed system mandates running all other services? Well if you’re targeting dev/prod parity then the answer may be yes. However, answering yes is not always practical. Consider the case where the system in question is a single service. Then it’s simple enough to achieve dev/prod parity. Now scale that up. What about dev/prod parity with N=5, 10, 20, what about a 100? No, the tenth factor offers no advice or guidelines for how to handle inflection points as the system grows or which degrees of dev/prod parity to consider.
Logs, the eleventh factor, state that apps never concern itself with routing or storage of its output stream. Instead, all output should be sent unbuffered to standard out. This works well in development because developers can see output in their terminal. It also works well in production since tools can capture output streams for analytics and storage. Again the eleventh factor is a wonderful starting point but needs to be improved upon for continuous delivery and production operations. The eleventh factor does not cover what or how things should be logged. In fact this only mention of telemetry — a vital facet of continuous deployment largely uncovered by the 12 factor app.
Admin processes, the twelfth factor, state that admin work such as migrating databases or other out-of-band work executes as a separate process. Processes may be started using the same release (meaning the same config and code). I don’t have more to say about this factor so let’s put a pin in it and wrap up.
Future episodes will dive deeper into the individual factors with a focus on identifying and closing the gaps. My biggest gripe with the 12 factor app is around the config and dev/prod factors and the overall omission of anything telemetry metrics-related.
I’m curious about your experience with 12 factor apps. How has it worked for you? What do you think it’s missing? Where could be improved? Please send your comments to firstname.lastname@example.org. Also, feel free to tell me what you want to be covered in future episodes. You know, so I have something to do, while we are quarantined.
Well, with that, I leave you to it. Good luck out there and happy shipping.
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