If you work with third-party APIs long enough, you will hit rate limits. The difference between a flaky integration and a reliable one often comes down to how you respond when the provider says "slow down." This guide walks through practical examples of handling rate limit exceeded errors, focusing on patterns that actually hold up in production. We will move beyond theory and into real examples from APIs like GitHub, Twitter/X, Stripe, and Google, showing how to back off, retry, and keep your users happy instead of staring at cryptic 429 responses. You will see examples of defensive client design, from exponential backoff to token bucket awareness, plus how to use headers like `Retry-After` and `X-RateLimit-Remaining` intelligently. Along the way, we will connect these patterns to broader reliability practices you might recognize from SRE and distributed systems research. By the end, you will have a clear mental playbook for handling rate limit exceeded errors in a way that is both polite to the provider and predictable for your own users.
If you work with APIs for more than five minutes, you’ll hit pagination. And the fastest way to understand it is to look at real examples of using query parameters for pagination instead of abstract theory. In this guide, we’ll walk through practical, production-style patterns you actually see in 2024 APIs, not just toy demos. We’ll look at the best examples of how APIs use query parameters like `page`, `limit`, `offset`, `cursor`, and even hybrid patterns that mix filters with pagination. Along the way, we’ll talk about trade‑offs, why some patterns age badly at scale, and which ones are more future‑proof. If you’re trying to design or consume an API and you want clear, opinionated guidance backed by real examples, you’re in the right place. By the end, you’ll have a playbook of examples of examples of using query parameters for pagination that you can copy, adapt, or at least critique with confidence.
If you’re trying to keep your API fast, fair, and hard to abuse, you don’t need more theory—you need **examples of rate limiting with Redis: practical examples** you can actually drop into production. Redis is fast, predictable, and battle‑tested in high‑traffic systems, which makes it a natural fit for enforcing request quotas, throttling bots, and protecting expensive endpoints. In this guide, we’ll walk through real examples of how teams use Redis to implement rate limiting in 2024 and 2025: from classic per‑IP limits to user‑tier quotas, login abuse protection, and multi‑region setups. You’ll see the trade‑offs between simple counters and more precise algorithms, how to use Redis primitives like `INCR`, `EXPIRE`, and Lua scripts, and where sliding windows and token buckets actually make sense. By the end, you’ll have a toolbox of patterns—not just theory—and several concrete **examples of rate limiting with Redis: practical examples** you can adapt to your own stack.