fastapi-redis-dep is a library that integrates Redis dependencies into FastAPI, making it easier to work with Redis in FastAPI.

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Project description

FastAPI-Redis-Dep（中文）

fastapi-redis-dep 是一个用于 FastAPI 的 Redis 集成库，通过依赖注入和生命周期管理的方式简化了 FastAPI 中 Redis 的操作。该库受 fastapi_plugins 第三方库的启发，提供了常用的中间件集成方式。

安装方式

使用 PIP 安装

pip install fastapi-redis-dep

使用 POETRY 安装

poetry add fastapi-redis-dep

API 介绍

快速开始

安装完成后，你需要在 FastAPI 的生命周期函数中注册本库。具体步骤如下：

导入 RedisRegistry 类。
定义生命周期管理函数 lifespan。

from fastapi import FastAPI, Depends
from contextlib import asynccontextmanager
from fastapi_redis_dep.redis import RedisRegistry, depends_redis


@asynccontextmanager
async def lifespan(app: FastAPI):
    await RedisRegistry.register_redis(app)
    yield
    await RedisRegistry.terminate(app)


app = FastAPI(lifespan=lifespan)


@app.get("/")
async def get_redis(redis: RedisDep = Depends(depends_redis)):
    return await redis.client.ping()

此时访问 / 路径，如果返回 True，恭喜你，FastAPI 的 Redis 集成已经完成，接下来你可以使用 Redis 的 API 和本库封装的 API。

Redis 五大数据结构封装类

String 数据结构

对应方法为 redis.string.。

@app.get("/string")
async def string(redis: RedisDependence):
    class TestModel(BaseModel):
        field1: str
        field2: int
        field3: bool

    test_data = TestModel(field1="value1", field2=123, field3=True)
    print(await redis.string.set("test", "test1"))
    print(await redis.string.get_cache("test"))
    print(await redis.string.set_cache("test",{"a": 1.0, "b": 2.0, "c": 3.0}))
    print(await redis.string.set_cache("test1", test_data))
    print(await redis.string.get_cache("test1", bind_pydantic_model=TestModel))
    print(await redis.string.set_cache("test1", 1.111))
    print(await redis.string.get("test"))
    print(await redis.string.delete("test", "test2"))
    print(await redis.string.set("test3", 1))
    print(await redis.string.increase("test3", 2))
    print(await redis.string.decrease("test3", 2))
    print(await redis.string.exists("test"))
    print(await redis.string.set("test", "test"))
    print(await redis.string.append("test", "1"))
    print(await redis.string.lens("test"))
    print(await redis.string.set_or_get("test5", "test5"))
    print(await redis.string.mset({
        "test7": 7,
        "test8": 8,
        "test9": 9
    }))
    print(await redis.string.mget("test7", "test8", "test9"))

    return

Hash 数据结构

对应方法为 redis.hash.。

@app.get("/hash")
async def hash(redis: RedisDep = Depends(depends_redis)):
    # 可以通过添加字典的方式引入哈希
    test_data = {
        "field1": "value1",
        "field2": 123,
        "field3": True
    }
    await redis.hash.set_hash("test_hash", test_data)
    print(await redis.hash.get_hash("test_hash"))
    print(await redis.hash.delete_field("test_hash", "field2"))
    print(await redis.hash.exists_field("test_hash", "field1"))
    print(await redis.hash.exists_field("test_hash", "field2"))

    # 同时支持 Pydantic 模型引入哈希
    from pydantic import BaseModel

    class TestModel(BaseModel):
        field1: str
        field2: int
        field3: bool

    test_data = TestModel(field1="value1", field2=123, field3=True)
    await redis.hash.set_hash("test_hash1", test_data)
    print(await redis.hash.get_hash("test_hash1", bind_pydantic_model=TestModel))
    print(await redis.hash.delete_field("test_hash1", "field2"))
    print(await redis.hash.exists_field("test_hash1", "field1"))
    print(await redis.hash.exists_field("test_hash1", "field2"))

