Many might have seen PostgreSQL issue the following error message: “ERROR: deadlock detected”. But what does it really mean? How can we prevent a deadlock and how can we reproduce the problem? Let’s dive into PostgreSQL locking and understand what deadlock and deadlock_timeout really mean.

Concurrent programming requires synchronization. We can’t have more than one thread accessing data at the same time; otherwise, we end up with a data race. The most common solution is to wrap the critical data access in a mutex. When misused, it can cause a program to run worse in threaded mode than it would have single threaded!

Now we know about mutex, then how about futexes, which are popup sometime especially when you are working with strace. Futex mechanism was proposed by Linux contributors from IBM in 2002 [1]; it was integrated into the kernel in late 2003. The main idea is to enable a more efficient way for userspace code to synchronize multiple threads, with minimal kernel involvement.

(or any static file hoster). So how do you use a database on a static file hoster? Firstly, SQLite (written in C) is compiled to WebAssembly. SQLite can be compiled with emscripten without any modifications, and the sql.js library is a thin JS wrapper around the wasm code. It also leverage HTTP Range header, which we cover in issue #196

We use cd all day long. It’s a built-in shell primitive. But at the same time, we also have /usr/bin/cd.What purpose does it serve? Another deep dive on this is cd is not a program, check it out too

With an increasing number of breaches, intrusions, and data thefts, securing a web application is extremely important. On the other hand, programmers often do not have a strong grasp of how attacks work and how to mitigate them. This post attempts to close that gap a little.

JWT is gaining popularity due to its convenience use to hold information without issuing an auth storage. Naturally, it has a lot of attacking vectors. Let’s learn about those attacks so we can use JWT securely

The post target data scientist to use SQL instead of analyze data with Panda. But we can see how thing are done in raw SQL to extract information out of SQL database Running and Cumulative Aggregation, one of topic in this article can be quite useful to analyze the growth or trend.

Back in old day, we take picture use film in camera. To create different look of image, we used a different film. Modern mirrorless cameras from Fujifilm have film simulation presets that digitally mimic the style of a particular film stock. So how these simulations were achieved? A good post to learn about image maniupulation, vector

Github praises feature flags in release rollout. This is a better post in my opinion in this topic because it also talk about the cost of feature flags.

We don’t usually include article that just show code without technical deep dive but this has some useful ones so we include it here