Redis is an open-source, in-memory data structure store used as a database, cache, message broker, and streaming engine. It supports various data structures such as strings, hashes, lists, sets, and more.
Redis Anti-Patterns Overview
Redis, despite its performance and versatility, has several common anti-patterns that can lead to memory issues, performance degradation, and operational problems. Here are the most important anti-patterns to avoid when working with Redis.
Using Redis as a Primary Database
Using Redis as your only database can lead to data loss, as Redis is primarily an in-memory store. Use Redis as a cache or for specific use cases (like session storage, rate limiting) alongside a persistent primary database.
Not Setting Key Expiration
Not setting expiration times on keys can lead to memory exhaustion as Redis accumulates stale data. Set appropriate TTLs (Time To Live) for different types of data based on how long they should be kept.
Using Keys Command in Production
The KEYS
command blocks the Redis server until it completes, which can cause performance issues in production. Use SCAN
instead, which iterates through keys incrementally without blocking the server.
Storing Large Objects
Storing large objects in Redis can lead to increased memory usage and poor performance. Break down large objects into smaller pieces, store only the necessary fields, or use a different storage solution for large data.
Not Using the Right Data Structure
Using the wrong data structure for your use case can lead to inefficient operations and complex application code. Choose the appropriate Redis data structure (string, list, set, sorted set, hash) based on how you need to access and manipulate the data.
Not Using Pipelining or Transactions
Sending individual commands to Redis creates network overhead. Use pipelining to send multiple commands in a single network round trip, and transactions (MULTI/EXEC) when you need operations to be executed atomically.
Not Implementing Proper Error Handling
Not handling Redis errors can lead to application crashes or incorrect behavior. Implement proper error handling for all Redis operations, including connection errors, command errors, and handling of null values.
Not Monitoring Redis Memory Usage
Not monitoring Redis memory usage can lead to unexpected out-of-memory errors or performance degradation. Set up monitoring for Redis memory usage and implement alerts for when memory usage exceeds certain thresholds.
Not Configuring Eviction Policies
Not configuring appropriate eviction policies can lead to Redis running out of memory and crashing. Configure maxmemory
and an appropriate eviction policy (allkeys-lru
, volatile-lru
, etc.) based on your use case.
Using Redis Pub/Sub for Critical Messages
Redis Pub/Sub doesn’t persist messages or provide delivery guarantees, making it unsuitable for critical messages. Use a proper message queue with persistence and acknowledgment for critical messages, such as Redis Streams, RabbitMQ, or Kafka.
Not Using Connection Pooling
Creating new Redis connections for each operation is inefficient and can lead to connection limits being reached. Use connection pooling to reuse connections across operations.
Not Using Redis Cluster for High Availability
Using a single Redis instance for critical applications can lead to single points of failure. Use Redis Cluster or Sentinel for high availability and automatic failover.
Not Using Redis for Its Intended Use Cases
Using Redis for complex queries and relationships it wasn’t designed for can lead to complex application code and poor performance. Use Redis for its intended use cases (caching, session storage, rate limiting, etc.) and use other databases for complex queries and relationships.