FastAPI Cache
A caching library for FastAPI that supports Redis, Memcached, DynamoDB, and in-memory backends with HTTP cache header support.
What is FastAPI Cache?
fastapi-cache is a Python library that adds caching capabilities to FastAPI applications. It allows developers to cache endpoint responses and function results using decorators, with support for multiple backend stores like Redis, Memcached, and DynamoDB. It solves performance bottlenecks by reducing repetitive computations and database calls in API routes.
FastAPI developers building high-performance web APIs that need response caching, especially those using Redis, Memcached, or DynamoDB as cache stores.
Developers choose fastapi-cache for its seamless integration with FastAPI, support for multiple backends, HTTP cache header compliance, and flexible caching strategies without boilerplate code.
Overview
fastapi-cache is a tool to cache fastapi response and function result, with backends support redis and memcached.
Use Cases
Best For
- Caching FastAPI endpoint responses to improve API response times
- Reducing database load by caching frequently accessed query results
- Implementing HTTP caching with ETag and Cache-Control headers in FastAPI
- Using Redis or Memcached as a cache backend for FastAPI applications
- Caching function results across distributed systems with DynamoDB
- Adding transparent caching to existing FastAPI routes with minimal code changes
Not Ideal For
- Applications requiring automatic background cleanup of expired cache entries, as the in-memory backend only removes them on access.
- Teams not using FastAPI's type annotations extensively, since caching of Pydantic models depends on return type hints for proper deserialization.
- Projects needing advanced cache invalidation strategies beyond simple time-based expiration, as the library focuses on basic expire mechanics without built-in event-driven invalidation.
- Environments where Redis clients are configured with decode_responses=True, as this breaks caching due to binary data handling requirements.
Pros & Cons
Pros
Supports Redis, Memcached, DynamoDB, and in-memory storage, allowing teams to choose based on infrastructure needs, as shown in the installation extras and backend options in the README.
Uses a simple decorator-based API that fits naturally into FastAPI route handlers, with examples in the README showing minimal code changes for caching endpoints and functions.
Enables efficient client-side caching by handling ETag and Cache-Control headers and supporting conditional requests like If-Non-Match, reducing server load as highlighted in the features.
Offers flexible cache key builders, expiration times, namespaces, and coders (JSON, Pickle, or custom), allowing fine-grained control over caching behavior through decorator parameters.
Cons
For caching complex types like Pydantic models, return type annotations are mandatory; without them, the cache reverts to primitive JSON, limiting usability in annotation-light codebases as warned in the README.
The in-memory backend does not automatically purge expired keys unless accessed, potentially leading to memory bloat in long-running applications, a noted drawback in the backend notes.
Injects request and response dependencies with fixed namespace keywords that could conflict with existing dependencies, requiring careful management and namespace adjustments in larger projects.
Frequently Asked Questions
Related Projects
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