List 数据结构

对应方法为 redis.list.。

@app.get("/list")
async def list(redis: RedisDep = Depends(depends_redis)):
    print(await redis.list.lpush("test_list", "a", "b", "c"))
    print(await redis.list.lindex("test_list", 0))
    print(await redis.list.linsert("test_list", "AFTER", "b", "x"))
    print(await redis.list.llen("test_list"))
    print(await redis.list.lpop("test_list"))
    print(await redis.list.lset("test_list", 1, "y"))
    print(await redis.list.rpush("test_list", "d", "e"))
    print(await redis.list.rpop("test_list"))
    print(await redis.list.lrange("test_list", 0, -1))
    print(await redis.list.ltrim("test_list", 0, 1))
    print(await redis.list.lrem("test_list", 1, "y"))

    async for value in redis.list.list_iterator("test_list"):
        print(value)

Set 数据结构

对应方法为 redis.set.。

@app.get("/set")
async def set(redis: RedisDep = Depends(depends_redis)):
    print(await redis.set.add("test_set", "a", "b", "c"))
    print(await redis.set.remove("test_set", "b"))
    print(await redis.set.exists("test_set", "a"))
    print(await redis.set.get_all("test_set"))
    print(await redis.set.length("test_set"))
    print(await redis.set.intersection(["test_set", "another_set"]))
    print(await redis.set.union(["test_set", "another_set"]))
    print(await redis.set.difference(["test_set", "another_set"]))

    # 添加一些数据到 another_set 进行测试
    await redis.set.add("another_set", "b", "c", "d")

    print(await redis.set.intersection(["test_set", "another_set"]))
    print(await redis.set.union(["test_set", "another_set"]))
    print(await redis.set.difference(["test_set", "another_set"]))

Sorted Set 数据结构

对应方法为 redis.zset.。

@app.get("/zset")
async def zset(redis: RedisDep = Depends(depends_redis)):
    print(await redis.zset.add("test_zset", {"a": 1.0, "b": 2.0, "c": 3.0}))
    print(await redis.zset.remove("test_zset", "b"))
    print(await redis.zset.score("test_zset", "a"))
    print(await redis.zset.get_all("test_zset", withscores=True))
    print(await redis.zset.length("test_zset"))
    print(await redis.zset.range_by_score("test_zset", 1.0, 3.1, withscores=True))
    print(await redis.zset.rank("test_zset", "a"))
    print(await redis.zset.rank("test_zset", "c", reverse=True))

缓存的使用

RedisDep 类下的 string 对象提供了缓存的基本操作，如 set_cache 和 get_cache。请注意，这些缓存只能用于基本数据类型和 Python 中的 Pydantic 对象。底层使用 orjson 进行 JSON 序列化，因此能够缓存的对象即为 orjson 支持的对象。同时由于重写了 default 函数，因此也能够缓存 Pydantic 对象。如果要缓存更复杂的对象（例如 Tortoise-ORM 的 ORM 对象），请使用第三方缓存工具如 cashews。

锁机制

@app.get("/lock")
async def lock(redis: RedisDep = Depends(depends_redis)):
    async with redis.lock("test_lock", timeout=10):
        print("locked")

底层使用 redis-lock-py 实现分布式锁。你可以通过 redis-lock-py 创建锁，并使用 redis.client 操作 Redis。

管道

@app.get("/pipeline")
async def pipeline(redis: RedisDep = Depends(depends_redis)):
    async with redis.pipe() as pipe:
        await pipe.set("test_pipeline", "test_pipeline")
        await pipe.get("test")
        print(await pipe.get("test_pipeline"))
        print(await pipe.get("test_pipeline"))

获取 Redis 原生操作

@app.get("/redis")
async def redis(redis: RedisDep = Depends(depends_redis)):
    print(redis.client.get("test"))

