Persistent, stale-free, local and cross-machine caching for Python functions.
Cachier
#######
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Persistent, stale-free, local and cross-machine caching for Python functions.
… code-block:: python
from cachier import cachier
import datetime
@cachier(stale_after=datetime.timedelta(days=3))
def foo(arg1, arg2):
“”“foo now has a persistent cache, triggering recalculation for values stored more than 3 days.”“”
return {‘arg1’: arg1, ‘arg2’: arg2}
… role:: python(code)
:language: python
… contents::
… section-numbering:
Install cachier with:
… code-block:: python
pip install cachier
For the latest version supporting Python 2.7 please use:
… code-block:: python
pip install 'cachier==1.2.8'
tested on Linux, OS X and Windows <https://travis-ci.org/shaypal5/cachier>_.Cachier is NOT:
Cache replacement policies <https://en.wikipedia.org/wiki/Cache_replacement_policies>_Cachier provides a decorator which you can wrap around your functions to give them a persistent cache. The positional and keyword arguments to the wrapped function must be hashable (i.e. Python’s immutable built-in objects, not mutable containers). Also, notice that since objects which are instances of user-defined classes are hashable but all compare unequal (their hash value is their id), equal objects across different sessions will not yield identical keys.
You can add a default, pickle-based, persistent cache to your function - meaning it will last across different Python kernels calling the wrapped function - by decorating it with the cachier decorator (notice the ()!).
… code-block:: python
from cachier import cachier
@cachier()
def foo(arg1, arg2):
“”“Your function now has a persistent cache mapped by argument values!”“”
return {‘arg1’: arg1, ‘arg2’: arg2}
Class and object methods can also be cached. Cachier will automatically ignore the self parameter when determining the cache key for an object method. This means that methods will be cached across all instances of an object, which may not be what you want.
… code-block:: python
from cachier import cachier
class Foo():
@staticmethod
@cachier()
def good_static_usage(arg_1, arg_2):
return arg_1 + arg_2
# Instance method does not depend on object's internal state, so good to cache
@cachier()
def good_usage_1(self, arg_1, arg_2):
return arg_1 + arg_2
# Instance method is calling external service, probably okay to cache
@cachier()
def good_usage_2(self, arg_1, arg_2):
result = self.call_api(arg_1, arg_2)
return result
# Instance method relies on object attribute, NOT good to cache
@cachier()
def bad_usage(self, arg_1, arg_2):
return arg_1 + arg_2 + self.arg_3
The Cachier wrapper adds a clear_cache() function to each wrapped function. To reset the cache of the wrapped function simply call this method:
… code-block:: python
foo.clear_cache()
Global Defaults
Settings can be globally configured across all Cachier wrappers through the use of the `set_default_params` function. This function takes the same keyword parameters as the ones defined in the decorator, which can be passed all at once or with multiple calls. Parameters given directly to a decorator take precedence over any values set by this function.
The following parameters will only be applied to decorators defined after `set_default_params` is called:
* `hash_func`
* `backend`
* `mongetter`
* `cache_dir`
* `pickle_reload`
* `separate_files`
* `entry_size_limit`
These parameters can be changed at any time and they will apply to all decorators:
* `allow_none`
* `caching_enabled`
* `stale_after`
* `next_time`
* `wait_for_calc_timeout`
* `cleanup_stale`
* `cleanup_interval`
The current defaults can be fetched by calling `get_default_params`.
Threads Limit
~~~~~~~~~~~~~
To limit the number of threads Cachier is allowed to spawn, set the ``CACHIER_MAX_WORKERS`` with the desired number. The default is 8, so to enable Cachier to spawn even more threads, you'll have to set a higher limit explicitly.
Global Enable/Disable
---------------------
Caching can be turned off across all decorators by calling `disable_caching`, and then re-activated by calling `enable_caching`.
These functions are convenience wrappers around the `caching_enabled` default setting.
Cache Shelf Life
----------------
Setting Shelf Life
You can set any duration as the shelf life of cached return values of a function by providing a corresponding timedelta object to the stale_after parameter:
… code-block:: python
import datetime
@cachier(stale_after=datetime.timedelta(weeks=2))
def bar(arg1, arg2):
return {‘arg1’: arg1, ‘arg2’: arg2}
Now when a cached value matching the given arguments is found the time of its calculation is checked; if more than stale_after time has since passed, the function will be run again for the same arguments and the new value will be cached and returned.
