Metadata-Version: 2.4
Name: cached-property
Version: 2.0.1
Summary: A decorator for caching properties in classes.
Home-page: https://github.com/pydanny/cached-property
Author: Daniel Roy Greenfeld
Author-email: daniel@feldroy.com
License: BSD
Keywords: cached-property
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: AUTHORS.md
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license
Dynamic: license-file
Dynamic: requires-python
Dynamic: summary

# cached-property

[![Github Actions status](https://github.com/pydanny/cached-property/workflows/Python%20package/badge.svg)](https://github.com/pydanny/cached-property/actions)
[![PyPI](https://img.shields.io/pypi/v/cached-property.svg)](https://pypi.python.org/pypi/cached-property)
[![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-000000.svg)](https://github.com/astral-sh/ruff)

A decorator for caching properties in classes.

## Why?

* Makes caching of time or computational expensive properties quick and easy.
* Because I got tired of copy/pasting this code from non-web project to non-web project.
* I needed something really simple that worked in Python 2 and 3.
  (Python 3.8 added a version of this decorator as [`@functools.cached_property`](https://docs.python.org/3.12/library/functools.html#functools.cached_property).)

## How to use it

Let's define a class with an expensive property. Every time you stay there the
price goes up by $50!

```python
class Monopoly:

    def __init__(self):
        self.boardwalk_price = 500

    @property
    def boardwalk(self):
        # In reality, this might represent a database call or time
        # intensive task like calling a third-party API.
        self.boardwalk_price += 50
        return self.boardwalk_price
```

Now run it:

```python
>>> monopoly = Monopoly()
>>> monopoly.boardwalk
550
>>> monopoly.boardwalk
600
```

Let's convert the boardwalk property into a `cached_property`.

```python
from cached_property import cached_property

class Monopoly(object):

    def __init__(self):
        self.boardwalk_price = 500

    @cached_property
    def boardwalk(self):
        # Again, this is a silly example. Don't worry about it, this is
        #   just an example for clarity.
        self.boardwalk_price += 50
        return self.boardwalk_price
```

Now when we run it the price stays at $550.

```python
>>> monopoly = Monopoly()
>>> monopoly.boardwalk
550
>>> monopoly.boardwalk
550
>>> monopoly.boardwalk
550
```

Why doesn't the value of `monopoly.boardwalk` change? Because it's a **cached property**!

## Invalidating the Cache

Results of cached functions can be invalidated by outside forces. Let's demonstrate how to force the cache to invalidate:

```python
>>> monopoly = Monopoly()
>>> monopoly.boardwalk
550
>>> monopoly.boardwalk
550
>>> # invalidate the cache
>>> del monopoly.__dict__['boardwalk']
>>> # request the boardwalk property again
>>> monopoly.boardwalk
600
>>> monopoly.boardwalk
600
```

## Working with Threads

What if a whole bunch of people want to stay at Boardwalk all at once? This means using threads, which
unfortunately causes problems with the standard `cached_property`. In this case, switch to using the
`threaded_cached_property`:

```python
from cached_property import threaded_cached_property

class Monopoly:

    def __init__(self):
        self.boardwalk_price = 500

    @threaded_cached_property
    def boardwalk(self):
        """threaded_cached_property is really nice for when no one waits
            for other people to finish their turn and rudely start rolling
            dice and moving their pieces."""

        sleep(1)
        self.boardwalk_price += 50
        return self.boardwalk_price
```

Now use it:

```python
>>> from threading import Thread
>>> from monopoly import Monopoly
>>> monopoly = Monopoly()
>>> threads = []
>>> for x in range(10):
>>>     thread = Thread(target=lambda: monopoly.boardwalk)
>>>     thread.start()
>>>     threads.append(thread)

>>> for thread in threads:
>>>     thread.join()

>>> self.assertEqual(m.boardwalk, 550)
```

## Working with async/await

The cached property can be async, in which case you have to use await
as usual to get the value. Because of the caching, the value is only
computed once and then cached:

```python
from cached_property import cached_property

class Monopoly:

    def __init__(self):
        self.boardwalk_price = 500

    @cached_property
    async def boardwalk(self):
        self.boardwalk_price += 50
        return self.boardwalk_price
```

Now use it:

```python
>>> async def print_boardwalk():
...     monopoly = Monopoly()
...     print(await monopoly.boardwalk)
...     print(await monopoly.boardwalk)
...     print(await monopoly.boardwalk)
>>> import asyncio
>>> asyncio.get_event_loop().run_until_complete(print_boardwalk())
550
550
550
```

Note that this does not work with threading either, most asyncio
objects are not thread-safe. And if you run separate event loops in
each thread, the cached version will most likely have the wrong event
loop. To summarize, either use cooperative multitasking (event loop)
or threading, but not both at the same time.

