Metadata-Version: 2.4
Name: percache
Version: 0.4.4
Summary: Persistently cache results of callables
Home-page: https://hg.sr.ht/~obensonne/percache
Author: Oben Sonne
Author-email: obensonne@googlemail.com
License: MIT License
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development
Classifier: Intended Audience :: Developers
Description-Content-Type: text/markdown
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: summary

percache
========

*percache* is a Python module to persistently cache results of functions
(or callables in general) using decorators.

It is somehow similar to the [Memoize
Example](http://wiki.python.org/moin/PythonDecoratorLibrary#Memoize)
from the Python Decorator Library but with the advantage that results
are stored *persistently* in a cache. *percache* provides memoization
across multiple invocations of the Python interpreter.

*percache* requires Python 3. Install with `pip install percache`. It
has no dependencies outside the standard library.

[![image](https://builds.sr.ht/~obensonne/percache.svg?search=0.4.4)](https://builds.sr.ht/~obensonne/percache?search=0.4.4)

Example
-------

    >>> import percache
    >>> cache = percache.Cache("/tmp/my-cache")
    >>>
    >>> @cache
    ... def longtask(a, b):
    ...     print("running a long task")
    ...     return a + b
    ...
    >>> longtask(1, 2)
    running a long task
    3
    >>>
    >>> longtask(1, 2)
    3
    >>> cache.close() # writes new cached results to disk

As you can see at the missing output after the second invocation,
`longtask` has been called once only. The second time the result is
retrieved from the cache. *The key feature of this module is that this
works across multiple invocations of the Python interpreter.*

A requirement on the results to cache is that they are
[pickable](http://docs.python.org/library/pickle.html#what-can-be-pickled-and-unpickled).

Each cache file can be used for any number of differently named
callables.

### Alternative back-ends and live synchronization

By default *percache* uses a
[shelve](http://docs.python.org/library/shelve.html) as its cache
back-end. Alternative back-ends may be used if they are given as
dictionary-like objects with a `close()` and `sync()` method:

```python
>>> class FooCache(dict):
...     def sync(self):
...         ...
...     def close(self):
...         ...
>>> fc = FooCache()
>>> cache = percache.Cache(fc, livesync=True)
```

In this example a cache is created in live-sync mode, i.e. results
*immediately* are stored permanently. Normally this happens not until a
cache's `close()` method has been called or until it gets
[finalized](). Note that the live-sync mode may slow down your
percache-decorated functions (though it reduces the risk of "loosing"
results).

Caching details (you should know)
---------------------------------

When caching the result of a callable, a SHA1 hash based on the
callable's name and arguments is used as a key to store the result in
the cache file.

The hash calculation does not work directly with the arguments but with
their *representations*, i.e. the string returned by applying `repr()`.
Argument representations are supposed to differentiate values
sufficiently for the purpose of the function but identically across
multiple invocations of the Python interpreter. By default the built-in
function `repr()` is used to get argument representations. This is just
perfect for basic types, lists, tuples and combinations of them but it
may fail on other types:

```python
>>> repr(42)
42                                  # good
>>> repr(["a", "b", (1, 2L)])
"['a', 'b', (1, 2L)]"               # good
>>> o = object()
>>> repr(o)
'<object object at 0xb769a4f8>'     # bad (address is dynamic)
>>> repr({"a":1,"b":2,"d":4,"c":3})
"{'a': 1, 'c': 3, 'b': 2, 'd': 4}"  # bad (order may change)
>>> class A(object):
...     def __init__(self, a):
...         self.a = a
...
>>> repr(A(36))
'<__main__.A object at 0xb725bb6c>' # bad (A.a not considered)
>>> repr(A(35))
'<__main__.A object at 0xb725bb6c>' # bad (A.a not considered)
```

A *bad* representation is one that is not identically across Python
invocations (all *bad* examples) or one that does not differentiate
values sufficiently (last 2 *bad* examples).

To use such types anyway you can either

1.  implement the type's `__repr__()` method accordingly or
2.  provide a custom representation function using the `repr` keyword of
    the `Cache` constructor.

### Implement the `__repr__()` method

To pass dictionaries to *percache* decorated functions, you could wrap
them in an own dictionary type with a suitable `__repr__()` method:

```python
>>> class mydict(dict):
...     def __repr__(self):
...         items = ["%r: %r" % (k, self[k]) for k in sorted(self)]
...         return "{%s}" % ", ".join(items)
...
>>> repr(mydict({"a":1,"b":2,"d":4,"c":3}))
"{'a': 1, 'b': 2, 'c': 3, 'd': 4}"  # good (always same order)
```

### Provide a custom `repr()` function

The following example shows how to use a custom representation function
to get a suitable argument representation of `file` objects:

```python
>>> def myrepr(arg):
...     if isinstance(arg, file):
...         # return a string with file name and modification time
...         return "%s:%s" % (arg.name, os.fstat(arg.fileno())[8])
...     else:
...         return repr(arg)
...
>>> cache = percache.Cache("/some/path", repr=myrepr)
```

Housekeeping
------------

-   Make sure to delete the cache file whenever the behavior of a cached
    function has changed!
-   To prevent the cache from getting larger and larger you can call the
    `clear()` method of a `Cache` instance. By default it clears *all*
    results from the cache. The keyword `maxage` my be used to specify a
    maximum number of seconds passed since a cached result has been
    *used* the last time. Any result not used (written or accessed) for
    `maxage` seconds gets removed from the cache.

Changes
-------

### Version 0.4.4

-   Project README in markdown

### Version 0.4.3

-   Pin build status badge in README to a specific version.

### Version 0.4.1

-   README fixes.
-   Use twine for PyPi upload.
-   Use builds.sr.ht for CI.

### Version 0.4.0

-   Update docs due to project migration to
    [sourcehut](https://hg.sr.ht/~obensonne/percache).
-   Discontinue Python 2 support.

### Version 0.3.0

-   Support Python 3.3 (next to 2.6 and 2.7)

### Version 0.2.1

-   Add missing README to PyPi package.

### Version 0.2

-   Automatically close (i.e. sync) the cache on finalization.
-   Optionally sync the cache on each change.
-   Support for alternative back-ends (others than
    [shelve](http://docs.python.org/library/shelve.html)).
-   Cache object are callable now, which makes the explicit `check()`
    method obsolete (though the old interface is still supported).

### Version 0.1.1

-   Fix wrong usage age output of command line interface.
-   Meet half way with pylint.

### Version 0.1

-   Initial release
