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Redis Caching Patterns

Redis Caching Patterns

Redis Caching Patterns

Redis Caching Patterns

Redis Caching Patterns

Redis Caching Patterns

Redis Caching Patterns

Redis Caching Patterns

Redis as a Cache

Redis is an in-memory data structure store that excels as a cache due to its sub-millisecond latency, rich data types, and built-in expiration. When used correctly, Redis can reduce database load by 90% or more while dramatically improving application response times.

Data Structures Overview

| Structure | Use Case | Example | |-----------|----------|---------| | String | Simple values, counters | User session, page views | | Hash | Object fields | User profile fields | | List | Ordered collection | Message queue, timeline | | Set | Unique values | Tags, followers | | Sorted Set | Ranked data | Leaderboard, rate limiting | | HyperLogLog | Cardinality estimation | Unique visitors | | Bitmap | Boolean flags | Daily active users |

Caching Patterns

Cache-Aside (Lazy Loading)

The most common caching pattern. The application checks the cache first; on a miss, it loads data from the database and populates the cache.

import redis

import json

r = redis.Redis(host='localhost', port=6379, decode_responses=True)

def get_user(user_id):

cache_key = f"user:{user_id}"

Try cache first

cached = r.get(cache_key)

if cached:

return json.loads(cached)

Cache miss: load from database

user = db.query("SELECT * FROM users WHERE id = %s", [user_id])

if user:

Populate cache with TTL

r.setex(cache_key, 3600, json.dumps(user))

return user

Pros: Only caches data that is requested, resilient to cache failures. Cons: Cache miss penalty (three round trips), stale data until TTL expires.

Write-Through

Data is written to the cache first, then to the database. Reads always hit the cache.

def update_user(user_id, data):

cache_key = f"user:{user_id}"

Write to cache first

r.setex(cache_key, 3600, json.dumps(data))

Then write to database

db.execute(

"UPDATE users SET name = %s, email = %s WHERE id = %s",

[data['name'], data['email'], user_id]

)

Pros: Cache is always consistent with database writes. Cons: Write latency increases, cache stores data that may never be read.

Write-Behind (Write-Back)

Data is written to cache and asynchronously written to the database later.

def write_behind(user_id, data):

cache_key = f"user:{user_id}"

Write to cache immediately

r.setex(cache_key, 3600, json.dumps(data))

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