These days, it seems like everyone is talking about AI. AI here, AI there—AI will replace us all, and so on. I started to wonder: how exactly is AI going to replace us? I decided to dig into this question and examine the technical foundations, mainly to understand it for myself—how exactly is AI supposed to replace us all? Spoiler: it isn’t planning to just yet, but what’s already available today is impressive.
How to compare the efficiency of SQL query plans? “Measure the execution time, of course!” — an experienced reader would say. And they would be absolutely right: from a practical perspective, the more efficient DBMS is the one that delivers higher TPS. However, sometimes we need to design a system that doesn't exist yet or predict behavior under loads that haven't occurred yet. In such cases, we need a characteristic that allows us to perform a qualitative analysis of a plan or compare two plans. This post is dedicated to one such characteristic — the number of data pages read.
Complex systems often appear chaotic or incomprehensible, yet closer examination reveals that such complexity can frequently be reduced to a simple underlying mechanism. By systematically removing layers of emergent behavior, one can uncover a fundamental rule or equation from which the entire system originates.
A few weeks ago, OpenAI announced that Codex is available for Plus users, and I didn’t miss a chance to try it. And today, I’m excited to share a guide to OpenAI’s Codex. As a developer, I’ve found it to be a powerful and practical tool.
We append the following metrics to the T-SQL procedure statements: execution count (x), CPU time in milliseconds (c), duration in microseconds (d), number of reads (r), number of writes (w), and @@rowcount value (n). You can display these as absolute values or percentages.
For years, we’ve studied Oracle to make PostgreSQL a more migration-friendly option. We introduced tools similar to SQL profile and SQL plan baseline as AQO and sr_plan extensions. In some cases, PostgreSQL even outperforms Oracle, especially in automatic re-optimization.
Migrations from Oracle to PostgreSQL are usually smooth performance-wise, and we’ve even developed session variable extensions to make the transition easier. While many enterprise-only features exist, PostgreSQL often integrates popular solutions directly into the core.
Manual resource management in low level C-style C++ code might be annoying. It's not practical to create good enough RAII wrappers for every single C API you use, but approaches with goto cleanup or loads of nested if (success) hurt readability.
A defer macro to the rescue! The deferred lambda will be executed on scope exit, no matter how it happens: you can return from any point, throw an exception (if allowed), or even use a goto to an outer scope. It is truly zero-cost and doesn't rely on C runtime or standard library, so it can be used even in kernel development.
In this tutorial, I’ll walk you through everything I’ve learned about using Google Jules — an asynchronous coding agent. I’ve kept the explanations clear and simple, so whether you're an experienced developer or a beginner, you’ll be able to follow along. By the end, you should feel confident working with Jules: assigning tasks, reviewing its output, and making the most of its capabilities. Ready? Let’s dive in. ;)
Hi everyone, I’m Alexey. I’m a big fan of observability, and in this post, I want to share something I’ve been working on — the pgpro-otel-collector.
TL;DR: pgpro-otel-collector is an OpenTelemetry collector (aka monitoring agent) tailored for gathering Postgres metrics and logs — brought to you by PostgresPro.
This article presents a business-driven approach to software architecture, focusing on maximizing profitability through technical decisions. It advocates for:
Distributed, stateless services with immutable models for scalability
Isolated third-party integrations without disrupting core logic
Simple, modular design
Best for:
✔ Tech leads designing scalable systems
✔ Managers optimizing dev efficiency
✔ Stakeholders evaluating ROI on architecture
If you've ever run multiple instances of PostgreSQL or other software on a single machine (whether virtual or physical), you've probably encountered the "noisy neighbor" effect — when instances disrupted each other. So, how do you make them get along? We’ve got the answer!
Learning Shopify has been on my bucket list for a few years now. Plenty of people in my circle — friends, colleagues, and fellow devs — are all somehow involved with Shopify in one way or the other. Earlier this year, I finally had some breathing room between projects, so I figured it was the perfect time to give Shopify a proper look.
I started exploring the platform by setting up a dev store, poking around the admin, and skimming through the API manual. While this was a quick and easy start, it didn’t give me a deeper understanding of the platform. Plus, clicking my way through the UI felt repetitive and tedious.
That got me thinking: is there a more efficient, developer-centric way to manage a store? Something that I could run in a terminal, plug into a CI/CD pipeline, or script my way out of those mundane tasks.
I recently got my hands on Windsurf AI, and I wanted to share my experience with this AI-first Integrated Development Environment (IDE). If you’re a developer like me, always on the lookout for tools to boost productivity, this might be on your radar. So, is Windsurf AI the real deal? Let’s find out.
My name is Ilya and I’m a Core Developer at Bright Security. In Bright we work on a DAST (Dynamic Application Security Testing) solution that helps development teams find and fix vulnerabilities early, straight from CI/CD. My own path began in full-stack engineering, but almost a decade of shipping production code drew me ever deeper into application security. In this article I’m explaining key approaches on what SAML actually is and how we detect it in Bright using DAST.
"Electricity is not just a form of energy, but the invisible current that connects all aspects of our lives, from technology to nature itself."
Would you like to know which indexes are used frequently or rarely? Which ones aren't used at all? Which tables and indexes are the largest? It's very easy to create visualizations for this. They're both visually appealing and practically useful.
If you work with PostgreSQL, you've likely run into performance issues at some point — especially as your database grows. Things may have been running smoothly at first, but as your client database expanded, queries started slowing down. Sound familiar? Here's a guide to help you identify and fix problematic queries, so you can get your PostgreSQL database running at peak performance again.
I’ve recently been playing with Agent Mode in VS Code, which looks promising. If you’re using VS Code and want to give your development a turbo boost, you’ll want to hear about this.
This article is based on the experience of developing the memsafe library, which, using the Clang plugin, adds safe memory management and invalidation control of reference data types to C++ during source code compilation.
When creating an app or any other software product for children, one of the hardest parts of the process is conducting user research correctly. Although working with kids might seem fun and entertaining, it takes certain skills to get them engaged in testing your product and voicing their opinion so you could gather all the necessary information. It’s important to understand that mentally kids function differently than adults, so working with a young target audience requires a different approach. Treating children like adults in the UX research process can lead to serious mistakes: they might not get a proper understanding of your product and you might end up getting wrong results, only wasting your time and budget. In order to avoid that, we’ve collected a few tips below that might help you communicate with kids more effectively for a productive and fruitful research session.
