FastAPI Performance Optimization: A Guide to Efficiency Improvement from Code to Deployment
To optimize the performance of FastAPI, it is necessary to systematically advance from five aspects: code, asynchronous processing, database, caching, and deployment. At the code level: prioritize using `async def` to handle I/O-intensive tasks (such as asynchronous database queries), use generators or pagination to reduce memory usage, and leverage parameter validation to filter invalid requests. For asynchronous programming, distinguish task types: use asynchronous frameworks for I/O-intensive tasks, and submit CPU-intensive tasks to a thread pool via `ThreadPoolExecutor`. The core of database optimization includes connection pool reuse, index optimization (to avoid full table scans), batch operations (e.g., `bulk_insert`), and lazy loading. Caching strategies are suitable for frequently accessed data: use in-memory caching with `cachetools` for simple scenarios, and Redis distributed caching for multi-instance deployments. At the deployment end, use Gunicorn + Uvicorn for multi-process/threading, Nginx reverse proxy for static resources, and containerization (Docker) with K8s for elastic scaling. Optimization should first identify bottlenecks, advance step-by-step from code to deployment, prioritize high-cost-effectiveness issues (such as indexes and caching), and continuously monitor and iterate.
Read MoreMongoDB 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 MoreLearn MongoDB Indexing: Boost Your Query Speed by 10x
MongoDB indexes are used to improve query performance, solving the inefficiency of "full table scan" (with a time complexity of O(n)) when no index is present. With an index, the complexity is reduced to O(log n), similar to using a library catalog to locate books. An index is a special data structure (based on B-tree/B+ tree) that stores mappings between field values and document positions. Basic types include single-field indexes (the most common, e.g., `db.users.createIndex({age:1})`); compound indexes (multi-field, e.g., `{age:1, gender:1}`, which must follow the "leftmost prefix principle"). There are also advanced types such as multikey, geospatial, and text indexes. Indexes are created using `createIndex()`, and verification is done using `explain()` to view the execution plan. It is recommended to create indexes on frequently queried, sorted, or compound query fields. Indexes are not suitable for small datasets, extremely frequent writes, low-cardinality, or highly repeated fields. Avoid over-indexing and duplicate indexes. Use `explain` to verify effectiveness and prevent index failure due to field type mismatches.
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