This is useful for lengthy calculations that depend on a dynamic data source.
Fuzzy Shelf Life
Sometimes you may want your function to trigger a calculation when it encounters a stale result, but still not wait on it if it's not that critical. In that case, you can set ``next_time`` to ``True`` to have your function trigger a recalculation **in a separate thread**, but return the currently cached stale value:
.. code-block:: python
@cachier(next_time=True)
Further function calls made while the calculation is being performed will not trigger redundant calculations.
Automatic Cleanup of Stale Values
Setting cleanup_stale=True on a decorator will spawn a background thread that periodically removes stale cache entries. The interval between cleanup runs is controlled by cleanup_interval and defaults to one day.
… code-block:: python
@cachier(stale_after=timedelta(seconds=30), cleanup_stale=True)
def compute():
…
As mentioned above, the positional and keyword arguments to the wrapped function must be hashable (i.e. Python’s immutable built-in objects, not mutable containers). To get around this limitation the hash_func parameter of the cachier decorator can be provided with a callable that gets the args and kwargs from the decorated function and returns a hash key for them.
… code-block:: python
def calculate_hash(args, kwds):
key = … # compute a hash key here based on arguments
return key
@cachier(hash_func=calculate_hash)
def calculate_super_complex_stuff(custom_obj):
# amazing code goes here
See here for an example:
Question: How to work with unhashable arguments <https://github.com/python-cachier/cachier/issues/91>_
If you want to load a value into the cache without calling the underlying function, this can be done with the precache_value function.
… code-block:: python
@cachier()
def add(arg1, arg2):
return arg1 + arg2
add.precache_value(2, 2, value_to_cache=5)
result = add(2, 2)
print(result) # prints 5
Cachier also accepts several keyword arguments in the calls of the function it wraps rather than in the decorator call, allowing you to modify its behaviour for a specific function call.
Max Age (max_age)
You can specify a maximum allowed age for a cached value on a per-call basis using the `max_age` keyword argument. If the cached value is older than this threshold, a recalculation is triggered. This is in addition to the `stale_after` parameter set at the decorator level; the strictest (smallest) threshold is enforced.
.. code-block:: python
from datetime import timedelta
from cachier import cachier
@cachier(stale_after=timedelta(days=3))
def add(a, b):
return a + b
# Use a per-call max age:
result = add(1, 2, max_age=timedelta(seconds=10)) # Only use cache if value is <10s old
**How it works:**
- The effective max age threshold is the minimum of `stale_after` (from the decorator) and `max_age` (from the call).
- If the cached value is older than this threshold, a new calculation is triggered and the cache is updated.
- If not, the cached value is returned as usual.
Entry Size Limit
~~~~~~~~~~~~~~~~
You can prevent very large return values from being cached by specifying
``entry_size_limit`` on the decorator. Values larger than this limit are
returned but not stored. The limit accepts an integer number of bytes or a
human readable string like ``"200MB"``.
.. code-block:: python
@cachier(entry_size_limit="10KB")
def load_data():
...
When ``cachier__verbose=True`` is passed to a call that returns a value
exceeding the limit, an informative message is printed.
Ignore Cache
~~~~~~~~~~~~
You can have ``cachier`` ignore any existing cache for a specific function call by passing ``cachier__skip_cache=True`` to the function call. The cache will neither be checked nor updated with the new return value.
.. code-block:: python
@cachier()
def sum(first_num, second_num):
return first_num + second_num
def main():
print(sum(5, 3, cachier__skip_cache=True))
Overwrite Cache
~~~~~~~~~~~~~~~
You can have ``cachier`` overwrite an existing cache entry - if one exists - for a specific function call by passing ``cachier__overwrite_cache=True`` to the function call. The cache will not be checked but will be updated with the new return value.
Verbose Cache Call
~~~~~~~~~~~~~~~~~~
You can have ``cachier`` print out a detailed explanation of the logic of a specific call by passing ``cachier__verbose=True`` to the function call. This can be useful if you are not sure why a certain function result is, or is not, returned.
