I recently had the opportunity to encrypt/decrypt stuff using AES, but I didn’t know it inside out well. I couldn’t help but be curious about how it is working, and I realized my mind could only be satisfied by digging deeper into its implementation. This post walks you through how AES encryption works by reading core implementation written in Go.

I believe that Git becomes understandable if we peel back the curtain and look at how Git stores your repository data. After we investigate this model, we’ll explore how this new perspective helps us understand commands like git cherry-pick and git rebase

Do you work on a service that you (and, presumably, your users) would like to keep working? Well, if so, I’m here to convince you that your service should have at least one or two generic mitigations ready to go. If it doesn’t, you’re in for a bad time. If it does, treasure them, maintain them, and use them, lest they rot beneath your feet.

embrace failure. automation. machine learning to automate the automation. build system that can detect and remediate issues.monitor and remediate hardware events without adversely impacting application performance. use prediction methodology for remediations. automate root cause analysis for hardware and system failures at scale to get to the bottom of issues quickly.

Last month, Cloudflare published a postmortem of a recent 6-hour outage caused by a partial switch failure which left etcd unavailable as it was unable to establish a stable leader. This outage has understandably led to discussion online about exactly what liveness guarantees are provided by the Raft consensus algorithm in the face of network failures.

Events in distributed systems include sending or receiving messages, or changing some state in a node. Not all events are related, but some events can cause and influence how other, later events, occur. For instance, a reply to a received mail message is influenced by that message, and maybe by other prior messages also received. This article brings an introduction to classic causality tracking mechanisms and covers some more recent developments. The presentation is supported by a new graphical notation that allows an intuitive interpretation of the causality relations described.

Despite the click bait title, this is a really good post with real pain point of Postgres.

Talk about one particularly difficult issue that blocked the launch of a device in Europe. This wasn’t the last bug we fixed on this platform, but it was the hardest to track down. It was outside of the Netflix application, in a part of the system that was outside of the playback pipeline, and all of the initial data pointed to a bug in the Netflix application itself.

we’ll cover the production workhorses for concurrent software – threading and locking – and learn about them through a series of interesting programs. By the end of this article you’ll know the terminology and patterns used by POSIX threads (pthreads