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A few caching data structures and other lossy things with capped sizes.

Project description

A few caching data structures and other lossy things with capped sizes.

Latest release 20250103:

  • Pull in @cachedmethod from cs.deco for dependency reasons.
  • Move file_based and @file_property from cs.fileutils to cs.cache.

cachedmethod(*da, **dkw)

Decorator to cache the result of an instance or class method and keep a revision counter for changes.

The cached values are stored on the instance (self). The revision counter supports the @revised decorator.

This decorator may be used in 2 modes. Directly:

@cachedmethod
def method(self, ...)

or indirectly:

@cachedmethod(poll_delay=0.25)
def method(self, ...)

Optional keyword arguments:

  • attr_name: the basis name for the supporting attributes. Default: the name of the method.
  • poll_delay: minimum time between polls; after the first access, subsequent accesses before the poll_delay has elapsed will return the cached value. Default: None, meaning the value never becomes stale.
  • sig_func: a signature function, which should be significantly cheaper than the method. If the signature is unchanged, the cached value will be returned. The signature function expects the instance (self) as its first parameter. Default: None, meaning no signature function; the first computed value will be kept and never updated.
  • unset_value: the value to return before the method has been called successfully. Default: None.

If the method raises an exception, this will be logged and the method will return the previously cached value, unless there is not yet a cached value in which case the exception will be reraised.

If the signature function raises an exception then a log message is issued and the signature is considered unchanged.

An example use of this decorator might be to keep a "live" configuration data structure, parsed from a configuration file which might be modified after the program starts. One might provide a signature function which called os.stat() on the file to check for changes before invoking a full read and parse of the file.

Note: use of this decorator requires the cs.pfx module.

Class CachingMapping(cs.resources.MultiOpenMixin, collections.abc.MutableMapping)

A caching front end for another mapping. This is intended as a generic superclass for a proxy to a slower mapping such as a database or remote key value store.

Note that this subclasses MultiOpenMixin to start/stop the worker Thread. Users must enclose use of a CachingMapping in a with statement. If subclasses also subclass MultiOpenMixin their startup_shutdown method needs to also call our startup_shutdown method.

Example:

class Store:
  """ A key value store with a slower backend.
  """
  def __init__(self, mapping:Mapping):
    self.mapping = CachingMapping(mapping)

.....
S = Store(slow_mapping)
with S:
  ... work with S ...

CachingMapping.__init__(self, mapping: Mapping, *, max_size=1024, queue_length=1024, delitem_bg: Optional[Callable[[Any], cs.result.Result]] = None, setitem_bg: Optional[Callable[[Any, Any], cs.result.Result]] = None, missing_fallthrough: bool = False): Initialise the cache.

Parameters:

  • mapping: the backing store, a mapping
  • max_size: optional maximum size for the cache, default 1024
  • queue_length: option size for the queue to the worker, default 1024
  • delitem_bg: optional callable to queue a delete of a key in the backing store; if unset then deleted are serialised in the worker thread
  • setitem_bg: optional callable to queue setting the value for a key in the backing store; if unset then deleted are serialised in the worker thread
  • missing_fallthrough: is true (default False) always fall back to the backing mapping if a key is not in the cache

CachingMapping.flush(self): Wait for outstanding requests in the queue to complete. Return the UNIX time of completion.

CachingMapping.items(self): Generator yielding (k,v) pairs.

CachingMapping.keys(self): Generator yielding the keys.

Class ConvCache(cs.fs.HasFSPath)

A cache for conversions of file contents such as thumbnails or transcoded media, etc. This keeps cached results in a file tree based on a content key, whose default function is cs.hashutils.file_checksum('blake3').

ConvCache.__init__(self, fspath: Optional[str] = None, content_key_func=None): Initialise a ConvCache.

Parameters:

  • fspath: optional base path of the cache, default from ConvCache.DEFAULT_CACHE_BASEPATH; if this does not exist it will be created using os.mkdir
  • content_key_func: optional function to compute a key from the contents of a file, default cs.hashindex.file_checksum (the blake3 hash of the contents)

ConvCache.content_key(self, srcpath): Return a content key for the filesystem path srcpath.

ConvCache.content_subpath(self, srcpath) -> str: Return the content key based subpath component.

This default assumes the content key is a hash code and breaks it hex representation into a 3 level hierarchy such as 'd6/d9/c510785c468c9aa4b7bda343fb79'.

ConvCache.convof(self, srcpath, conv_subpath, conv_func, *, ext=None, force=False) -> str: Return the filesystem path of the cached conversion of srcpath via conv_func.

Parameters:

  • srcpath: the source filesystem path
  • conv_subpath: a name for the conversion which encompasses the salient aspaects such as 'png/64/64' for a 64x64 pixel thumbnail in PNG format
  • conv_func: a callable of the form conv_func(srcpath,dstpath) to convert the contents of srcpath and write the result to the filesystem path dstpath
  • ext: an optional filename extension, default from the first component of conv_subpath
  • force: option flag to require conversion even if the cache has an entry

convof(srcpath, conv_subpath, conv_func, *, ext=None, force=False)

ConvCache.convof using the default cache.

file_based(*da, **dkw)

A decorator which caches a value obtained from a file.

In addition to all the keyword arguments for @cs.cache.cachedmethod, this decorator also accepts the following arguments:

  • attr_name: the name for the associated attribute, used as the basis for the internal cache value attribute
  • filename: the filename to monitor. Default from the ._{attr_name}__filename attribute. This value will be passed to the method as the filename keyword parameter.
  • poll_delay: delay between file polls, default DEFAULT_POLL_INTERVAL.
  • sig_func: signature function used to encapsulate the relevant information about the file; default cs.filestate.FileState({filename}).

If the decorated function raises OSError with errno == ENOENT, this returns None. Other exceptions are reraised.

file_property(*da, **dkw)

A property whose value reloads if a file changes.

Class LRU_Cache

A simple least recently used cache.

Unlike functools.lru_cache this provides on_add and on_remove callbacks.

LRU_Cache.__init__(self, max_size, *, on_add=None, on_remove=None): Initialise the LRU_Cache with maximum size max, additon callback on_add and removal callback on_remove.

LRU_Cache.__delitem__(self, key): Delete the specified key, calling the on_remove callback.

LRU_Cache.__setitem__(self, key, value): Store the item in the cache. Prune if necessary.

LRU_Cache.flush(self): Clear the cache.

LRU_Cache.get(self, key, default=None): Mapping method: get value for key or default.

LRU_Cache.items(self): Items from the cache.

LRU_Cache.keys(self): Keys from the cache.

lru_cache(max_size=None, cache=None, on_add=None, on_remove=None)

Enhanced workalike of @functools.lru_cache.

Release Log

Release 20250103:

  • Pull in @cachedmethod from cs.deco for dependency reasons.
  • Move file_based and @file_property from cs.fileutils to cs.cache.

Release 20241122:

  • convof, ConvCache.convof: new options force=False parameter to bypass existing cache entries.
  • ConvCache.convof: check the conv_subpath using cs.fs.validate_rpath.

Release 20240422.1: ConvCache docstring update.

Release 20240422: New ConvCache and convof: a cache for conversions of file contents such as thumbnails or transcoded media.

Release 20240412:

  • New CachingMapping, a caching front end for another mapping.
  • LRU_Cache: add keys() and items().

Release 20181228: Initial PyPI release.

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