Cache `None` Values
~~~~~~~~~~~~~~~~~~~
By default, ``cachier`` does not cache ``None`` values. You can override this behaviour by passing ``allow_none=True`` to the function call.
Cachier Cores
=============
Pickle Core
-----------
The default core for Cachier is pickle based, meaning each function will store its cache in a separate pickle file in the ``~/.cachier`` directory. Naturally, this kind of cache is both machine-specific and user-specific.
You can configure ``cachier`` to use another directory by providing the ``cache_dir`` parameter with the path to that directory:
.. code-block:: python
@cachier(cache_dir='~/.temp/.cache')
You can slightly optimise pickle-based caching if you know your code will only be used in a single thread environment by setting:
.. code-block:: python
@cachier(pickle_reload=False)
This will prevent reading the cache file on each cache read, speeding things up a bit, while also nullifying inter-thread functionality (the code is still thread safe, but different threads will have different versions of the cache at times, and will sometime make unnecessary function calls).
Setting the optional argument ``separate_files`` to ``True`` will cause the cache to be stored in several files: A file per argument set, per function. This can help if your per-function cache files become too large.
.. code-block:: python
from cachier import cachier
@cachier(separate_files=True)
def foo(arg1, arg2):
"""Your function now has a persistent cache mapped by argument values, split across several files, per argument set"""
return {'arg1': arg1, 'arg2': arg2}
You can get the fully qualified path to the directory of cache files used by ``cachier`` (``~/.cachier`` by default) by calling the ``cache_dpath()`` function:
.. code-block:: python
>>> foo.cache_dpath()
"/home/bigus/.cachier/"
MongoDB Core
------------
You can set a MongoDB-based cache by assigning ``mongetter`` with a callable that returns a ``pymongo.Collection`` object with writing permissions:
.. code-block:: python
from pymongo import MongoClient
def my_mongetter():
client = MongoClient(get_cachier_db_auth_uri())
db_obj = client['cachier_db']
if 'someapp_cachier_db' not in db_obj.list_collection_names():
db_obj.create_collection('someapp_cachier_db')
return db_obj['someapp_cachier_db']
@cachier(mongetter=my_mongetter)
This allows you to have a cross-machine, albeit slower, cache. This functionality requires that the installation of the ``pymongo`` python package.
In certain cases the MongoDB backend might leave a deadlock behind, blocking all subsequent requests from being processed. If you encounter this issue, supply the ``wait_for_calc_timeout`` with a reasonable number of seconds; calls will then wait at most this number of seconds before triggering a recalculation.
.. code-block:: python
@cachier(mongetter=False, wait_for_calc_timeout=2)
Memory Core
-----------
You can set an in-memory cache by assigning the ``backend`` parameter with ``'memory'``:
.. code-block:: python
@cachier(backend='memory')
Note, however, that ``cachier``'s in-memory core is simple, and has no monitoring or cap on cache size, and can thus lead to memory errors on large return values - it is mainly intended to be used with future multi-core functionality. As a rule, Python's built-in ``lru_cache`` is a much better stand-alone solution.
SQLAlchemy (SQL) Core
---------------------
**Note:** The SQL core requires SQLAlchemy to be installed. It is not installed by default with cachier. To use the SQL backend, run::
pip install SQLAlchemy
Cachier supports a generic SQL backend via SQLAlchemy, allowing you to use SQLite, PostgreSQL, MySQL, and other databases.
**Usage Example (SQLite in-memory):**
.. code-block:: python
from cachier import cachier
@cachier(backend="sql", sql_engine="sqlite:///:memory:")
def my_func(x):
return x * 2
**Usage Example (PostgreSQL):**
.. code-block:: python
@cachier(backend="sql", sql_engine="postgresql://user:pass@localhost/dbname")
def my_func(x):
return x * 2
**Usage Example (MySQL):**
.. code-block:: python
@cachier(backend="sql", sql_engine="mysql+pymysql://user:pass@localhost/dbname")
def my_func(x):
return x * 2
Redis Core
----------
**Note:** The Redis core requires the redis package to be installed. It is not installed by default with cachier. To use the Redis backend, run::
pip install redis
Cachier supports Redis-based caching for high-performance scenarios. Redis provides fast in-memory storage with optional persistence.
