performance

worker-threads vs cluster: When to Use Which, With Reasoning Node.js is single-threaded. That's the first thing everyone learns. But at some point you realize okay, single-threaded is fine for most things. But what happens when your app needs to actually use more than one CPU core? Or what happens when one heavy task starts blocking everything else? That's where worker-threads and cluster both sh…

From ThreadPoolExecutor to httpx AsyncClient: True Async Refactoring Published on : 2026-06-06 Reading time : 6 min Tags : #python #async #performance #optimization The Problem: Fake Async The supabase-async library claimed to be async but actually wrapped synchronous calls with ThreadPoolExecutor: # ❌ Fake async (old code) class SupabaseAsync : def __init__ ( self ): self . _executor = ThreadPoo…

The essays collected in "Performing Space" are intent on moving forward the discussion about the relevance and significance of the interrelation between performance and space. Besides, in virtue of the fact that the contributors to the volume have different disciplinary or artistic backgrounds, this collection is aimed to initiate an interdisciplinary examination of performance and space, and to …

🔍 Introduction If you've ever used a bottleneck calculator, you've probably seen a simple percentage telling you whether your CPU or GPU is holding your system back. But here’s the truth most people don’t realize: Bottlenecks are not fixed numbers — they are dynamic, workload-dependent behaviors. In this post, we’ll go beyond basic tools and break down how CPU and GPU bottlenecks actually work in…

The server is on a Gbit link. ethtool confirms 1000 Mbps on the interface. And yet every SFTP transfer caps out somewhere around 800 KB/s. Not 80 MB/s — 800 kilobytes per second. Less than 1% of the theoretical capacity. The problem isn't bandwidth. It's a stack of bad defaults: a congestion control algorithm designed for year-2000 networks, microscopically small NIC ring buffers, and application…

When your profiler swears everything’s fast but your metrics are screaming bloody murder eBPF From Rust: The 34ms Ghost We Couldn’t See When your profiler swears everything’s fast but your metrics are screaming bloody murder eBPF provides kernel-depth observability without overhead — tracing from userspace through syscalls to kernel internals reveals bottlenecks invisible to traditional profilers…