Contextualizing Clojure in the small and the large

March 24, 2022

One of the best ways to grow at a job is to leave it

I always saw leaving one job for another as an opportunity for new experiences. Recently though, I've been reflecting on how changing jobs is useful for integrating, as well as challenging, prior experiences.

In my tech career so far, job changes have came with a change to both the domain and the tech stack. I've jumped from building offline-first Android apps in international health, to working on Clojure-backed microservices at a bank, to working on JavaScript VMs with an open-source co-op. There was fun to be had with each fresh tech-stack, along with a variety of new constraints that came with each new domain.

Only recently though have I had the opportunity to work in a context similar to that of a previous one.

At my current workplace we use Clojure and Datomic, both of which were go-to tools during my 4 years of building microservices at Nubank. Obvious to some, I've been surprised at how this overlap has proven to be an opportunity to better integrate my prior experiences. In fact, I only now feel able to properly understand and reflect upon key decisions and patterns I encountered at Nubank.

the Nubank times

When I joined Nubank in early 2017, they had a somewhat solidified tech stack that was serving ~1 million clients. We were around 75 engineers and our bread-and-butter tech tools, Clojure, Datomic, Kafka, and S3, were all already in place. Additionally, the microservice architecture, and how we mocked it out for unit and integration testing, had been shown viable.

Interestingly, almost nobody on the team had used these niche tech tools before joining. So, Clojure for me, as well as most others joining, was defined by what was in place when we joined:

As Nubank grew we started hiring and working with people who had in fact used Clojure professionally prior to joining Nubank. Most of them expressed some level of discomfort with our established Clojure ways; a discomfort that I wasn't able to contextualize at the time.

programming in the small using patterns from the large

I figured that when I joined Nextjournal I'd be able to bring a lot of nice ideas from Nubank to the table. I've been surprised to see how, even though both places use Clojure and Datomic, the patterns I learned at Nubank make little sense on a small engineering team.

Through the experience of working at Nextjournal I've started to better understand Nubank's approach to Clojure. How it shines in an organization geared for the madness of the hyper-growth that VC investors demand, and also how it deviates from what many Clojurists are used to.

the REPL

It is kind of crazy that for the first year or two using Clojure I basically didn't know about the REPL. This lead to some wild workflows, like if I wanted to debug a library issue arising in a microservice, I'd checkout and modify the source, install it locally via lein install, and restart my Midje test runner. Test coverage was good and the Midje test runner did code reloading, so the test suite felt like a fine entry point to interacting with our systems.

Even though I was REPL-oblivious, I'm sure other Nubank engineers were into the REPL-driven flow. That said, at Nextjournal I'm noticing that the REPL-driven workflow not only informs the development process, but also the structure of code.

reloadable code

Writing code in a manner that works well with reloading is something we did at Nubank. For instance, sometimes at Nubank we'd reference functions via (var some-function) instead of the standard some-function to ensure that changes to some-function were propagated even when some-function references were bound to intermediates.

Yet, at Nextjournal, it seems to hold much more influence. Clojure protocols, which we used extensively for mocking at Nubank, are avoided given their issues with code reloading. This was also one of the main factors when choosing a software component management library; where integrant was preferred over component due to it being designed around protocols.


To me it has been interesting to see how REPL-driven workflows might shape the general structure of code.

Let's compare for example


The first version is easier to invoke via the REPL because you offload any db connection setup logic to the get-db-conn function. You don't need to worry about building a connection and passing it in. On the flip-side, at the lookup-user-i call-sites you don't have arguments going in, which provides folks reading the code with less context regarding the function's behavior.

I don't know what the correct balance is here but it has been interesting to include REPL interactiveness as a factor when weighing code design.

approach to tests

REPL-driven flows have, to me, sometimes felt at odds with maintaining good test coverage. While Nubank's culture was pretty test-centric, folks I'd chat up from the larger Clojure community sometimes seemed to view tests as secondary.