**Usage Example (Local Redis):**
.. code-block:: python
import redis
from cachier import cachier
# Create Redis client
redis_client = redis.Redis(host='localhost', port=6379, db=0)
@cachier(backend="redis", redis_client=redis_client)
def my_func(x):
return x * 2
**Usage Example (Redis with custom key prefix):**
.. code-block:: python
import redis
from cachier import cachier
redis_client = redis.Redis(host='localhost', port=6379, db=0)
@cachier(backend="redis", redis_client=redis_client, key_prefix="myapp")
def my_func(x):
return x * 2
**Usage Example (Redis with callable client):**
.. code-block:: python
import redis
from cachier import cachier
def get_redis_client():
return redis.Redis(host='localhost', port=6379, db=0)
@cachier(backend="redis", redis_client=get_redis_client)
def my_func(x):
return x * 2
**Configuration Options:**
- ``sql_engine``: SQLAlchemy connection string, Engine, or callable returning an Engine.
- All other standard cachier options are supported.
**Table Schema:**
- ``function_id``: Unique identifier for the cached function
- ``key``: Cache key
- ``value``: Pickled result
- ``timestamp``: Datetime of cache entry
- ``stale``: Boolean, is value stale
- ``processing``: Boolean, is value being calculated
- ``completed``: Boolean, is value calculation completed
**Limitations & Notes:**
- Requires SQLAlchemy (install with ``pip install SQLAlchemy``)
- For production, use a persistent database (not ``:memory:``)
- Thread/process safety is handled via transactions and row-level locks
- Value serialization uses ``pickle``. **Warning:** `pickle` can execute arbitrary code during deserialization if the cache database is compromised. Ensure the cache is stored securely and consider using safer serialization methods like `json` if security is a concern.
- For best performance, ensure your DB supports row-level locking
Contributing
============
Current maintainers are Shay Palachy Affek (`[email protected] <mailto:[email protected]>`_, `@shaypal5 <https://github.com/shaypal5>`_) and `Jirka Borovec <https://github.com/Borda>`_ (`@Borda <https://github.com/Borda>`_ on GitHub); You are more than welcome to approach them for help. Contributions are very welcomed! :)
Installing for development
--------------------------
Clone:
.. code-block:: bash
git clone [email protected]:python-cachier/cachier.git
Install in development mode with test dependencies for local cores (memory and pickle) only:
.. code-block:: bash
cd cachier
pip install -e . -r tests/requirements.txt
Each additional core (MongoDB, Redis, SQL) requires additional dependencies. To install all dependencies for all cores, run:
.. code-block:: bash
pip install -r tests/mongodb_requirements.txt
pip install -r tests/redis_requirements.txt
pip install -r tests/sql_requirements.txt
Running the tests
-----------------
To run the tests, call the ``pytest`` command in the repository's root, or:
.. code-block:: bash
python -m pytest
To run only MongoDB core related tests, use:
.. code-block:: bash
pytest -m mongo
To run only memory core related tests, use:
.. code-block:: bash
pytest -m memory
To run all tests EXCEPT MongoDB core related tests, use:
.. code-block:: bash
pytest -m "not mongo"
To run all tests EXCEPT memory core AND MongoDB core related tests, use:
.. code-block:: bash
pytest -m "not (mongo or memory)"
Running MongoDB tests against a live MongoDB instance
-----------------------------------------------------
**Note to developers:** By default, all MongoDB tests are run against a mocked MongoDB instance, provided by the ``pymongo_inmemory`` package. To run them against a live MongoDB instance, you now have several options:
**Option 1: Using the test script (recommended)**
.. code-block:: bash
# Test MongoDB only
./scripts/test-local.sh mongo
# Test MongoDB with local backends
./scripts/test-local.sh mongo memory pickle
This script automatically handles Docker container lifecycle, environment variables, and cleanup. Additional options:
- ``-v, --verbose``: Show verbose output
- ``-k, --keep-running``: Keep containers running after tests
- ``-h, --html-coverage``: Generate HTML coverage report
**Option 2: Using Make**
.. code-block:: bash
make test-mongo-local # Run MongoDB tests with Docker
make test-all-local # Run all backends with Docker
make test-mongo-inmemory # Run with in-memory MongoDB (default)
**Option 3: Manual setup**
.. code-block:: bash
# Start MongoDB with Docker
docker run -d -p 27017:27017 --name cachier-test-mongo mongo:latest
# Run tests
CACHIER_TEST_HOST=localhost CACHIER_TEST_PORT=27017 CACHIER_TEST_VS_DOCKERIZED_MONGO=true pytest -m mongo
# Clean up
docker stop cachier-test-mongo && docker rm cachier-test-mongo
**CI Environment:** The ``CACHIER_TEST_VS_DOCKERIZED_MONGO`` environment variable is set to ``True`` in the GitHub Actions CI environment, which runs tests against a real MongoDB instance on every commit and pull request.
