redis

Redis Best Practices

Redis is a powerful, single-threaded, in-memory database. Its efficiency hinges on how you interact with it. Follow these rules to ensure your applications are performant, scalable, and resilient.

1. Data Modeling & Key Design

Efficient key design is paramount for performance and memory.

1.1. Use Hierarchical Key Naming

Adopt a consistent, hierarchical key naming convention. This improves readability and allows for logical grouping.

❌ BAD

user:1:name
user:1:email
product:123:details
order:456:items

✅ GOOD

user:1:profile:name
user:1:profile:email
product:123:data
order:456:cart:items

Rationale: Clearer separation, better organization.

1.2. Avoid Large Objects in Single Keys

Do not store multi-megabyte objects in a single key. Break down complex data into hashes or streams. This improves eviction, replication, and memory efficiency.

❌ BAD

# Storing a large JSON object directly as a string
user_data = {"id": 1, "name": "Alice", "preferences": {...large_object...}, "history": [...many_items...]}
redis_client.set(f"user:1:data", json.dumps(user_data))

✅ GOOD

# Using a Redis Hash for structured data
redis_client.hset(f"user:1:profile", mapping={
    "id": 1,
    "name": "Alice",
    "email": "[email protected]"
})
# Storing large, evolving data in a stream or separate keys
redis_client.xadd(f"user:1:activity_stream", {"action": "login", "timestamp": time.time()})

Rationale: Hashes are optimized for field-value pairs, streams for time-series data. This keeps individual values small, aiding Redis's single-threaded nature.

1.3. Use Hashtags for Multi-Key Operations in Clusters

When performing multi-key operations (e.g., transactions, Lua scripts) in a Redis Cluster, ensure all involved keys reside on the same hash slot by using hashtags.

❌ BAD

# These keys will likely be on different slots, causing MOVED errors in a cluster
redis_client.mget("user:1:profile", "user:1:orders")