Administrating

pg_stat_statements is the standard PostgreSQL extension used to track query statistics: number of executions, total and average execution time, number of returned rows, and other metrics. This information allows to analyze query behavior over time, identify problem areas, and make informed optimization decisions. However, in systems with high contention, pg_stat_statements itself can become a bottleneck and cause performance drops. In this article, we will analyze in which scenarios the extension becomes a source of problems, how sampling is structured, and in which cases its application can reduce overhead.

Training is one of the most essential factors for growth, evolution and effective collaboration for any UX design team. Design is an ever-changing industry, and there is always something new to learn even for the most experienced professionals. The reality is, not all companies take training seriously, whether the reason is a low budget, lack of time or just not realising its necessity. Meanwhile, investing in training not only boosts the quality of work, but also helps find ways to find easier solutions, work faster and optimize the whole process. Let’s discuss some tips that might help you provide effective and fruitful training for your design team without spending extra budget or wasting time on some questionable training methods. 

Ashamed to admit, I still use Swarm in 2025. It's easy to explain, quick to set up, and simple to use. It's a starting point in the world of containers when full-blown Kubernetes is overkill. But Docker's evolution hasn't been the most straightforward. Over time, it began to lack a minimal dynamic proxy for Swarm - one that you run once, which configures traffic routing to microservices automatically - and forget about it. That's how Millau was born - an ingress-proxy and load balancer based on labels. It currently powers its own website and a few other projects.

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.

You may have already read in the news that on the eve of Cosmonautics Day, a new stable release of Angie 1.9.0 was released, an nginx fork that continues to be developed by the team of former nginx developers. Approximately every quarter, we try to release new stable versions and delight users with numerous improvements. This release is no exception, but it's one thing to read a dry changelog and quite another to get to know the functionality in more detail, to learn how and in which cases it can be applied.

The list of innovations that we will discuss in more detail:

— Saving shared memory zones with cache index to disk;
— Persistent switching to a backup group of proxied servers;
— 0-RTT in the stream module;
— New busy status for proxied servers in the built-in statistics API;
— Improvements to the ACME module, which allows automatic obtaining of Let's Encrypt TLS certificates and others;
— Caching TLS certificates when using variables.

Imagine a familiar situation: it’s Monday morning, tasks are piling up, and you need to quickly spin up a new service using Postgres Pro. Or maybe you’ve just upgraded your database server over the weekend — added more CPUs, more RAM.

Here’s how to get your database tuned and ready to make the most of the new hardware and workload, without wasting time.

It’s Never Too Late: My Start into DevOps

Summary

Learn about the challenges, growth, and lessons gained — from juggling work, studies, and a pet project to achieving a dream.

For people familiar with the term DPI (Deep Packet Inspection), it often carries an unpleasant association: blocking, regulators, censorship, tightening controls. In reality, DPI is simply the name of a technology whose essence lies in the deep analysis of network traffic. Deep traffic analysis involves identifying protocols, extracting the most significant fields and metadata, classifying internet services, and analyzing the nature of network flows. I will explain how such solutions work in this article.

Historically, we’ve ended up using a few rather obscure PHP extensions—written and barely (if at all) maintained by their original authors—that aren’t available in standard Debian package sources.

We stick to the principle of “do it right, and it’ll work right,” which means Slackware-style dropping binaries into the system outside of package managers is frowned upon.

So instead, we’ll be building proper .deb packages for PHP extensions—without breaking compatibility with the existing environment.

The Cisco IOS/IOS-XE operating system is a source of inspiration for many other vendors. The internet is full of guides on how to configure a typical Cisco router for SOHO scenarios. However, unlike consumer-grade routers, configuring something like Cisco IOS requires caution. If you don’t think things through, the router may start "living its own life" and end up, for instance, as a DDoS amplification tool.

Setting up a v3 user on the server-side agent for the default Linux snmpd (net-snmp package). Out of scope: SNMP traps and read-write (rw) users.

SNMP version 3 allows packet transmission in encrypted form, making it safe to transfer telemetry over public networks without the risk of exposing either authentication information (analogous to a community string) or the data stream itself, which is encrypted using a symmetric algorithm with a shared key.

This world needs a new theory — a theory that could describe all the theories on the planet. A theory that could easily describe philosophy, mathematics, physics, and psychology. The one that makes all kinds of sciences computable.

This is exactly what we are working on. If we succeed, this theory will become the unified meta-theory of everything.

A year has passed since our last publication, and our task is to share the progress with our English-speaking audience. This is still not a stable version; it’s a draft. Therefore, we welcome any feedback, as well as your participation in the development of the links theory.

As with everything we have done before, the links theory is published and released into the public domain — it belongs to humanity, that means, it is yours. This work has many authors, but the work itself is far more important than any specific authorship. We hope that today it can become useful to more people.

We invite you to become a part of this exciting adventure.

