Redis core

Redis Core

Foundational guidance for modeling data in Redis. Covers data-type selection and key-name conventions — the two decisions that most directly drive memory, performance, and maintainability.

1. Choose the right data structure

Pick the type that matches the access pattern, not just the shape of the data.

Use case	Recommended type	Why
Simple values, counters	String	Atomic INCR/DECR, SET/GET
Object with independently updated fields	Hash	Per-field reads/writes, no whole-object rewrite
Queue, recent-N items	List	O(1) push/pop at ends
Unique items, membership checks	Set	O(1) SADD/SISMEMBER/SCARD
Rankings, score-based ranges	Sorted Set	Score-ordered; ZADD/ZRANGE/ZRANK
Nested / hierarchical data	JSON	Path-level updates, nested arrays, RQE indexing
Event log, fan-out messaging	Stream	Persistent, consumer groups
Vector similarity	Vector Set	Native vector storage with HNSW

Common anti-pattern: stuffing a flat object into a serialized string. Updating one field means fetch + parse + mutate + rewrite. Use a Hash instead.

See references/choose-data-structure.md for full rationale and Python/Java examples.

2. Use consistent key names

Use colon-separated segments with a stable hierarchy:

{entity}:{id}:{attribute}
user:1001:profile
user:1001:settings
order:2024:items
session:abc123
article:987:likes
game:space-invaders:leaderboard

Rules of thumb:

• Lowercase, colon-separated. No spaces, no mixed casing (User_1001_Profile is bad).
• Keep keys short but readable — keys live in memory and appear in every command.
• Don't use full URLs or long strings as keys. Extract a short identifier, or use a hash digest of the URL.
• Prefix for multi-tenancy (tenant:42:user:7:cart) so scans and ACLs can target a tenant cleanly.
• Be consistent. Pick one convention per service and apply it across all keys.

See references/key-naming.md for cleanup examples and edge cases.

References

• Redis: Choosing the right data type
• Redis: Keys