The power of Pathom

Most people don’t know the power of Pathom. Most people that I know of think that Pathom is about graphs.

They are wrong.

I mean, yes, in the end you’ll have a graph, with dependencies, but that’s not the point. The point of Pathom is the ability to transform your code into a soup or attributes. It’s also probably the best usage of qualified keywords for me, if not the only one that justifies the downsides (I’ll not enter in details here – just know that having to convert from qualified keywords to unqualified multiple times is not fun at all).

The name “soup of attributes” may not be a beautiful one, but believe me – it’s incredible. The idea is quite simple – instead of trying to handle all possible conversions from multiple sources to multiple destinations, you just define which attributes can be computed in terms of others, and Pathom does the rest. As always, things are better with examples, so let’s go.

I had to work on a system that somehow had strange rules – it needed to generate a bunch of text files for different companies. Each company expected a different file name and different fields. To generate the file, we had to accumulate data that came from a payload, from an external system that we called via REST API, and also from some data we had on our database. To make things worse, some companies would expect some of the data that was returned from REST on the filename, and there were also some state changes – like, if a file was already processed, the company would send us a return file, and we had to read some content of this file, move this file to another directory, renaming the file in the process.

nREPL on… Chlorine?

When I started the Chlorine project, I just thought it would be great if I could target all Clojure-like REPLs that already exist but didn’t have tooling support. At the time, this would include Lumo and Plank, mostly. Also, Shadow-CLJS and Figwheel have some “clunky run-some-code-and-transform-in-cljs” way of working that simply didn’t click with me.

Now, almost a year later, Chlorine supports Clojure, ClojureScript (Shadow-CLJS, Lumo, Plank, or even over clj), ClojureCLR, Arcadia, Babashka, Clojerl (Clojure on Erlang) and Joker (Clojure on Go, also a linter). But the reality is that working with a pure Socket REPL is really hard – a socket REPL works exactly like a regular one, printing namespaces after each code, and so on. Also, there are some strange decisions on some REPLs, mostly likely ClojureScript (that is the second most used Clojure flavor), so things are not always easy. To put things in perspective, currently Chlorine uses 3 ways to evaluate code: It uses unrepl, that only works on Clojure, or uses internal APIs of shadow-cljs (that obviously only works for shadow-cljs), and for other implementations it uses a kind of a hack – it evaluates the code, inside a trycatch, and it returns a vector where the first element is a symbol in a specific format that Chlorine will understand and then link that with the response. This “hacky way” is currently being used for every other implementation except Clojure and Shadow-CLJS. Things work (autocomplete works too), but it is not pretty and sometimes have strange results.

As a matter of fact, I was already thinking about removing UNREPL (it’s really hard to implement new features on it, and some good ideas only work in theory – for example, the ability to evaluate long strings / collections and render only a part at a time aren’t that good with lots of edge-cases) and, to do it, I though about a better, non-hacky way to evaluate things on some Socket-REPLs (that, again, would only work on some REPLs – ClojureScript REPLs will probably never support “upgradable REPLs” because of the way they work) – the only thing that I had to understand is how to implement this “upgraded REPL”…

Then, recently, Babashka added an initial support for nREPL, with an insane low amount of lines. So, I’ve tried to implement a way to evaluate code over nREPL… and it was really simple to do it, using a npm library that already did it. But implementing like this meant that the user would need to know if the host/port to connect is a Socket REPL, or a nREPL (and the user does not know – lots of tools like lein and shadow-cljs show an nREPL port to be connected).

REPL-Tooling Clients

Chlorine, Clover and Clematis are all implementations of the same library: REPL-Tooling. In this post I will show you how to create a new implementation of it in a way that’s completely disconnected from any editors, so you can grasp the general concepts.

Suppose I want to do an implementation for an editor that doesn’t run JavaScript – so it’ll connect by some kind of socket. In this example I’m going to use WebSockets because… why not?

We’re going to create a shadow-cljs node project and add repl-tooling as a dependency. We will also had some more dependencies: mostly ws for websockets and the same react libraries that we use for reagent (react, create-react-class and react-dom) – repl-tooling still needs reagent, and probably in the future I will split it into two different libraries (one for the REPL handling and other for the visual rendering part). This supposedly is not to much of a problem because ClojureScript compiler will probably remove these parts in the dead code elimination process anyway. So, our package.json file will just be like this:

  "name": "ws-repl",
  "devDependencies": {
    "shadow-cljs": "^2.8.83"
  "dependencies": {
    "create-react-class": "^15.6.3",
    "install": "^0.13.0",
    "react": "^16.12.0",
    "react-dom": "^16.12.0",
    "ws": "^7.2.1"

And our shadow-cljs.edn file:

{:source-paths ["src"]

 :dependencies [[repl-tooling "0.4.0"]]
 :builds {:node {:output-to "index.js"
                 :target :node-script
                 :main ws-repl.core/main}}}

The first step is when someone connects to the WebSocket. Then, we’ll just create a connection to the client, and send a list of supported commands – for now, is just the “connect” command:

Ubiquitous interface – how to integrate things in Clojure

Inspired by this thread on Reddit, I decided to write a little bit about my experience integrating things in Clojure.