Contributors are encouraged to test against a real MongoDB instance before submitting PRs to ensure compatibility, though the in-memory MongoDB instance serves as a good proxy for most development.
**HOWEVER, the tests run against a live MongoDB instance when you submit a PR are the determining tests for deciding whether your code functions correctly against MongoDB.**
Testing all backends locally
-----------------------------
To test all cachier backends (MongoDB, Redis, SQL, Memory, Pickle) locally with Docker:
.. code-block:: bash
# Test all backends at once
./scripts/test-local.sh all
# Test only external backends (MongoDB, Redis, SQL)
./scripts/test-local.sh external
# Test specific combinations
./scripts/test-local.sh mongo redis
# Keep containers running for debugging
./scripts/test-local.sh all -k
# Test specific test files with selected backends
./scripts/test-local.sh mongo -f tests/test_mongo_core.py
# Test multiple files across all backends
./scripts/test-local.sh all -f tests/test_main.py -f tests/test_redis_core_coverage.py
The unified test script automatically manages Docker containers, installs required dependencies, and runs the appropriate test suites. The ``-f`` / ``--files`` option allows you to run specific test files instead of the entire test suite. See ``scripts/README-local-testing.md`` for detailed documentation.
Running pre-commit hooks locally
--------------------------------
After you've installed test dependencies, you can run pre-commit hooks locally by using the following command:
.. code-block:: bash
pre-commit run --all-files
Adding documentation
--------------------
This project is documented using the `numpy docstring conventions`_, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings (in my personal opinion, of course). When documenting code you add to this project, please follow `these conventions`_.
.. _`numpy docstring conventions`: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt
.. _`these conventions`: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt
Additionally, if you update this ``README.rst`` file, use ``python setup.py checkdocs`` to validate it compiles.
Credits
=======
Created by `Shay Palachy Affek <https://github.com/shaypal5>`_ ([email protected]), which currently assists in maintenance.
Current lead developer/contributor: `Jirka Borovec <https://github.com/Borda>`_ (`@Borda <https://github.com/Borda>`_ on GitHub).
Other major contributors:
* `Jirka Borovec <https://github.com/Borda>`_ - Arg order independence, args-to-kwargs for less unique keys and numerous development and CI contributions.
* `Judson Neer <https://github.com/lordjabez>`_ - Precaching, method caching support and numerous improvements and bugfixes.
* `cthoyt <https://github.com/cthoyt>`_ - Base memory core implementation.
* `amarczew <https://github.com/amarczew>`_ - The ``hash_func`` kwarg.
* `non-senses <https://github.com/non-senses>`_ - The ``wait_for_calc_timeout`` kwarg.
* `Elad Rapaport <https://github.com/erap129>`_ - Multi-file Pickle core, a.k.a ``separate_files`` (released on ``v1.5.3``).
* `John Didion <https://github.com/jdidion>`_ - Support for pickle-based caching for cases where two identically-named methods of different classes are defined in the same module.
Notable bugfixers:
* `MichaelRazum <https://github.com/MichaelRazum>`_.
* `Eric Ma <https://github.com/ericmjl>`_ - The iNotify bugfix (released on ``v1.5.3``).
* `Ofir <https://github.com/ofirnk>`_ - The iNotify bugfix (released on ``v1.5.3``).
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.. links:
.. _pymongo: https://api.mongodb.com/python/current/
.. _watchdog: https://github.com/gorakhargosh/watchdog