Cache

Warning

Deprecated since version 0.15: This will be removed in version 1.0. It has been extracted tocachelib.

werkzeug.contrib.cache

The main problem with dynamic Web sites is, well, they’re dynamic. Eachtime a user requests a page, the webserver executes a lot of code, queriesthe database, renders templates until the visitor gets the page he sees.

This is a lot more expensive than just loading a file from the file systemand sending it to the visitor.

For most Web applications, this overhead isn’t a big deal but once itbecomes, you will be glad to have a cache system in place.

How Caching Works

Caching is pretty simple. Basically you have a cache object lurking aroundsomewhere that is connected to a remote cache or the file system orsomething else. When the request comes in you check if the current pageis already in the cache and if so, you’re returning it from the cache.Otherwise you generate the page and put it into the cache. (Or a fragmentof the page, you don’t have to cache the full thing)

Here is a simple example of how to cache a sidebar for 5 minutes:

def get_sidebar(user):
    identifier = 'sidebar_for/user%d' % user.id
    value = cache.get(identifier)
    if value is not None:
        return value
    value = generate_sidebar_for(user=user)
    cache.set(identifier, value, timeout=60 * 5)
    return value

Creating a Cache Object

To create a cache object you just import the cache system of your choicefrom the cache module and instantiate it. Then you can start workingwith that object:

>>> from werkzeug.contrib.cache import SimpleCache
>>> c = SimpleCache()
>>> c.set("foo", "value")
>>> c.get("foo")
'value'
>>> c.get("missing") is None
True

Please keep in mind that you have to create the cache and put it somewhereyou have access to it (either as a module global you can import or you justput it into your WSGI application).

Cache System API

• class werkzeug.contrib.cache.BaseCache(default_timeout=300)
• Baseclass for the cache systems. All the cache systems implement thisAPI or a superset of it.

Parameters:default_timeout – the default timeout (in seconds) that is used ifno timeout is specified on set(). A timeoutof 0 indicates that the cache never expires.

• add(key, value, timeout=None)
• Works like set() but does not overwrite the values of alreadyexisting keys.

Parameters:

- **key** – the key to set
- **value** – the value for the key
- **timeout** – the cache timeout for the key in seconds (if notspecified, it uses the default timeout). A timeout of0 idicates that the cache never expires.Returns:

Same as set(), but also False for alreadyexisting keys.Return type:boolean

• clear()
• Clears the cache. Keep in mind that not all caches supportcompletely clearing the cache.

Returns:Whether the cache has been cleared.Return type:boolean

• dec(key, delta=1)
• Decrements the value of a key by delta. If the key doesnot yet exist it is initialized with -delta.

For supporting caches this is an atomic operation.

Parameters:

- **key** – the key to increment.
- **delta** – the delta to subtract.Returns:

The new value or None for backend errors.

• delete(key)
• Delete key from the cache.

Parameters:key – the key to delete.Returns:Whether the key existed and has been deleted.Return type:boolean

• deletemany(*keys_)
• Deletes multiple keys at once.

Parameters:keys – The function accepts multiple keys as positionalarguments.Returns:Whether all given keys have been deleted.Return type:boolean

• get(key)
• Look up key in the cache and return the value for it.

Parameters:key – the key to be looked up.Returns:The value if it exists and is readable, else None.

• getdict(*keys_)
• Like get_many() but return a dict:

d = cache.get_dict("foo", "bar")
foo = d["foo"]
bar = d["bar"]

Parameters:keys – The function accepts multiple keys as positionalarguments.

• getmany(*keys_)
• Returns a list of values for the given keys.For each key an item in the list is created:

foo, bar = cache.get_many("foo", "bar")

Has the same error handling as get().

Parameters:keys – The function accepts multiple keys as positionalarguments.

• has(key)
• Checks if a key exists in the cache without returning it. This is acheap operation that bypasses loading the actual data on the backend.

This method is optional and may not be implemented on all caches.

Parameters:key – the key to check

• inc(key, delta=1)
• Increments the value of a key by delta. If the key doesnot yet exist it is initialized with delta.

For supporting caches this is an atomic operation.

Parameters:

- **key** – the key to increment.
- **delta** – the delta to add.Returns:

The new value or None for backend errors.

• set(key, value, timeout=None)
• Add a new key/value to the cache (overwrites value, if key alreadyexists in the cache).

Parameters:

- **key** – the key to set
- **value** – the value for the key
- **timeout** – the cache timeout for the key in seconds (if notspecified, it uses the default timeout). A timeout of0 idicates that the cache never expires.Returns:

True if key has been updated, False for backenderrors. Pickling errors, however, will raise a subclass ofpickle.PickleError.Return type:boolean

• setmany(_mapping, timeout=None)
• Sets multiple keys and values from a mapping.

