How to Implement Redis as a Cache with PostgreSQL for Data Persistence

What is the best way to integrate Redis as a cache and PostgreSQL for data persistence in web service architecture?

Integrating Redis with PostgreSQL combines the benefits of fast cache retrieval with persistent data storage. This setup is particularly useful for applications requiring quick access to frequently used data, while ensuring reliable data persistence.

import redis
import psycopg2

def fetch_data(key):
    cache = redis.Redis(host='localhost', port=6379)
    # Check if data is in Redis cache
    if cache.exists(key):
        return cache.get(key)
    # If not in cache, fetch from PostgreSQL
    conn = psycopg2.connect(host='localhost', database='mydb', user='myuser', password='mypassword')
    cursor = conn.cursor()
    cursor.execute('SELECT value FROM mytable WHERE key = %s', (key,))
    result = cursor.fetchone()
    if result:
        # Store result in cache for future access
        cache.set(key, result[0])
        conn.close()
        return result[0]
    conn.close()
    return None

Causes

• High latency in database queries can degrade application performance.
• Increased load on PostgreSQL when handling repetitive queries.
• Data retrieval needs that exceed the speed capabilities of traditional databases.

Solutions

• Implement Redis as an intermediary cache to store frequently accessed data, reducing load on PostgreSQL.
• Use a cache expiration strategy to ensure data validity.
• Set up a strategy for cache invalidation on data updates in PostgreSQL to prevent stale data.

Mistake: Not setting an appropriate expiration time for cached data.

Solution: Implement a TTL (Time-To-Live) for the cache entries to ensure data freshness.

Mistake: Accessing the database for unchanged data.

Solution: Implement cache validation checks before querying PostgreSQL.

Mistake: Overloading Redis with too much data.

Solution: Only cache data critical for performance; avoid caching large blobs.