上述为原生的操作，在当前封装的函数不满足需求时可以使用，否则建议使用封装后的函数。

设置 Redis 配置

需要引入 RedisSettings 类来配置 Redis 连接参数。RedisSettings的实现如下：

from pydantic import BaseSettings


class RedisSettings(BaseSettings):
    redis_ssl: bool = False
    redis_url: Optional[str] = None
    redis_host: str = 'localhost'
    redis_port: int = 6379
    redis_user: Optional[str] = None
    redis_password: Optional[str] = None
    redis_db: int = 12

    redis_max_connections: Optional[int] = None
    redis_decode_responses: bool = True

    redis_secret: Optional[str] = None

    redis_ttl: int = 3600

    def get_redis_address(self) -> str:
        socket_conn = "redis"

        if self.redis_ssl:
            socket_conn = "rediss"

        if self.redis_url:
            return self.redis_url
        elif self.redis_db:
            return f'{socket_conn}://{self.redis_host}:{self.redis_port}/{self.redis_db}'
        else:
            return f'{socket_conn}://{self.redis_host}:{self.redis_port}'

你可以创建一个 settings.py 文件，然后导入这个类。通过这个类实例化对象，实例化时请传入要修改的配置，可以通过一个方法输出这个对象本身。例如：

def get_redis_settings():
    return RedisSettings(
        redis_url=redis_url,
        redis_host=redis_host,
        redis_port=redis_port,
        redis_user=redis_user,
        redis_password=redis_password
    )

简写方式

fastapi-redis-dep同时提供了简写方式，需要导入RedisDependence，这是一个注解，通过它你可以快速引入Redis的依赖注入，而无需使用上面的方法。例如：

@app.get("/redis")
async def redis(redis: RedisDependence):
    print(redis.client.get("test"))

在这个例子中，RedisDependence注解用于快速引入Redis的依赖注入，简化了代码。

FastAPI-Redis-Dep (English)

fastapi-redis-dep is a Redis integration library for FastAPI that provides common middleware integration using dependency injection and lifecycle management (inspired by the third-party library fastapi_plugins). This makes it easier to operate Redis within FastAPI.

Installation Method

Using PIP

pip install fastapi-redis-dep

Using POETRY

poetry add fastapi-redis-dep

API Introduction

Quick Start

After installation, you need to register this library in the lifecycle functions of FastAPI. Specifically:

Import the RedisRegistry class.
Define the lifespan function.

from fastapi import FastAPI, Depends
from contextlib import asynccontextmanager
from fastapi_redis_dep.redis import RedisRegistry, depends_redis


@asynccontextmanager
async def lifespan(app: FastAPI):
    await RedisRegistry.register_redis(app)
    yield
    await RedisRegistry.terminate(app)


app = FastAPI(lifespan=lifespan)


@app.get("/")
async def get_redis(redis: RedisDep = Depends(depends_redis)):
    return await redis.client.ping()

At this point, accessing the / path, if it returns True, congratulations! Your FastAPI has successfully integrated with Redis. You can now use Redis APIs and the APIs encapsulated by this library. For more details, see below.

Five Major Redis Data Structure Encapsulation Classes

String Data Structure

Corresponding methods are under redis.string..

@app.get("/string")
async def string(redis: RedisDep = Depends(depends_redis)):
    print(await redis.string.set("test", "test1"))
    print(await redis.string.get_cache("test"))
    print(await redis.string.set_cache("test1", 1.111))
    print(await redis.string.get("test"))
    print(await redis.string.delete("test", "test2"))
    print(await redis.string.set("test3", 1))
    print(await redis.string.increase("test3", 2))
    print(await redis.string.decrease("test3", 2))
    print(await redis.string.exists("test"))
    print(await redis.string.set("test", "test"))
    print(await redis.string.append("test", "1"))
    print(await redis.string.lens("test"))
    print(await redis.string.set_or_get("test5", "test5"))
    print(await redis.string.mset({
        "test7": 7,
        "test8": 8,
        "test9": 9
    }))
    print(await redis.string.mget("test7", "test8", "test9"))

Hash Data Structure

Corresponding methods are under redis.hash..