Automatic data scraping (parsing) has become an essential practice for developers, analysts, and automation specialists. It is used to extract massive amounts of information from websites—from competitors’ prices and reviews to social media content. To achieve this, numerous “scrapers” have been developed—libraries, frameworks, and cloud services that enable programmatic extraction of web data. Some solutions are designed for rapid parsing of static pages, others for bypassing complex JavaScript navigation, and yet others for retrieving data via APIs.

In this article, I will review the top scraping tools—both open source libraries and commercial SaaS/API services—and compare them according to key metrics: • Speed and scalability; • Ability to bypass anti-bot protections; • Proxy support and CAPTCHA recognition; • Quality of documentation; • Availability of APIs and other important features.

Author: Sergey Lukyanchikov, C-NLTX/Open-Source

Disclaimer: The views expressed in this document reflect the author's subjective perspective on the current and potential capabilities of jBPM.

This text presents jBPM as a platform for orchestrating external AI-centric environments, such as Python, used for designing and running AI solutions. We will provide an overview of jBPM’s most relevant functionalities for AI orchestration and walk you through a practical example that demonstrates its effectiveness as an AI orchestration platform:

In recent years, discussions about the environmental impact of information and communication technologies (ICTs) have largely revolved around hardware — data centers, electronic waste, and energy consumption. However, an equally important factor has been overlooked: the software development methodologies themselves.

When I read the UNCTAD “Digital Economy Report 2024”, I was struck by the complete absence of any mention of how programming methodologies impact sustainability. There was no discussion of whether developers use algorithm-centric or code-centric methodologies when creating software, nor how these choices affect the environment.

This realization led me to introduce the concept of Eco-Methodological Sustainability — a new approach that highlights the role of structured software development methodologies in shaping an environmentally sustainable future for the digital economy.

The photograph accompanying this article captures a moment I once witnessed on a beach in Silicon Valley, California. The endless waves of the Pacific Ocean stretch into the distance, fading and dissolving into the sand. All three states of matter are present here: the solid, unmoving sand; the liquid, flowing water; and the wind — invisible yet tangible chaos of air. This landscape seems to symbolize the transitions and boundaries between order and freedom, between stability and change.

This very scene inspired me to reflect on how DAOs, Decentralized Autonomous Organizations, transform into a "third state" for communities. They combine the chaotic freedom of scattered individuals and the structured order of centralized organizations, creating something new — fluid and adaptive.

Apache Kafka is a distributed event-streaming platform designed to handle real-time data feeds. It allows applications to publish, process, and subscribe to streams of data in a highly scalable, fault-tolerant manner.

Have you ever struggled to turn business ideas into a product? Or tried to understand the way another team works?

If you have, you know how exhausting it can be. Different ways of thinking –business, analysis, and engineering – don’t always fit together easily.

This article blends a simple story with engineering tools to show how creativity and structure can work together. Using characters like The King, The Troll, and The Prince, it explores how storytelling can help solve tough problems and make complex ideas clearer.

If you’ve ever faced a "troll" at work, this story might help you see things differently – and maybe even make the process a bit more fun.

Hello, friends

I decided to start this year with an article in the field of HIFI sound / audiophilia and this article is about sharing the results of my wireless earbud tests, which I’ve been conducting for several years since the emergence of TWS on the market. 
Sound testing is my hobby, which can be useful for others, because in everyone’s life are important bright pleasant emotions, relaxation and mental comfort, which is not always enough in the flow of turbulent events of life. And music is just the thing to bring it all in, if only for moments, or even hours, and choosing the right headphones is crucial to getting the right effect.

Testing sound is a hobby with benefits that extend beyond personal enjoyment, as its results can be valuable to others. After all, life is made brighter and more fulfilling by positive emotions, moments of relaxation, and peace of mind — things that often feel scarce in the whirlwind of modern life. Music is one of those rare things that can bring these experiences into our lives, even if just for a moment or hours at a time. Choosing the right headphones is essential to achieving that effect.

By the end of 2024, I completed testing the best wireless in-ear headphones (commonly referred to as “earbuds”) available on the market — or at least those I could find. “Best” here doesn’t necessarily mean the most expensive. It refers to the top performers in terms of sound quality. We’ve all encountered situations where lower-priced items turned out to be just as good, if not better, than their costly counterparts. That’s why my sound-quality ranking includes headphones from various price categories — from ultra-premium TWS models to more budget-friendly options.

In the fast-paced world of software development, ensuring the quality and reliability of applications is crucial. With the ever-increasing complexity of software systems, it becomes essential to adopt a comprehensive and structured approach to testing. One such approach can be encapsulated in the acronym "RABBIT," where each letter stands for a distinct branch of testing. This multifaceted strategy covers various testing methodologies that together create a robust framework for quality assurance. This article explores each branch of RABBIT testing in detail, providing insights into their significance, implementation, and appropriate use cases.