Redis Implementation Guides

Redis is more than a cache. Its versatile data structures and atomic operations make it ideal for implementing common distributed system primitives like rate limiters, locks, queues, and counters. These patterns appear everywhere in production systems but are often implemented incorrectly or inefficiently.

This section provides practical guides for implementing these primitives with Redis. Each guide covers multiple approaches, explains the tradeoffs, and includes working code examples you can adapt for your own systems.

📄️ How to Implement Rate Limiting in Redis

Every API needs rate limiting, but not every API needs the same algorithm. The right choice depends on whether you want strict limits, smooth traffic, burst tolerance, or minimal memory usage. Redis can implement all the common approaches, each with different commands and tradeoffs.

📄️ How to Build a Scheduled Queue in Redis

Many applications need to process tasks at a specific time in the future rather than immediately. Email reminders that go out 24 hours before an event, retry logic that waits before trying again, subscription renewals that trigger on a billing date. These all require a scheduled queue: a way to store tasks now and process them later when their time comes.

📄️ How to Build a URL Shortener in Redis

URL shorteners turn long URLs into compact codes that redirect to the original destination. Behind every short link is a simple lookup: given a code like abc123, return the full URL. Redis excels at this because the core operation is a key-value lookup, and Redis handles millions of these per second with sub-millisecond latency.

📄️ How to Build a Distributed Lock in Redis

When multiple processes need exclusive access to a shared resource, you need a lock. In a single process, a mutex works fine. Across multiple servers, you need a distributed lock: a way for any process to claim exclusive access, do its work, and release the lock so others can proceed. Common use cases include preventing duplicate cron job execution, serializing access to external APIs with strict rate limits, and ensuring only one worker processes a specific task.

📄️ How to Build Feature Flags in Redis

Feature flags let you enable or disable functionality without deploying new code. They decouple releases from deployments: you can ship code to production with a feature turned off, then flip it on for specific users, a percentage of traffic, or everyone at once. This enables gradual rollouts, A/B testing, kill switches for problematic features, and beta access for specific customers.

📄️ How to Track Online Users in Redis

Knowing who is currently online powers features like "5 friends are active," green status dots in chat applications, and "3 people are viewing this document." The challenge is that "online" is fuzzy. Users do not explicitly log out. They close browser tabs, lose network connections, or just walk away. A presence system must infer status from activity and handle users who silently disappear.

📄️ How to Store User Sessions in Redis

Web applications need to remember who you are between requests. After you log in, the server creates a session) containing your user ID, permissions, and other state. Each subsequent request includes a session ID that the server uses to look up this data. The challenge is where to store sessions when you have multiple application servers behind a load balancer).

📄️ How to Process Jobs Exactly Once in Redis

Processing a job more than once can cause real problems. Charging a customer twice, sending duplicate emails, or creating duplicate records all erode trust and create support tickets. The challenge is that distributed systems fail in messy ways: workers crash mid-job, networks partition, and processes restart. Ensuring a job runs exactly once requires careful coordination.

📄️ How to Build Typing Indicators in Redis

The "someone is typing..." indicator is a small detail that makes chat feel alive. When you see those three dots or a friend's name appear, you know a message is coming. Building this feature seems simple at first, but the edge cases add up quickly. What happens when someone starts typing, gets distracted, and never sends? What if multiple people type at once? What if a user closes their browser mid-sentence?

📄️ How to Count Unique Users Without Storing Every ID

Counting unique visitors sounds simple. Add each user ID to a collection, then count the size. But what happens when you have millions of visitors? A naive approach stores every single ID, and memory grows linearly with your user count. Ten million visitors means ten million stored IDs, consuming hundreds of megabytes just to produce one number.

📄️ How to Check if a Value Exists Without Storing It

Checking if something exists is one of the most common operations in software. Is this username taken? Have I already processed this webhook? Is this IP address on the blocklist? The straightforward approach is to store every value and check against the list. But when that list grows to millions of entries, memory becomes a problem.

📄️ How to Build a Reliable Queue in Redis

The simplest Redis queue uses LPUSH to add jobs and RPOP to fetch them. This works until a worker crashes after popping a job but before finishing it. The job vanishes, lost forever. For background tasks that matter, you need a queue where jobs survive worker failures.