@app.get("/hash")
async def hash(redis: RedisDep = Depends(depends_redis)):
    # Introduce a hash by adding a dictionary
    test_data = {
        "field1": "value1",
        "field2": 123,
        "field3": True
    }
    await redis.hash.set_hash("test_hash", test_data)
    print(await redis.hash.get_hash("test_hash"))
    print(await redis.hash.delete_field("test_hash", "field2"))
    print(await redis.hash.exists_field("test_hash", "field1"))
    print(await redis.hash.exists_field("test_hash", "field2"))

    # Also supports introducing a hash via Pydantic models
    from pydantic import BaseModel

    class TestModel(BaseModel):
        field1: str
        field2: int
        field3: bool

    test_data = TestModel(field1="value1", field2=123, field3=True)
    await redis.hash.set_hash("test_hash1", test_data)
    print(await redis.hash.get_hash("test_hash1", bind_pydantic_model=TestModel))
    print(await redis.hash.delete_field("test_hash1", "field2"))
    print(await redis.hash.exists_field("test_hash1", "field1"))
    print(await redis.hash.exists_field("test_hash1", "field2"))

List Data Structure

Corresponding methods are under redis.list..

@app.get("/list")
async def list(redis: RedisDep = Depends(depends_redis)):
    print(await redis.list.lpush("test_list", "a", "b", "c"))
    print(await redis.list.lindex("test_list", 0))
    print(await redis.list.linsert("test_list", "AFTER", "b", "x"))
    print(await redis.list.llen("test_list"))
    print(await redis.list.lpop("test_list"))
    print(await redis.list.lset("test_list", 1, "y"))
    print(await redis.list.rpush("test_list", "d", "e"))
    print(await redis.list.rpop("test_list"))
    print(await redis.list.lrange("test_list", 0, -1))
    print(await redis.list.ltrim("test_list", 0, 1))
    print(await redis.list.lrem("test_list", 1, "y"))

    async for value in redis.list.list_iterator("test_list"):
        print(value)

Set Data Structure

Corresponding methods are under redis.set..

@app.get("/set")
async def set(redis: RedisDep = Depends(depends_redis)):
    print(await redis.set.add("test_set", "a", "b", "c"))
    print(await redis.set.remove("test_set", "b"))
    print(await redis.set.exists("test_set", "a"))
    print(await redis.set.get_all("test_set"))
    print(await redis.set.length("test_set"))
    print(await redis.set.intersection(["test_set", "another_set"]))
    print(await redis.set.union(["test_set", "another_set"]))
    print(await redis.set.difference(["test_set", "another_set"]))

    # Add some data to another_set for testing
    await redis.set.add("another_set", "b", "c", "d")

    print(await redis.set.intersection(["test_set", "another_set"]))
    print(await redis.set.union(["test_set", "another_set"]))
    print(await redis.set.difference(["test_set", "another_set"]))

Sorted Set Data Structure

Corresponding methods are under redis.zset..

@app.get("/zset")
async def zset(redis: RedisDep = Depends(depends_redis)):
    print(await redis.zset.add("test_zset", {"a": 1.0, "b": 2.0, "c": 3.0}))
    print(await redis.zset.remove("test_zset", "b"))
    print(await redis.zset.score("test_zset", "a"))
    print(await redis.zset.get_all("test_zset", withscores=True))
    print(await redis.zset.length("test_zset"))
    print(await redis.zset.range_by_score("test_zset", 1.0, 3.1, withscores=True))
    print(await redis.zset.rank("test_zset", "a"))
    print(await redis.zset.rank("test_zset", "c", reverse=True))

Cache Usage

The string object under the RedisDep class provides basic cache operations such as set_cache and get_cache. Note that this cache can only be used for caching primitive data types and Python's Pydantic objects. The underlying implementation uses orjson for JSON serialization, so the objects that can be cached are those that orjson can serialize. Since the default function has been overridden, Pydantic objects can also be cached. If you want to cache more complex objects such as Tortoise-ORM ORM objects, please use third-party caching tools like cashews.

