MySQL Performance Benchmarking: From Manual Tests to Production-Ready, Multi-Environment Analysis What core problem does this article solve? It provides a complete, repeatable workflow for benchmarking MySQL performance using sysbench and tsar, transforming raw numbers into actionable insights for infrastructure decisions. Performance testing is often treated as an afterthought—run a few commands, glance at the QPS, and call it a day. But when you’re choosing between cloud providers, validating new hardware, or tuning critical database parameters, gut feelings aren’t enough. You need precise, reproducible data aligned with system metrics. This guide walks through an integrated benchmarking suite that automates testing, captures …
When your team starts integrating artificial intelligence into daily workflows, there’s one detail that often gets overlooked: data format. Most developers default to JSON because it’s universal, familiar, and compatible. But here’s a question worth asking: Is JSON really the best choice for AI models? A new format called TOON is starting to gain traction. Short for Token-Oriented Object Notation, it’s specifically designed for large language models. Today, we’ll explore why TOON might be a better choice than JSON in certain scenarios. The Hidden Costs of Using JSON with AI Let’s start with a real-world scenario. Imagine you’re building an …
First Paint, First Thought: How to Make Web Pages Feel Smart Before They Finish Loading A plain-language guide for developers who want Server-Side Rendering + AI to deliver instant, personal experiences ❝ “When the browser stops spinning, the user should already feel understood.” ❞ 1 The Problem We’re Solving Users no longer measure a web app only by how 「fast」 it is. They also ask: Does it 「speak my language」 on arrival? Does it 「know what I came for」 before I click? Does it 「feel human」, not robotic? Traditional 「Server-Side Rendering」 (SSR) gives us speed. Large Language Models (LLMs) give …
Deep Dive into RPython GC Object Allocation Performance In the realm of software development, the performance of garbage collection (GC) mechanisms plays a pivotal role in shaping the overall behavior of programs. RPython, a restricted variant of Python designed specifically for the PyPy project, boasts an impressive garbage collection component that excels in object allocation speed and memory management efficiency. I. Setting Up the Testing Environment for RPython GC A. Writing the Test Code To explore the object allocation speed of RPython GC, we first crafted a basic test script: class A(object): pass def run(loops): # Initial test code for …