redis-best-practices

SKILL.md

Redis Best Practices

Core Principles

Use Redis for caching, session storage, real-time analytics, and message queuing

Choose appropriate data structures for your use case

Implement proper key naming conventions and expiration policies

Design for high availability and persistence requirements

Monitor memory usage and optimize for performance

Key Naming Conventions

Use colons as namespace separators

Include object type and identifier in key names

Keep keys short but descriptive

Use consistent naming patterns across your application

# Good key naming examples
user:1234:profile
user:1234:sessions
order:5678:items
cache:api:products:list
queue:email:pending
session:abc123def456
rate_limit:api:user:1234

Data Structures

Strings

Use for simple key-value storage, counters, and caching

Consider using MGET/MSET for batch operations

# Simple caching
SET cache:user:1234 '{"name":"John","email":"john@example.com"}' EX 3600

# Counters
INCR stats:pageviews:homepage
INCRBY stats:downloads:file123 5

# Atomic operations
SETNX lock:resource:456 "owner:abc" EX 30

Hashes

Use for objects with multiple fields

More memory-efficient than multiple string keys

Supports partial updates

# Store user profile
HSET user:1234 name "John Doe" email "john@example.com" created_at "2024-01-15"

# Get specific fields
HGET user:1234 email
HMGET user:1234 name email

# Increment numeric fields
HINCRBY user:1234 login_count 1

# Get all fields
HGETALL user:1234

Lists

Use for queues, recent items, and activity feeds

Consider blocking operations for queue consumers

# Message queue
LPUSH queue:emails '{"to":"user@example.com","subject":"Welcome"}'
RPOP queue:emails

# Blocking pop for workers
BRPOP queue:emails 30

# Recent activity (keep last 100)
LPUSH user:1234:activity "viewed product 567"
LTRIM user:1234:activity 0 99

# Get recent items
LRANGE user:1234:activity 0 9

Sets

Use for unique collections, tags, and relationships

Supports set operations (union, intersection, difference)

# User tags/interests
SADD user:1234:interests "technology" "music" "travel"

# Check membership
SISMEMBER user:1234:interests "music"

# Find common interests
SINTER user:1234:interests user:5678:interests

# Online users tracking
SADD online:users "user:1234"
SREM online:users "user:1234"
SMEMBERS online:users

Sorted Sets

Use for leaderboards, priority queues, and time-series data

Elements sorted by score

# Leaderboard
ZADD leaderboard:game1 1500 "player:123" 2000 "player:456" 1800 "player:789"

# Get top 10
ZREVRANGE leaderboard:game1 0 9 WITHSCORES

# Get player rank
ZREVRANK leaderboard:game1 "player:123"

# Time-based data (score = timestamp)
ZADD events:user:1234 1705329600 "login" 1705330000 "purchase"

# Get events in time range
ZRANGEBYSCORE events:user:1234 1705329600 1705333200

Streams

Use for event streaming and log data

Supports consumer groups for distributed processing

# Add events to stream
XADD events:orders * customer_id 1234 product_id 567 amount 99.99

# Read from stream
XREAD COUNT 10 STREAMS events:orders 0

# Consumer groups
XGROUP CREATE events:orders order-processors $ MKSTREAM
XREADGROUP GROUP order-processors worker1 COUNT 10 STREAMS events:orders >

# Acknowledge processed messages
XACK events:orders order-processors 1234567890-0

Caching Patterns

Cache-Aside Pattern

# Pseudo-code for cache-aside
def get_user(user_id):
    # Try cache first
    cached = redis.get(f"cache:user:{user_id}")
    if cached:
        return json.loads(cached)

    # Cache miss - fetch from database
    user = database.get_user(user_id)

    # Store in cache with expiration
    redis.setex(f"cache:user:{user_id}", 3600, json.dumps(user))

    return user

Write-Through Pattern

def update_user(user_id, data):
    # Update database
    database.update_user(user_id, data)

    # Update cache
    redis.setex(f"cache:user:{user_id}", 3600, json.dumps(data))

Cache Invalidation

# Delete specific cache
DEL cache:user:1234

# Delete by pattern (use with caution in production)
# Use SCAN instead of KEYS for large datasets
SCAN 0 MATCH cache:user:* COUNT 100