Lock Mechanism

@app.get("/lock")
async def lock(redis: RedisDep = Depends(depends_redis)):
    async with redis.lock("test_lock", timeout=10):
        print("locked")

Under the hood, it uses redis-lock-py, so you can perform lock operations using redis-lock-py. This function essentially does the following:

def lock(self, name: str, timeout: int = 10):
    return RedisLock(self._client, name, blocking_timeout=timeout)

In summary, you can create locks using redis-lock-py and then operate on Redis using the redis.client.

Pipelines

@app.get("/pipeline")
async def pipeline(redis: RedisDep = Depends(depends_redis)):
    async with redis.pipe() as pipe:
        await pipe.set("test_pipeline", "test_pipeline")
        await pipe.get("test")
        print(await pipe.get("test_pipeline"))
        print(await pipe.get("test_pipeline"))

Accessing Native Redis Operations

@app.get("/redis")
async def redis(redis: RedisDep = Depends(depends_redis)):
    print(redis.client.get("test"))

The above represents native operations, which should be used when the current encapsulated functions do not meet your needs; otherwise, it is recommended to use the encapsulated functions.

Setting Redis Configuration

YTo configure Redis connection parameters, you need to introduce the RedisSettings class. The implementation of RedisSettings is as follows:

from pydantic import BaseSettings


class RedisSettings(BaseSettings):
    redis_ssl: bool = False
    redis_url: Optional[str] = None
    redis_host: str = 'localhost'
    redis_port: int = 6379
    redis_user: Optional[str] = None
    redis_password: Optional[str] = None
    redis_db: int = 12

    redis_max_connections: Optional[int] = None
    redis_decode_responses: bool = True

    redis_secret: Optional[str] = None

    redis_ttl: int = 3600

    def get_redis_address(self) -> str:
        socket_conn = "redis"

        if self.redis_ssl:
            socket_conn = "rediss"

        if self.redis_url:
            return self.redis_url
        elif self.redis_db:
            return f'{socket_conn}://{self.redis_host}:{self.redis_port}/{self.redis_db}'
        else:
            return f'{socket_conn}://{self.redis_host}:{self.redis_port}'

You can create a settings.py file and import this class. Instantiate the class with the configurations you want to modify. You can output this object itself through a method. For example:

def get_redis_settings():
    return RedisSettings(
        redis_url=redis_url,
        redis_host=redis_host,
        redis_port=redis_port,
        redis_user=redis_user,
        redis_password=redis_password
    )

Abbreviation Method

fastapi-redis-dep also provides a shorthand way, which requires importing RedisDependence. This is a decorator that allows you to quickly inject Redis dependencies without using the method described above. For example:

@app.get("/redis")
async def redis(redis: RedisDependence):
    print(redis.client.get("test"))

In this example, the RedisDependence decorator is used to quickly inject Redis dependencies, simplifying the code.

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fastapi-redis-dep 0.2.0

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Project description

FastAPI-Redis-Dep（中文）

安装方式

使用 PIP 安装

使用 POETRY 安装

API 介绍

快速开始

Redis 五大数据结构封装类

String 数据结构

Hash 数据结构

List 数据结构

Set 数据结构

Sorted Set 数据结构

缓存的使用

锁机制

管道

获取 Redis 原生操作

设置 Redis 配置

简写方式

FastAPI-Redis-Dep (English)

Installation Method

Using PIP

Using POETRY

API Introduction

Quick Start

Five Major Redis Data Structure Encapsulation Classes

String Data Structure

Hash Data Structure

List Data Structure

Set Data Structure

Sorted Set Data Structure

Cache Usage

Lock Mechanism

Pipelines

Accessing Native Redis Operations

Setting Redis Configuration

Abbreviation Method

Project details

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Maintainers

Unverified details

Meta

Classifiers

Release history Release notifications | RSS feed

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Source Distribution

Built Distribution

File details

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