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.
Read MoreStoring JSON Data with MongoDB: Advantages of Document-Oriented Databases
As a document - oriented database, MongoDB naturally fits the JSON data structure. It can solve the problems of fixed table structure and difficult expansion of traditional relational databases. Its core advantages include: no need to pre - define table structures, and fields can be added or removed dynamically (for example, users can add a "hobby" field without modifying the table); native support for nested structures (for example, user information and addresses can be stored in a nested manner); adaptation to rapid iteration needs, and no need to modify the database structure when adding new product types or fields; support for horizontal expansion (sharding function) to handle large - volume data; and the query syntax is similar to JSON, which is intuitive and easy to use (for example, the syntax for querying "users over 20 years old" is concise). Applicable scenarios include content management systems, user portraits, and rapidly iterating Internet applications. It should be noted that for scenarios with strong transactional requirements (such as bank transfers) or extremely high data consistency requirements, it is recommended to give priority to relational databases. With its flexible structure and ease of use, MongoDB is an efficient choice for handling unstructured or semi - structured data.
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