redis-decorators 1.0.1

Cache function return values automatically with decorators.

Redis Decorators

Redis cache decorators for automatically caching function return values in Python.

Get Started

Install

pip install redis_decorators

Initialize

The main class, RedisCaching, will initialize Redis for you.

from redis_decorators import RedisCaching

caching = RedisCaching('redis://redis:6379')
cache = caching.get_cache()  # Redis instance

Usage

The simplest way to start caching return values is to use one of the RedisCaching.cache_* decorators. The cache key is generated automatically based on the function name and arguments.

@caching.cache_string()
def my_string_function(arg1, arg2):
    # ... do some calculation
    return 'my_value'

Calculate Cache Key

If you want to have control over how cache keys are calculated, you can specify get_cache_key in the following ways:

Lambda function

@caching.cache_string(get_cache_key=lambda arg1: return f'{arg1}-cache-key')
def my_string_function(arg1):
    return 'my_value'

Separate function definition

The decorator returns a wrapper that allows you to define additional properties:

@caching.cache_string()
def my_string_function(arg1):
    return 'my_value'

# The cache key function gets the same arguments as the value function.
@my_string_function.cache_key
def my_string_function_cache_key(arg1):
    return f'{arg1}-cache-key

Key Expiration

You can define an expiration time in seconds or with a datetime.timedelta, similar to how you would define the cache key calculation:

# Define a static expiration time with expire_in kwarg:
@caching.cache_string(expire_in=60)  # Cache key expires in 60 seconds.
def my_string_function(arg1):
    return 'my_value'

# Calculate expiration time with a function:
@caching.cache_string()
def my_string_function(arg1):
    return 'my_value'

@my_string_function.expire_in
def my_string_function_expire_in(arg1):
    # ... calculate seconds or a timedelta
    return datetime.now() - some_other_datetime

Included Value Types

There are decorators already defined for various common datatypes.

Decorator	Wrapped Function Return Type	Redis Set Function	Redis Get Function
RedisCaching.cache_str	str	set	get
RedisCaching.cache_dict_str	str	hset	hget
RedisCaching.cache_dict	dict	hset	hgetall
RedisCaching.cache_list	list	rpush	lrange

You can see how the various datatypes are stored and fetched in cacheable.py.

Special Decorators

All decorators accept the same arguments except for the following:

• RedisCaching.cache_dict_str

This decorator stores a value inside a cached dictionary (a redis hash).

Usage:

@caching.cache_dict_string(dict_key='foo', get_cache_key=lambda arg1: return f'{arg1}-cache-key')
def my_nested_value(arg1):
    return "bar"

In the above example, calling my_nested_value('hello') results in a cached hash with key hello-cache-key and value { 'foo': 'bar' }.

Custom Data Types

You can cache and retrieve any arbitrary data type as long as it can be serialized/transformed into a type that redis supports.

Examples

Cache a decimal.Decimal

This example serializes Decimal objects to strings and coerces fetched values back into Decimal objects.

# Define a custom `CacheElement`
class CacheDecimal(CacheElement[Decimal, str]):
    cacheable: Cacheable[str] = StringCacheable()

    def load(self, value: str) -> Decimal:
        return Decimal(value)

    def dump(self, value: Decimal) -> str:
        return str(value)

# Use the custom CacheElement with RedisCaching.cache_value
@caching.cache_value(CacheDecimal())
def my_decimal_function(arg1):
    return Decimal('1.234')

Cache your own serializable type

If you have a custom data type that is serializable, you can define a custom CacheElement to cache it.

class MyObject:
    def serialize(self):
        # return a string representation

    @classmethod
    def from_str(cls, value):
        # parse value and return a new instance


class CacheMyObject(CacheElement[MyObject, str]):
    cacheable: Cacheable[str] = StringCacheable()

    def load(self, value: str) -> MyObject:
        return Decimal.from_str(value)

    def dump(self, value: MyObject) -> str:
        return value.serialize()

# Use the custom CacheElement with RedisCaching.cache_value
@caching.cache_value(CacheMyObject())
def my_decimal_function(arg1):
    return MyObject()

Note the underlying Cacheable in these examples is StringCacheable. If you want to store your object as a different type, you can use other Cacheable classes to do so. For example, to store your object as a dictionary, you would use the DictCacheable instead of StringCacheable. With DictCacheable, the load function would take a dict object as the value argument and return your object type; the dump function would take your object type as the value argument and return a dict.

See cacheable.py and cache_element.py for examples of Cacheable and CacheElement, respectively.

Advanced Usage

Deferred Init

If your redis config is not available at the time RedisCaching is initialized, you can defer initialization using RedisCaching.init. This use case is common when using web frameworks like Flask or Pyramid, where you may have modules that use cache decorators that get imported before your app configuration is initialized.

You can create a RedisCaching instance without providing a URL or redis config and use decorators throughout your code base. The cache functions will get registered before the connection to redis is made.

Example

Create an instance of RedisCaching in, for instance, extensions.py:

from redis_decorators import RedisCaching
caching = RedisCaching()

Use the cache decorators wherever you need to:

from extensions import caching

@caching.cache_string()
def my_cached_string_function():
    # ...

Initialize your RedisCaching instance after your config has been initialized:

from extensions import caching

def app():
    # initialize app and config, visit view modules, etc.
    caching.init(app.config.REDIS_URL, **app.config.REDIS_CONFIG)

Development

Install dev dependencies

pip install -r requirements.txt

Run tests with pytest

pytest

Run tests with a live redis server

By default, tests are run using fakeredis instead of connecting to a real redis server. To run tests agains a live (local) redis server:

docker compose up -d
pytest --redis-url redis://localhost:6379

Contributing

PRs and issues are always welcome.