How fast can Rust make Python? With a bit of data-oriented design , really fast: Details Library Operations Collection Size Time (seconds) Redbear 100000 × 5 10 0.056 Redbear 1000000 × 5 10 0.583 Redbear 10000 × 5 1000 0.015 Redbear 100000 × 5 1000 0.145 NumPy 100000 × 5 10 0.285 NumPy 1000000 × 5 10 2.436 NumPy 10000 × 5 1000 0.039 NumPy 100000 × 5 1000 0.371 When I count ops above, I actually…
I'm a staff engineer working with Python, Clojure, and all the DevOps technologies. Outside of work, I write Rust, contribute to open source, maintain a Kubernetes homelab cluster, and dabble in 3D printing and hardware. In the past, I taught software engineering at Washington University in St. Louis , and worked with web technologies, across many industries, since 2006.
The Python community is abuzz with excitement about better async support. If you have an existing service, you might wonder if you're missing out. Benchmarks show higher throughput and promise the ability to handle more requests with less hardware. Will a switch to async be a free lunch for your existing service? As often happens, the reality differs from expectations. Unless you run in a…
In the last part we covered all the fundamental techniques in writing macros. This enables us to write just about any macro we could think of, but knowing a few tricks can make the process much easier. In this part, we will build on our where clause from the previous article. In the process we will allow the where clause to support multiple comparison operator (instead of just = ) as well as…
In the last part we covered the very basics of macros and implemented a single argument query select. This was a good start, but only scratched the surface of what macros can do. In this part we will implement multi-field select and a basic where clause. That will make our query macro far more useful.
Macros are required to do some very helpful things in Rust, but this isn't an article about any of those things. I became enamored with macros many years ago when I read Practical Common Lisp and saw the implementation of a SQL DSL that works with built in data structures. The whole implementation took less than a screen of code and absolutely blew away my expectations of what it took to…
One of the most misused data structures is the humble map; hashmap, dictionary, hash table, etc. It's a great data structure for quick storage and access of key value pairs. Unfortunately, because of its ease and availability, it becomes pervasive at jobs it has no business doing. The problem grows most visible in dynamically typed languages that make the map a first class citizen (Python,…
I recently took over the maintenance of an open source Clojure project. One of the first things I noticed was the lack CI or CD. These are things that aren't required but can save a lot of work, especially for a public project.
Distrobox is the single piece of software that completely changed the way I work. If you've hear of or used Vagrant and thought it was a great idea but implemented in a really heavy handed manner, then Distrobox is exactly what you've been looking for.
DRY has become a mantra throughout the industry. Any time repetitive code shows up, DRY gets applied as a cure all. If you even start to question DRYing up a piece of code, you are viewed as a heretic to the entire industry.
I recently started a new job that gave me the chance to run whatever Linux distribution would make me the most productive. In the face of complete freedom, I decided to re-evaluate my daily driver and see if there was a distribution that would give me the most up-to-date software while maintaining a stable system. The results of my search surprised even me.
When I started using lisp, I came from the world of modern dependency managers (npm, pip, maven, and the like). So I started searching for an equivalent in lisp. I quickly came across quicklisp . To my surprise, however, quicklisp worked differently than these packages. It works more like a dependency cache than true dependency manager, but that is a topic for another article.
In Part 2 we got a taste of using returns and created our first model methods. In this part, we will build out views that interact with databases and learn how to combine returns containers. This composition is the key to effectively using container types and where we will see the benefits of all our work.
In Part 1 of this series, we looked at how to setup a python project with types, setup our project, and used some basic types. In this part we will start working with the database, implement custom database methods, and look at how to use returns to improve the safety of those methods.
Django is a great framework for web development. Unfortunately, due to the nature of Python and web development in general, it often leads to hard to track down bugs and tangled code. We will take a look at one way to reign this in.
Hy introduces a List that gives you all the power of macros and the elegant Lisp syntax while enabling the full power of Python.
Engineers want to measure the quality and effectiveness of their work. They turn to code coverage, burn down charts, and yearly goals, with the best intention.
A few months ago, Gabriel Gonzalez wrote an excellent article on creating useful tools with Haskell. He used an example of a small CLI tool that aligns the equals signs of a multi-line text input. The article wrapped up by integrating this tool into vim.
Creating hierarchy in a blog is a useful technique. It allows you to have different sections by type of content. It lets users navigate by their interests quickly. It makes your blog organized.
Serverless functions are a great alternative for many light tasks that would traditionally required a server. They allow you to split up work across mutiple small functions, and you only pay for what you use. On top of that, they require less maintenance than managing your own server or Kubernetes cluster.
React and Redux are a top choice for front end projects. React provides fast performance, easy ways to create shared components, and a plethora of libraries. Redux lets you simplify state management in your application, but it has serious drawbacks.
At a summer barbecue, a friend asked if I could build a website for his new restaurant. But what he didn't know was that I haven't built a static site in nearly a decade. However I couldn't let him spend thousands of dollars on a cookie cutter website designed by a firm that knew nothing about his restaurant.
Every line of code you write is a liability. That line of code you just wrote adds complexity to your code base, gives you more nuances to remember, it might even be a bug. If code is so expensive and dangerous. What can you do? After all, you are a developer.
JavaScript dependencies are notoriously hard to mock and test across framework and environments. However, Dependency Injection (DI) ensures testability. It is common in statically typed languages, like Java or Go, but is also useful in dynamic languages like JavaScript.
A good development environment will boost your productivity in any language. A good Clojure development environment will make you 10x more productive. This tutorial is focused on a ClojureScript environment for a Leiningen based project being developed in Emacs .
I really loved lodash. It brough the joy of compact functional code to JavaScript. Especially 6 years ago when I first embraced it. It was a wonderful library. Back then, there was no ES6, no Babel, and terrible browser incompatabilities ran rampant. Lodash along with jQuery were the saving light of that time.
Rails has great capabilities for working CSV files. However, like with many things, the most obvious way is not the most efficient.
Haskell is notoriously difficult to setup, which probably led to many people being scared away from ever getting started. However, there has been a lot of work done to address these short comings. And there is a way to setup a very pleasant environment thanks to the hard work of many projects.