Performance

Introduction Database indexes are essential for optimizing query performance in relational databases, acting as lookup tables that dramatically reduce data retrieval times from full table scans to targeted searches.[1][2] Internally, they rely on sophisticated data structures like B-trees and B+ trees to organize keys and pointers efficiently, minimizing disk I/O operations which are often the primary bottleneck in database systems.[3][5] This article explores how indexes work under the hood, from creation and structure to query execution, maintenance, and trade-offs, providing developers and DBAs with the depth needed to design effective indexing strategies. ...

Introduction Backpressure is the set of techniques that keep a fast producer from overwhelming a slow consumer. It is how systems say “not so fast,” preserving stability, bounded memory, and predictable latency. Without it, you get congestion collapses, out-of-memory crashes, timeout storms, and cascading failures. This article takes a detailed, practical look at backpressure: what it is, why it matters, how it’s implemented from TCP to reactive libraries, and how to design apps that use it well. You’ll find mental models, algorithms, concrete code examples, operational guidance, and a checklist for building robust, flow-controlled systems. ...

Introduction Port exhaustion is a pernicious, often misunderstood failure mode that can bring a high-traffic Nginx deployment to its knees. The symptoms are intermittent and confusing—spiky 5xx error rates, “Cannot assign requested address” in logs, and upstream timeouts—yet the root cause is usually simple: you ran out of usable ephemeral ports for outbound connections. In this article, we’ll explain what port exhaustion is and why Nginx is especially prone to it in reverse-proxy scenarios. We’ll cover how to diagnose it accurately, provide practical fixes at the Nginx and OS levels, and offer architectural strategies to prevent it from recurring. Whether you’re running bare metal, in containers, or behind a cloud NAT gateway, this guide will help you understand and solve Nginx port exhaustion. ...

Introduction “Elastic cache” can mean two things depending on context: the architectural idea of a cache that scales elastically with demand, and Amazon’s managed in-memory service, Amazon ElastiCache. In practice, both converge on the same goals—low latency, high throughput, and the ability to scale up or down as workloads change. In this guide, we’ll cover the fundamentals of elastic caching, common patterns, and operational considerations. We’ll then dive into Amazon ElastiCache (for Redis and Memcached), including architecture choices, security, observability, cost optimization, and sample code/infra to get you started. Whether you’re building high-traffic web apps, real-time analytics, or microservices, this article aims to be a practical, complete resource. ...

Introduction Redis has been the de facto standard for in-memory data structures for over a decade, powering low-latency caching, ephemeral data, and real-time features. In recent years, Dragonfly emerged as a modern, Redis-compatible in-memory store that promises higher throughput, lower tail latencies, and significantly better memory efficiency on today’s multi-core machines. If you’re evaluating Dragonfly vs Redis for new projects or considering switching an existing workload, this article offers a comprehensive, practical comparison based on architecture, features, performance, durability, operational models, licensing, and migration paths. It’s written for engineers and architects who want to make an informed, low-risk choice. ...