Scalability

Python LLM applications often start small: a FastAPI route, a call to an LLM provider, some prompt engineering, and you’re done. Then traffic grows, latencies spike, and your CPUs sit mostly idle while users wait seconds—or tens of seconds—for responses. What went wrong? One of the most common and least understood culprits is thread pool starvation. This article explains what thread pool starvation is, why it’s especially dangerous in Python LLM apps, how to detect it, and concrete patterns to avoid or fix it. ...

Introduction Distributed systems run everything from streaming platforms to payment networks and logistics providers. Building them for production requires more than just connecting services—you need to understand failure modes, consistency models, data and network behavior, and how to operate systems reliably at scale. This article provides a high-level but comprehensive tour of the essential concepts you need in practice. It favors pragmatic guidance, proven patterns, and the “gotchas” teams hit in real-world environments. ...

Introduction Thread pools are a foundational concurrency primitive used to execute units of work (tasks) using a fixed or managed set of threads. They improve performance by amortizing thread lifecycle costs, improve stability by bounding concurrency, and provide operational control via queueing, task rejection, prioritization, and metrics. Despite their ubiquity, thread pools are often misconfigured or misapplied, leading to oversubscription, latency spikes, deadlocks, or underutilization. This comprehensive guide covers how thread pools work, design dimensions and trade-offs, sizing formulas and tuning strategies, scheduling algorithms, instrumentation, and language-specific implementations with code examples. It is aimed at practitioners building high-throughput, low-latency systems, or anyone seeking a deep understanding of thread pool internals and best practices. ...

Batching API requests is a proven technique to improve throughput, reduce overhead, and tame the N+1 request problem across web and mobile apps. But batching is more than “combine a few calls into one.” To do it well you need to consider protocol details, error semantics, idempotency, observability, rate limiting, and more. This article explains how batching works, when to use it, and how to design and implement robust batch endpoints with real code examples. ...

Introduction System design is the craft of turning ideas into resilient, scalable, and cost‑effective products. It spans protocols, storage engines, compute orchestration, observability, and more. This deep, zero‑to‑hero tutorial curates the top 50 technologies you should know—organized by category—with concise explanations, practical tips, code samples, and a learning path. Whether you’re preparing for interviews or architecting large‑scale systems, use this guide as your roadmap. Note: You don’t have to master everything at once. Build a foundation, then layer on technologies as your use cases demand. ...