The first thing to understand is that Clojure have an ubiquitous interface: EDN. And it is important to understand what this means. In the beginning, I made this mistake of “Death By Specificity” on my now abandoned Relational project: to abstract things that don’t need to be abstracted.

But can we do even better? How about we de-abstract (concretize? Is this a real word?) things that are already abstracted?

Strange decisions in Clojure – keyword inheritance

First, a disclaimer: the opinions on these posts are my own, and they reflect (for me) a design decision on the language that I don’t understand, specially considering other decisions that seems to contradict it. I also want to say that Clojure (and ClojureScript) is my favorite language, the one that I enjoy writing on my free time and professionally, so by no means this is a rant on the whole language!

Well, this is a new “series” on this blog: what is on the Clojure language that I don’t like, that I feel is out-of-place, and sometimes I can’t understand? In this first post, “keyword inheritance”. And what is that?

Clojure allows us to use derive to generate a “parent-child” inheritance against keywords. So, for example:

(isa? ::dog ::animal) ; => false
(derive ::dog ::animal)
(isa? ::dog ::animal) ; => true

This will change the way multimethods work too: so, for example, if derive is used and a multimethod expects an ::animal and you send a ::dog, it’ll use the implementation for ::animal:

(defmulti cry :type)
(defmethod cry ::animal [_] "Some animal crying")

(cry {:type ::wolf})
; Execution error (IllegalArgumentException) at user/eval152 (REPL:1).
; No method in multimethod 'cry' for dispatch value: :user/wolf
(cry {:type ::dog})
; => "Some animal crying"


Introduction to Kafka with Clojure

Recently I was trying to study Kafka, but I didn’t find a single resource that would give me a quick introduction and hands-on experience with it and Clojure. So, I’m making my own here! Don’t expect a “too deep introduction” – this is just the quick-and-dirty introduction about the concepts, and then I’ll show some code examples in Clojure

Kafka is a messaging system similar to RabbitMQ and SQS. The great differential is that it’s faster than both solutions, and works very well in cluster mode. Installing Kafka locally is quite complicated so you probably will wants to use the docker-compose.yaml file below:

version: '2'
    image: confluentinc/cp-zookeeper:5.3.1
    hostname: zookeeper
    container_name: zookeeper
      - "2181:2181"
    image: confluentinc/cp-enterprise-kafka:5.3.1
    hostname: broker
    container_name: broker
      - zookeeper
      - "9092:9092"
      KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181'
      KAFKA_METRIC_REPORTERS: io.confluent.metrics.reporter.ConfluentMetricsReporter

This file will create the Kafka broker (like a single node of the messaging) and will add Zookeeper (that will allow you to coordinate between different Kafkas, decide which node is the leader, and also participate on the node election when the leader goes down, and other things). You will connect into 9092 port, and then listen and send messages from there.

Subtyping on functional languages

This is almost a new post on the Function SOLID series. It should be about the Liskov Substitution Princible, but before we talk about it it’s important to understand the concept of subtyping.

Subtyping in Dynamic languages like Clojure or in languages that do not have hierarchical typing like Haskell seems strange. But subtyping is not only a concept about object-oriented programming languages – it’s about restrictions, and the concept of variance. I’m going to try to explain both in this post

To begin with, we can say that a supertype is more generic than a subtype, but that’s not all. In truth, is all about properties that you can prove about a specific data. For example, if we use Clojure as a starting language you could say that the coll? is a property of maps and vectors, but vector? is a property that only applies to vectors. In practice, this means that coll? is a superclass of vector?.

(coll? []) ; => true
(coll? {}) ; => true

(vector? []) ; => true
(vector? {}) ; => false


(check (my-code) => (needs :tests))

So, yesterday I made a talk (in Portuguese only, unfortunately) about the difficulties of testing Clojure and ClojureScript code. Specifically, I think the most problematic issue is the lack of “custom matcher libraries”, and how the default error messages are kinda bad and don’t help you identify the problems.

Then, on Clojurians’ #announcements Slack channel, I found that clojure.test Expectations library have a new version. So, why not integrate it on my Check library, and maybe continue developing it?

What is check?

Midje is too magic. Clojure.test is too little. Thinking about findind a “middle ground” I’ve started the “check” project, and I’m using it to test my personal projects like Chlorine, Clover, REPL-Tooling and Paprika. The problem is that, while the API is stable, but it still doesn’t do all the things I want.

Why I tend to avoid core.async?

It’s no surprise that I don’t like core.async very much. For starters, it make my functional composition looks like imperative programming again. There’s also multiple issues that you need to be aware of (like, don’t use async/put! because you will have problems, deadlocks that are difficult to predict, go blocks don’t compose over functions so you loose lots of helper macros like delay).

But the most important reason is that most of the time, I’m working in ClojureScript. And it’s impossible to migrate callback to core.async.

Well, you may be tempted to write something like:

(js/someFunction "i'm async" "lol" #(async/put! some-channel %))

And one day or another you’ll have the dreadful Assert failed: No more than 1024 pending puts are allowed error. There are multiple ways around this problem, but none of then work if you can’t lose messages.