## Timing out the cache

Sometimes you want the price of things to reset after a time. Use the `ttl`
versions of `cached_property` and `threaded_cached_property`.

```python
import random
from cached_property import cached_property_with_ttl

class Monopoly(object):

    @cached_property_with_ttl(ttl=5) # cache invalidates after 5 seconds
    def dice(self):
        # I dare the reader to implement a game using this method of 'rolling dice'.
        return random.randint(2,12)
```

Now use it:

```python
>>> monopoly = Monopoly()
>>> monopoly.dice
10
>>> monopoly.dice
10
>>> from time import sleep
>>> sleep(6) # Sleeps long enough to expire the cache
>>> monopoly.dice
3
>>> monopoly.dice
3
```

**Note:** The `ttl` tools do not reliably allow the clearing of the cache. This
is why they are broken out into seperate tools. See https://github.com/pydanny/cached-property/issues/16.

## Credits

* Pip, Django, Werkzeug, Bottle, Pyramid, and Zope for having their own implementations. This package originally used an implementation that matched the Bottle version.
* Reinout Van Rees for pointing out the `cached_property` decorator to me.
* My awesome wife [@audreyfeldroy](https://github.com/audreyfeldroy) who created [`cookiecutter`](https://github.com/cookiecutter/cookiecutter), which meant rolling this out took me just 15 minutes.
* @tinche for pointing out the threading issue and providing a solution.
* @bcho for providing the time-to-expire feature



# History

## 2.0.1 (2024-10-25)

* Via `python_requires` specifies that cached_property is for Python version 3.8 or higher
* Officiall drop support for Python 2.6

## 2.0.0 (2024-10-25)

* Remove support for Python versions < 3.8
* Add formal support for Python versions up to 3.13
* Switch to Markdown for docs
* Migrate from black to ruff

## 1.5.2 (2020-09-21)

* Add formal support for Python 3.8
* Remove formal support for Python 3.4
* Switch from Travis to GitHub actions
* Made tests pass flake8 for Python 2.7

## 1.5.1 (2018-08-05)

* Added formal support for Python 3.7
* Removed formal support for Python 3.3

## 1.4.3  (2018-06-14)

* Catch SyntaxError from asyncio import on older versions of Python, thanks to @asottile

## 1.4.2 (2018-04-08)

* Really fixed tests, thanks to @pydanny

## 1.4.1 (2018-04-08)

* Added conftest.py to manifest so tests work properly off the tarball, thanks to @dotlambda
* Ensured new asyncio tests didn't break Python 2.7 builds on Debian, thanks to @pydanny
* Code formatting via black, thanks to @pydanny and @ambv

## 1.4.0 (2018-02-25)

* Added asyncio support, thanks to @vbraun
* Remove Python 2.6 support, whose end of life was 5 years ago, thanks to @pydanny

## 1.3.1 (2017-09-21)

* Validate for Python 3.6

## 1.3.0 (2015-11-24)

* Drop some non-ASCII characters from HISTORY.rst, thanks to @AdamWill
* Added official support for Python 3.5, thanks to @pydanny and @audreyr
* Removed confusingly placed lock from example, thanks to @ionelmc
* Corrected invalidation cache documentation, thanks to @proofit404
* Updated to latest Travis-CI environment, thanks to @audreyr

## 1.2.0 (2015-04-28)

* Overall code and test refactoring, thanks to @gsakkis
* Allow the del statement for resetting cached properties with ttl instead of del obj._cache[attr], thanks to @gsakkis.
* Uncovered a bug in PyPy, https://bitbucket.org/pypy/pypy/issue/2033/attributeerror-object-attribute-is-read, thanks to @gsakkis
* Fixed threaded_cached_property_with_ttl to actually be thread-safe, thanks to @gsakkis

## 1.1.0 (2015-04-04)

* Regression: As the cache was not always clearing, we've broken out the time to expire feature to its own set of specific tools, thanks to @pydanny
* Fixed typo in README, thanks to @zoidbergwill

## 1.0.0 (2015-02-13)

* Added timed to expire feature to `cached_property` decorator.
* **Backwards incompatiblity**: Changed `del monopoly.boardwalk` to `del monopoly['boardwalk']` in order to support the new TTL feature.

## 0.1.5 (2014-05-20)

* Added threading support with new `threaded_cached_property` decorator
* Documented cache invalidation
* Updated credits
* Sourced the bottle implementation

## 0.1.4 (2014-05-17)

* Fix the dang-blarged py_modules argument.

## 0.1.3 (2014-05-17)

* Removed import of package into `setup.py`

## 0.1.2 (2014-05-17)

* Documentation fixes. Not opening up a RTFD instance for this because it's so simple to use.

## 0.1.1 (2014-05-17)

* setup.py fix. Whoops!

## 0.1.0 (2014-05-17)

* First release on PyPI.