# Tag-based invalidation using sets
SADD cache:tags:user:1234 "cache:user:1234:profile" "cache:user:1234:orders"
# Invalidate all related caches
SMEMBERS cache:tags:user:1234
# Then delete each key

Expiration and Memory Management

TTL Best Practices

Always set TTL on cache keys

Use jitter to prevent thundering herd

Consider sliding expiration for session data

# Set with expiration
SET cache:data:123 "value" EX 3600

# Set expiration on existing key
EXPIRE cache:data:123 3600

# Check TTL
TTL cache:data:123

# Persist key (remove expiration)
PERSIST cache:data:123

Memory Management

# Check memory usage
INFO memory

# Get key memory usage
MEMORY USAGE cache:large:object

# Configure max memory policy
CONFIG SET maxmemory 2gb
CONFIG SET maxmemory-policy allkeys-lru

Transactions and Atomicity

MULTI/EXEC Transactions

# Transaction block
MULTI
INCR stats:views
LPUSH recent:views "page:123"
EXEC

# Watch for optimistic locking
WATCH user:1234:balance
balance = GET user:1234:balance
MULTI
SET user:1234:balance (balance - 100)
EXEC

Lua Scripts

Use for complex atomic operations

Scripts execute atomically

-- Rate limiting script
local key = KEYS[1]
local limit = tonumber(ARGV[1])
local window = tonumber(ARGV[2])

local current = tonumber(redis.call('GET', key) or '0')

if current >= limit then
    return 0
end

redis.call('INCR', key)
if current == 0 then
    redis.call('EXPIRE', key, window)
end

return 1

# Execute Lua script
EVAL "return redis.call('GET', KEYS[1])" 1 mykey

Pub/Sub and Messaging

# Publisher
PUBLISH channel:notifications '{"type":"alert","message":"New order"}'

# Subscriber
SUBSCRIBE channel:notifications

# Pattern subscription
PSUBSCRIBE channel:*

High Availability

Replication

Use replicas for read scaling

Configure proper persistence on master

# On replica
REPLICAOF master_host 6379

# Check replication status
INFO replication

Redis Sentinel

Use for automatic failover

Deploy at least 3 Sentinel instances

Redis Cluster

Use for horizontal scaling

Data automatically sharded across nodes

Use hash tags for related keys

# Hash tags ensure keys go to same slot
SET {user:1234}:profile "data"
SET {user:1234}:settings "data"

Persistence

RDB Snapshots

# Manual snapshot
BGSAVE

# Configure automatic snapshots
CONFIG SET save "900 1 300 10 60 10000"

AOF (Append-Only File)

# Enable AOF
CONFIG SET appendonly yes
CONFIG SET appendfsync everysec

# Rewrite AOF
BGREWRITEAOF

Security

Require authentication

Use TLS for connections

Bind to specific interfaces

Disable dangerous commands

# Set password
CONFIG SET requirepass "your_strong_password"

# Authenticate
AUTH your_strong_password

# Rename dangerous commands (in redis.conf)
rename-command FLUSHALL ""
rename-command FLUSHDB ""
rename-command KEYS ""

Monitoring

# Server info
INFO

# Memory stats
INFO memory

# Client connections
CLIENT LIST

# Slow log
SLOWLOG GET 10

# Monitor commands (debug only)
MONITOR

# Key count per database
INFO keyspace

Connection Management

Use connection pooling

Set appropriate timeouts

Handle reconnection gracefully

# Python example with connection pool
import redis

pool = redis.ConnectionPool(
    host='localhost',
    port=6379,
    max_connections=50,
    socket_timeout=5,
    socket_connect_timeout=5
)

redis_client = redis.Redis(connection_pool=pool)

Performance Tips

Use pipelining for batch operations

Avoid large keys (>100KB values)

Use SCAN instead of KEYS in production

Monitor and optimize memory usage

Consider using RedisJSON for complex JSON operations

# Pipeline example (pseudo-code)
pipe = redis.pipeline()
pipe.get("key1")
pipe.get("key2")
pipe.set("key3", "value")
results = pipe.execute()