fastapi-redis-cache-reborn

• Documentation Site
• Migrating from fastapi-redis-cache
• Features
• Installation
• Usage
  • Redis Server
  • Initialize Redis in your FastAPI application
  • @cache Decorator
    • Pre-defined Lifetimes
• Questions/Contributions

Documentation Site

There is now a documentation site at https://seapagan.github.io/fastapi-redis-cache-reborn/ that is generated from the docs folder in this repository. The site is built using MkDocs and the Material for MkDocs theme. The documentation is a work in progress, but I will be adding more content as time goes on, and cutting down the README file to be more concise.

Migrating from fastapi-redis-cache

This project is a continuation of fastapi-redis-cache which seems to no longer be maintained and had fallen behind in both Redis and FastAPI versions. I decided to split this as a separate repository rather than a fork, since the original project has had no activity for a over three years.

Right now the code is basically the same as the original project, but I have updated the Package management system to use Poetry, the dependencies and the CI/CD pipeline, and added type-hinting. I've also merged some open PRs from the original project that fixed some issues.

See the TODO File file for a list of things I plan to do in the near future.

The package still has the same interface and classes as the original. You will still import the package as fastapi_redis_cache in your code, the name has only changed on PyPI to avoid conflicts with the original package. This is to make it transparent to migrate to this version.

However, it is important to make sure that the old package is uninstalled before installing this one. The package name has changed, but the module name is still fastapi_redis_cache. The best way is to remove your old virtual environment and run pip install or poetry install again.

Features

• Cache response data for async and non-async path operation functions.
• Lifetime of cached data is configured separately for each API endpoint.
• Requests with Cache-Control header containing no-cache or no-store are handled correctly (all caching behavior is disabled).
• Requests with If-None-Match header will receive a response with status 304 NOT MODIFIED if ETag for requested resource matches header value.

Installation

if you are using poetry (recommended):

poetry add fastapi-redis-cache-reborn

Otherwise you can use pip:

pip install fastapi-redis-cache-reborn

Usage

Redis Server

You will need access to a Redis server. If you don't have one running locally, you can use Docker or even a cloud service like Redis Cloud or AWS ElastiCache.

There is a docker-compose-redis-only.yml file in the root of this repository that you can use to start a Redis server locally. Just run:

docker compose -f docker-compose-redis-only.yml up -d

This will spin up a Redis server on localhost:6379, without any password, running in the background. You can stop it with:

docker compose -f docker-compose-redis-only.yml down

The image is based on redis/redis-stack so also includes RedisInsight running on port 8001 that you can use to inspect the Redis server.

Note that this is a development server and should not be used in production.

Initialize Redis in your FastAPI application

Create a FastApiRedisCache instance when your application starts by defining a 'lifespan' event handler as shown below. Replace the REDIS_SERVER_URL with the address and port of your own Redis server.

import os

from contextlib import asynccontextmanager

from fastapi import FastAPI, Request, Response
from fastapi_redis_cache import FastApiRedisCache, cache
from sqlalchemy.orm import Session

REDIS_SERVER_URL = "redis://127.0.0.1:6379"

@asynccontextmanager
async def lifespan(app: FastAPI):
    redis_cache = FastApiRedisCache()
    redis_cache.init(
        host_url=os.environ.get("REDIS_URL", REDIS_SERVER_URL),
        prefix="myapi-cache",
        response_header="X-MyAPI-Cache",
        ignore_arg_types=[Request, Response, Session]
    )
    yield

app = FastAPI(title="FastAPI Redis Cache Example",lifespan=lifespan)

# routes and more code

After creating the instance, you must call the init method. The only required argument for this method is the URL for the Redis database (host_url). All other arguments are optional:

• host_url (str) — Redis database URL. (Required)
• prefix (str) — Prefix to add to every cache key stored in the Redis database. (Optional, defaults to None)
• response_header (str) — Name of the custom header field used to identify cache hits/misses. (Optional, defaults to X-FastAPI-Cache)
• ignore_arg_types (List[Type[object]]) — Cache keys are created (in part) by combining the name and value of each argument used to invoke a path operation function. If any of the arguments have no effect on the response (such as a Request or Response object), including their type in this list will ignore those arguments when the key is created. (Optional, defaults to [Request, Response])
  • The example shown here includes the sqlalchemy.orm.Session type, if your project uses SQLAlchemy as a dependency (as demonstrated in the FastAPI docs), you should include Session in ignore_arg_types in order for cache keys to be created correctly (More info).

@cache Decorator

Decorating a path function with @cache enables caching for the endpoint. Response data is only cached for GET operations, decorating path functions for other HTTP method types will have no effect. If no arguments are provided, responses will be set to expire after one year, which, historically, is the correct way to mark data that "never expires".

# WILL NOT be cached
@app.get("/data_no_cache")
def get_data():
    return {"success": True, "message": "this data is not cacheable, for... you know, reasons"}

# Will be cached for one year
@app.get("/immutable_data")
@cache()
async def get_immutable_data():
    return {"success": True, "message": "this data can be cached indefinitely"}

Response data for the API endpoint at /immutable_data will be cached by the Redis server. Log messages are written to standard output whenever a response is added to or retrieved from the cache.

In most situations, response data must expire in a much shorter period of time than one year. Using the expire parameter, You can specify the number of seconds before data is deleted:

# Will be cached for thirty seconds
@app.get("/dynamic_data")
@cache(expire=30)
def get_dynamic_data(request: Request, response: Response):
    return {"success": True, "message": "this data should only be cached temporarily"}

Note

expire can be either an int value or timedelta object. When the TTL is very short (like the example above) this results in a decorator that is expressive and requires minimal effort to parse visually. For durations an hour or longer (e.g., @cache(expire=86400)), IMHO, using a timedelta object is much easier to grok (@cache(expire=timedelta(days=1))).

Pre-defined Lifetimes

The decorators listed below define several common durations and can be used in place of the @cache decorator:

• @cache_one_minute
• @cache_one_hour
• @cache_one_day
• @cache_one_week
• @cache_one_month
• @cache_one_year

For example, instead of @cache(expire=timedelta(days=1)), you could use:

from fastapi_redis_cache import cache_one_day

@app.get("/cache_one_day")
@cache_one_day()
def partial_cache_one_day(response: Response):
    return {"success": True, "message": "this data should be cached for 24 hours"}

If a duration that you would like to use throughout your project is missing from the list, you can easily create your own:

from functools import partial, update_wrapper
from fastapi_redis_cache import cache

ONE_HOUR_IN_SECONDS = 3600

cache_two_hours = partial(cache, expire=ONE_HOUR_IN_SECONDS * 2)
update_wrapper(cache_two_hours, cache)

Then, simply import cache_two_hours and use it to decorate your API endpoint path functions:

@app.get("/cache_two_hours")
@cache_two_hours()
def partial_cache_two_hours(response: Response):
    return {"success": True, "message": "this data should be cached for two hours"}

Tip

Please read the full documentation on the website for more information on the @cache decorator and the pre-defined lifetimes. There is also a section on cache keys that explains how the cache keys are generated and how to use them properly.

Questions/Contributions

If you have any questions, please open an issue. Any suggestions and contributions are absolutely welcome. This is still a very small and young project, I plan on adding a feature roadmap and further documentation in the near future.