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()