Redis: A Deep Dive into How It Handles Data and Powers Scalable Systems

When building scalable systems, Redis often comes up as a go-to solution for performance-critical tasks. But what makes Redis so fast and reliable? What actually happens under the hood when we store or retrieve data? In this article, we’ll explore Redis in depth and how it works internally, the data structures it uses, its memory model, and how it supports large-scale systems.

What is Redis?

Redis (REmote DIctionary Server) is an open-source, in-memory key-value store. But calling it just a key-value store is underselling it. Redis supports rich data types like Lists, Sets, Sorted Sets, Hashes, Bitmaps, HyperLogLogs, and Streams, making it much more than a simple cache.

It's incredibly fast because it stores all data in memory, and it’s often used as a:

• Cache layer in front of databases

• Message broker for pub/sub systems

• Rate limiter

• Session store

• Real-time leaderboard or analytics backend

Let’s break down what makes Redis so powerful under the hood.

1. In-Memory Storage: Speed by Design

Redis keeps all data in memory, which is significantly faster than disk-based databases. When you read or write data, it’s just accessing RAM and there’s no disk I/O involved unless persistence is enabled.

But it’s not just about speed. Redis uses highly optimized data structures in memory:

• Strings are binary-safe and can be used for anything from simple keys to counters.

• Lists are linked lists, perfect for queues.

• Sets are implemented using hash tables.

• Sorted Sets (ZSETs) use skip lists for fast range queries.

• Hashes are perfect for storing objects (like user profiles).

Internally, Redis uses an abstract data structure layer (called RedisObject) to map keys to these underlying representations efficiently.

2. Memory Efficiency: Redis Isn't Just RAM-Hungry

While Redis stores everything in memory, it’s smart about memory usage. It applies optimizations like:

• SDS (Simple Dynamic Strings) to avoid frequent reallocations.

• Ziplist and Intset encodings for small Lists/Sets/Hashes to save space.

• Lazy freeing and memory recycling to reduce fragmentation.

Additionally, Redis uses LRU (Least Recently Used) and LFU (Least Frequently Used) algorithms to evict keys when using maxmemory policies, enabling it to operate as a bounded cache.

3. Persistence: RAM + Disk (Optional)

To avoid data loss, Redis supports two persistence models:

• RDB (Redis Database Backup): Snapshotting the dataset at intervals.

• AOF (Append-Only File): Logging every write operation.

You can even combine both for safety and fast recovery. Persistence doesn’t slow down Redis too much because I/O is handled in background threads.

4. Single-Threaded but Non-Blocking

Redis is often questioned for being single-threaded. But it’s designed that way — by using a single-threaded event loop for command execution, it avoids concurrency issues.

However, that doesn’t mean Redis is limited. Internally, it uses I/O multiplexing via epoll/kqueue/select to handle thousands of connections without blocking. Background operations like persistence, key eviction, and clustering happen in separate threads.

In newer Redis versions, multi-threaded I/O is available to parallelize network reads/writes, improving throughput for high-traffic environments.

5. Data Expiry and TTLs

Redis allows setting TTLs (Time To Live) on keys. It tracks expirations using a combination of passive and active strategies:

• Passive: On key access, Redis checks if it’s expired.

• Active: A background process samples a few keys at intervals and evicts the expired ones.

This hybrid approach ensures memory is reclaimed efficiently without needing to scan the entire dataset.

6. Use Cases in Scalable Systems

Redis shines in high-throughput, low-latency environments. Here’s how it’s used:

• Caching: Reduce database load by caching frequently accessed queries.

• Session Management: Store session data with TTLs in web apps.

• Pub/Sub: Real-time messaging systems and live feeds.

• Rate Limiting: Track API usage with counters and TTLs.

• Queues & Streams: Implement task queues or Kafka-like stream processing.

• Leaderboard & Scoring: Use ZSETs for ranking players or items.

Its speed, atomic operations, and ability to scale horizontally via clustering make it a top choice for performance-critical tasks.

7. Redis Clustering & Sharding

To scale beyond a single instance, Redis supports clustering:

• Data is partitioned into 16,384 hash slots.

• Each node owns a subset of these slots.

• Requests are routed based on hash slot assignment.

Cluster mode supports automatic failover, replication, and rebalancing, allowing Redis to scale horizontally.

For simpler cases, you can also use Redis Sentinel for high availability with master-slave setups.

8. Redis Modules: Extending the Core

Redis can be extended with modules to support advanced features like:

• RedisJSON: Store and query JSON documents.

• RediSearch: Full-text search capabilities.

• RedisGraph: Graph queries with Cypher-like syntax.

• RedisAI: Run ML models directly in Redis.

These modules let Redis act as more than just a cache and it becomes a flexible data platform.

Real-World Example: GitHub's Use of Redis at Scale

One of the most prominent companies leveraging Redis at scale is GitHub. They use Redis for multiple real-time use cases:

• Job Queues: GitHub runs millions of background jobs using Redis-backed queues (e.g., Sidekiq). These include CI events, webhook deliveries, and notification dispatches.

• Rate Limiting: Redis provides atomic counters with TTLs to throttle API usage and prevent abuse.

• Caching: Repository metadata, rendered Markdown, and user sessions are cached in Redis to avoid repetitive database queries and improve latency.

GitHub’s massive scale with millions of users, repositories, and events and is supported in part by Redis’s speed, efficiency, and reliability.

Final Thoughts

Redis is more than an in-memory cache and it’s a powerful data engine optimized for real-time workloads. Its efficient memory usage, clever data structures, and scalability features make it a solid choice for building distributed, high-performance systems.

If you’re architecting systems where latency and throughput matter, Redis deserves a serious look. And understanding how it works internally helps you use it more effectively and avoid common pitfalls like memory bloat or misuse of data types.

Redis isn’t just fast and it’s smart. And that’s what makes it such a critical piece of modern system design.

- Jagadhiswaran Devaraj

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