MongoDB and Redis: Combination Strategies for Caching and Database

This article introduces methods to optimize system performance by combining MongoDB and Redis. MongoDB, a document - oriented database, is suitable for long - term storage of complex semi - structured data (such as product details) but has slow disk I/O. Redis, an in - memory cache, is fast and ideal for high - frequency hot data (such as popular products) but has limited memory. Each has its own bottlenecks when used alone, but their combination allows division of labor: MongoDB is responsible for long - term storage, while Redis handles high - frequency caching, sharing the pressure on MongoDB. Common strategies include: caching hot data from MongoDB (user requests first check Redis; if not found, query MongoDB and update the cache), session management (storing user tokens in Redis), high - frequency counters/rankings (using Redis sorted sets), and temporary data storage. It is necessary to be aware of cache penetration (requests for empty data query MongoDB), cache breakdown (a sudden increase in pressure when hot keys expire), and cache avalanche (a large number of keys expiring and flooding MongoDB). Solutions include caching empty values, random expiration, and preheating the cache. In summary, the combination achieves the division of labor of "long - term storage + high - frequency caching", improving performance. It is necessary to flexibly apply it to different scenarios and pay attention to cache - related issues.