Introduction
Redis cache is a high-performance, in-memory data store used to speed applications and reduce database load. Developers use Redis to cache query results, session data, and other frequently accessed items to cut latency and increase throughput. This guide explains core Redis concepts, setup options, eviction and persistence choices, clustering basics, and simple patterns you can apply today. Expect clear steps, real-world examples, and practical tips aimed at beginners and intermediate readers who want to implement reliable, fast caching.
What is Redis and why use a cache?
Redis is an open-source in-memory datastore that supports strings, hashes, lists, sets, and more. As a cache, it stores hot data close to your application so reads are extremely fast. Typical benefits include:
- Reduced latency and faster response times
- Lower persistent database load
- Support for advanced data structures and atomic operations
- Flexible persistence and replication options
Core concepts
Memory and performance
Redis operates in memory, so consider available RAM first. Use keys and small values to maximize effective cache size. Monitor memory usage and evictions when approaching limits.
TTL and expiry
Set a TTL (time-to-live) per key to auto-expire stale cache entries. TTL ensures cache doesn’t serve outdated data and helps with capacity management.
Eviction policies
When memory is full, Redis evicts keys based on a configured policy. Common policies:
- noeviction — refuse writes when full
- allkeys-lru — evict least recently used across all keys
- volatile-lru — evict least recently used among expiring keys
Getting started: Basic setup
Install Redis locally or use a managed service. For local testing, install from redis.io. Managed options include cloud providers and Azure Cache for Redis: Azure Cache for Redis.
Simple configuration
Key config choices:
- maxmemory — set a memory limit
- maxmemory-policy — choose an eviction policy
- save / appendonly — choose persistence settings
Client usage example (pseudo-code)
Cache read-through pattern (simplified):
// 1. Try cache
value = redis.get(key)
if value == null:
// 2. Load from DB
value = db.query(key)
// 3. Store with TTL
redis.set(key, value, ttl=300)
return value
Eviction, persistence, and durability
Pick settings based on use case:
- Cache-only: use no persistence for pure cache workloads to maximize throughput.
- Hybrid: use RDB snapshots for periodic persistence and appendonly for stronger durability.
- For session stores, consider replication and persistence trade-offs for failover behavior.
Scaling with clustering and replication
Redis supports two main scaling patterns:
- Replication (master-replica) for read scaling and failover
- Clustering for sharding data across nodes to increase overall memory and throughput
When to use clustering
Use clustering when dataset size exceeds a single node’s memory or when you need linear write scaling. Clustering requires clients that understand Redis Cluster slots.
Patterns and best practices
Cache-aside (lazy loading)
Application checks cache first, loads from DB on miss, then writes to cache. This keeps cache simple and resilient to evictions.
Write-through and write-behind
Write-through writes to cache and DB synchronously. Write-behind buffers writes to the DB asynchronously. Choose based on consistency and latency needs.
Atomic counters and rate limiting
Use Redis commands like INCR and INCRBY with TTL for counters and token-bucket rate limiting. These operations are atomic and fast.
Cache invalidation
Invalidate or update keys on writes to the source of truth. Use versioned keys or publish/subscribe to notify other services when data changes.
Security and networking
Disable external access on default ports unless behind a secure network. Use AUTH, TLS, and role-based access on managed services.
Redis vs Memcached: Quick comparison
| Feature | Redis | Memcached |
|---|---|---|
| Data types | Rich (strings, hashes, lists, sets) | Simple key-value |
| Persistence | Yes (RDB, AOF) | No |
| Clustering | Yes | Limited |
| Use cases | Cache, session store, leaderboard, pub/sub | Simple cache |
Real-world examples
API response caching
Cache JSON responses for 30–300 seconds depending on data volatility. Use caches for filtering and sorted queries with pre-computed results to reduce DB load.
Session storage
Store session tokens in Redis with TTL matching session expiry. Enable replication to avoid session loss on failover.
Leaderboards
Use Redis sorted sets (ZSET) to implement leaderboards with atomic score updates and fast range queries.
Monitoring and tuning
Track metrics: memory usage, evictions, hit rate, latency, and connected clients. Tools: Redis INFO command, Prometheus exporters, and cloud provider dashboards.
Troubleshooting common issues
- High evictions: increase maxmemory, change eviction policy, or add nodes.
- Slow commands: avoid KEYS on large datasets; use SCAN or index structures.
- Replication lag: check network and CPU spikes; tune persistence options.
Conclusion
Redis cache provides a fast, flexible way to speed applications and reduce backend load. Choose the right eviction policy, TTL strategy, and scaling model for your needs. Start small with cache-aside patterns, monitor key metrics, and iterate toward clustering or managed services as demand grows. Apply the examples and best practices above to deliver immediate performance gains.