Plurrrr

Sharding was one of the first ways databases were distributed to improve performance. Recent innovations have made it one of the best.

Recent sudo releases added new features that allow you to watch and control previously hidden problem areas.

One of the problems we had was the readability of logs. When you know what you’re looking for in a log, it is easy to search for an exception. But when you don’t know what is wrong, going through gigabytes of logs in white text on a black background can be very time consuming and error-prone.

Essentially, solution is to add colors to our logs to facilitate the readability. Wouldn’t it be great if every level (i.e., info, warning, error) message had a different color? What if we could have different colors in our stack trace to highlight different exceptions?

The async and await keywords have been in JavaScript for some time now, with full support across all major browsers and Node. While a lot has been written on how to use them, they are still a mystery to many fresh JavaScript and Node developers.

This article aims to explain what async and await do and how they work, in simple terms, while not treating them as black magic.

This will be yet another exploratory series on my blog — this time around, I want to write a little about optimizing performance in Swift code. This is, of course, an endless topic so what I’m going to do is, similarly to previous posts, focus on a problem and then track my way through working on it.

Traffic proxying, the act of encapsulating one flow of data inside another, is a valuable privacy tool for establishing boundaries on the Internet.

I’ve, once again, given this device to a host of professionals on The Verge’s team, and the reactions I saw couldn’t be more different from those we saw in 2020. They were impressed. For their workloads, it’s faster than anything they’ve ever used. It’s changed what they can do.

The Studio reminds me of a few Macs we’ve seen before—it’s sort of a trashcan Mac Pro by way of the PowerMac G4 Cube. It borrows elements of the Mac Pro and the Mac mini, but it replaces neither. It’s both a glimpse at what is possible now that Apple is leaving the Intel era behind, and yet another recommitment to the Mac as a powerful and flexible platform for getting work done.

This post is the first part of our series "The great Python dataframe showdown", where we set to uncover powerful Python libraries to work with dataframes other than pandas that are less widespread but interesting and useful.

All the cool kids are on AWS, and all the cool kids are definitely using Kubernetes. So I thought it was about time to do a definitive guide on how to set it up. I will be setting up EKS - Elastic (because amazon) Kontainer Service - which is amazon-speak for Kubernetes.

In object-oriented programming, an abstract type provides a base implementation that other types can inherit from in order to gain access to some kind of shared, common functionality. What separates abstract types from regular ones is that they’re never meant to be used as-is (in fact, some programming languages even prevent abstract types from being instantiated directly), since their sole purpose is to act as a common parent for a group of related types.

A recent Twitter thread brought home to me that there is widespread confusion about what types actually are, even among the most prominent researchers. In particular: are types the same thing as sets? At the risk of repeating some of my prior posts, perhaps it’s time for a little history about type theory, set theory and their respective roles as foundations of mathematics.

While it's controversial to put business logic in your database, I suspect it is not controversial to claim that it's important to understand the permissions and security of your database. If you neglect learning how your database handles authorization, then you probably aren't following the principle of least privilege — your database might be accessed by coworkers (e.g. developers, data scientists, marketers, accountants), contractors, continuous integration processes, or deployed services that have more privileges than they should, which increases the risk of data leaks, improper data access (e.g. of personal identifiable information), and accidental or malicious data corruption and data loss.

This document attempts to explain the Apple Silicon (i.e. M1 and later) Mac boot ecosystem (henceforth "AS Macs"), as it pertains for how open OSes interoperate with the platform.

It is intended for developers and maintainers of Linux, BSD and other OS distributions and boot-related components, as well as users interested in the platform, and its goal is to cover the overall picture without delving into excessive technical detail. Specifics should be left to other wiki pages. It also omits details that only pertain to macOS (such as how kernel extensions work and are loaded).

To clarify from the start, this article is meant for people who write Haskell, because it covers do notation, the most common way of writing monadic code. I wanted to write it, because I believe you can write monadic code without understanding monads.

If you need to process a large JSON file in Python, it’s very easy to run out of memory. Even if the raw data fits in memory, the Python representation can increase memory usage even more.

And that means either slow processing, as your program swaps to disk, or crashing when you run out of memory.

One common solution is streaming parsing, aka lazy parsing, iterative parsing, or chunked processing. Let’s see how you can apply this technique to JSON processing.

Swift 5.6 includes a number of enhancements to the type system, improved interaction with pointers, and adds the ability to run new plugin commands using the package manager.

There’s a lot of arguments for and against monorepos. Done right, I believe monorepos will help you move with urgency and focus as your engineering organization grows. That doesn’t mean it’s easy! I’ve also seen monorepos turn into monoliths that block teams from getting anything out in a timely manner.

The general advice of “just convert all local date/time data to UTC and store that” is overly broad in my view. For future and near-past events, it doesn’t take into account that time zone rules change, making the initial conversion potentially inaccurate. Part of the point of writing this blog post is to raise awareness, so that even if people do still recommend storing UTC, they can add appropriate caveats rather than treating it as a universal silver bullet.

With massive data volumes growing at exponential rates, we need to find scalable methods to process them and find insights.

The CSSWG (CSS Working Group) is currently debating what to name a conditional structure, and it’s kind of fascinating. There are a lot of strong opinions, and I’m not sure how many of them are weakly held.

I’ve been writing Go professionally for five years now, and the feature I’ve wanted the most – more than generics, even – is a sum type.

Specifically, I want to define a type comprising several distinct values, and to write code that handles all possible values of that type which is checked by the compiler.

Such a type is key in large projects where the compiler should help you find modules that need updating when you extend the base sum type. With generics in go 1.18, it’s possible to write an ergonomic switch statement that can help polyfill for sum types, until they are supported properly.