I can understand how a REPL-centric dev flow can take the place of a more test-driven one, since the REPL is a great tool for a single engineer to validate behavior. Yet given this viability I wonder how often it inhibits one from switching contexts to write tests, which are a critical way of communicating on a team.

Nextjournal is still missing a lot of test harness code, so I've been having some fun seeing if the tools that worked at Nubank are applicable to our small team. For instance, I'm trialing state-flow in one of our projects at Nextjournal. state-flow is a DSL that encourages a relatively constrained way of writing single-service integration tests, which works great when you have hundreds of engineers writing tests. At the same time, I'm not yet sure if the overhead of this DSL makes sense on a small team.

One place where I think tests can work well with REPL-driven dev is in the context of seed data. A colleague of mine that practices REPL-driven dev recently wanted to interact with some logic I'd written and was asking how he could seed the system with useful example data. Someone practicing REPL-driven dev would probably have a REPL session namespace lying around that calls directly into functions in the service to seed the system. But my feature work takes a much more test-driven approach so I had no REPL sessions to share, only my tests. Luckily, through the process of running a test, the system effectively gets seeded with data. Since our integration tests only interact with the service at its HTTP endpoints, the seed data is coherent, complete, and robust to changes to both internal data models and code layout. This to me is an added benefit over a REPL session namespace, which might become stale as code changes.

I wrote a little snippet that runs a test defined with state-flow's defflow and returns the resulting service system for you to interact with in the REPL. defflow is analogous to clojure.test's deftest, but must setup and run using a special initialization and runner logic that manage starting up the service.

I've been using this snippet lately on a consultancy project while developing some operational dashboards in Clerk. It has provided a way to quickly get a service instance into a specific state so I can test my dashboard logic.

library wrapping

At Nubank we wrapped every 3rd-party library in our own API, which meant we:

Looking at it now, it makes perfect sense for a large engineering team but doesn't exactly transfer to smaller teams.

Without much thought I was trying to apply the same approach at Nextjournal and was initially surprised at the pushback. After some time on the team I'm starting to come around to the critiques, which are loosely:

Of course it is all trade-offs, but I'm slowly learning to make these instead of unconsciously applying the approach I've used for years now.

the toolset

One final point of resistance that I've appreciated from my new colleagues, as I've tried to port over my favorite things from Nubank, has been the resistance to increasing the surface area of tooling.

In particular, after a few years of using Kafka, or at least a heavily wrapped and abstracted version of it, everything to me looks like a nail that can be hammered in with it: Too many async operations, use kafka. Need to queue up notifications to be sent, use kafka. And so on.

So I keep bringing up in conversation how great it is and how we should use it for everything. And given the industry's large-scale adoption of Kafka, I kind of expected my colleagues to chat me up about it and maybe give it a try. Instead I've been met with skepticism over whether the complexity of such a tool is actually needed.

My colleagues have taken to asking me "is it possible to solve the problem using tools we already have; like just Datomic and threads?". And so with some sketching we came up with a cute Datomic-backed solution for sending notifications reliably. When I came back to them with a prototype, that covers all the bases, like retry and timeout logic, they in turn challenged me with "we probably don't need retries or timeouts in this context, let's leave out that complexity and add it later if needed".

to conclude

In all, it has been a very interesting and informative shift from slinging code at Nubank to wrangling parens at Nextjournal. The contexts are clearly very different, in terms of both team size and product needs. Yet I've still been surprised at how much I've needed to unlearn or adapt in terms of how I approach Clojure and the style of code I write.

This whole process has helped me better understand Nubank's approach to Clojure. A measured amount of rigidity and uniformity were embraced to allow hundreds of non-Clojurists to join and contribute in a useful way. Additionally, I feel that some patterns and ideas from Java and Ruby were adopted, while eschewing others from the Lisp tradition, to generally further this approachability.