Redis Best Practices

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()

Converted and distributed by TomeVault — claim your Tome and manage your conversions.