Parameters:

- **mapping** – a mapping with the keys/values to set.
- **timeout** – the cache timeout for the key in seconds (if notspecified, it uses the default timeout). A timeout of0 idicates that the cache never expires.Returns:

Whether all given keys have been set.Return type:boolean

Cache Systems

• class werkzeug.contrib.cache.NullCache(default_timeout=300)
• A cache that doesn’t cache. This can be useful for unit testing.

Parameters:default_timeout – a dummy parameter that is ignored but existsfor API compatibility with other caches.

• class werkzeug.contrib.cache.SimpleCache(threshold=500, default_timeout=300)
• Simple memory cache for single process environments. This class existsmainly for the development server and is not 100% thread safe. It triesto use as many atomic operations as possible and no locks for simplicitybut it could happen under heavy load that keys are added multiple times.

Parameters:

• threshold – the maximum number of items the cache stores beforeit starts deleting some.
• default_timeout – the default timeout that is used if no timeout isspecified on set(). A timeout of0 indicates that the cache never expires.

• class werkzeug.contrib.cache.MemcachedCache(servers=None, default_timeout=300, key_prefix=None)
• A cache that uses memcached as backend.

The first argument can either be an object that resembles the API of amemcache.Client or a tuple/list of server addresses. In theevent that a tuple/list is passed, Werkzeug tries to import the bestavailable memcache library.

This cache looks into the following packages/modules to find bindings formemcached:

• pylibmc
• google.appengine.api.memcached
• memcached
• libmc

Implementation notes: This cache backend works around some limitations inmemcached to simplify the interface. For example unicode keys are encodedto utf-8 on the fly. Methods such as get_dict() returnthe keys in the same format as passed. Furthermore all get methodssilently ignore key errors to not cause problems when untrusted user datais passed to the get methods which is often the case in web applications.

Parameters:

• servers – a list or tuple of server addresses or alternativelya memcache.Client or a compatible client.
• default_timeout – the default timeout that is used if no timeout isspecified on set(). A timeout of0 indicates that the cache never expires.
• key_prefix – a prefix that is added before all keys. This makes itpossible to use the same memcached server for differentapplications. Keep in mind thatclear() will also clear keys with adifferent prefix.

• class werkzeug.contrib.cache.GAEMemcachedCache
• This class is deprecated in favour of MemcachedCache whichnow supports Google Appengine as well.

Changed in version 0.8: Deprecated in favour of MemcachedCache.

• class werkzeug.contrib.cache.RedisCache(host='localhost', port=6379, password=None, db=0, default_timeout=300, key_prefix=None, **kwargs)
• Uses the Redis key-value store as a cache backend.

The first argument can be either a string denoting address of the Redisserver or an object resembling an instance of a redis.Redis class.

Note: Python Redis API already takes care of encoding unicode strings onthe fly.

New in version 0.7.

New in version 0.8: key_prefix was added.

Changed in version 0.8: This cache backend now properly serializes objects.

Changed in version 0.8.3: This cache backend now supports password authentication.

Changed in version 0.10: **kwargs is now passed to the redis object.

Parameters:

• host – address of the Redis server or an object which API iscompatible with the official Python Redis client (redis-py).
• port – port number on which Redis server listens for connections.
• password – password authentication for the Redis server.
• db – db (zero-based numeric index) on Redis Server to connect.
• default_timeout – the default timeout that is used if no timeout isspecified on set(). A timeout of0 indicates that the cache never expires.
• key_prefix – A prefix that should be added to all keys.

Any additional keyword arguments will be passed to redis.Redis.

• class werkzeug.contrib.cache.FileSystemCache(cache_dir, threshold=500, default_timeout=300, mode=384)
• A cache that stores the items on the file system. This cache dependson being the only user of the cache_dir. Make absolutely sure thatnobody but this cache stores files there or otherwise the cache willrandomly delete files therein.

Parameters:

• cache_dir – the directory where cache files are stored.
• threshold – the maximum number of items the cache stores beforeit starts deleting some. A threshold value of 0indicates no threshold.
• default_timeout – the default timeout that is used if no timeout isspecified on set(). A timeout of0 indicates that the cache never expires.
• mode – the file mode wanted for the cache files, default 0600

• class werkzeug.contrib.cache.UWSGICache(default_timeout=300, cache='')
• Implements the cache using uWSGI’s caching framework.

Note

This class cannot be used when running under PyPy, because the uWSGIAPI implementation for PyPy is lacking the needed functionality.

Parameters:

• default_timeout – The default timeout in seconds.
• cache – The name of the caching instance to connect to, forexample: mycache@localhost:3031, defaults to an empty string, whichmeans uWSGI will cache in the local instance. If the cache is in thesame instance as the werkzeug app, you only have to provide the name ofthe cache.