dogpile provides a locking interface around a “value creation” and
“value retrieval” pair of functions.
Changed in version 0.6.0: The
dogpile package encapsulates the
functionality that was previously provided by the separate
The primary interface is the
Lock object, which provides for
the invocation of the creation function by only one thread and/or process at
a time, deferring all other threads/processes to the “value retrieval” function
until the single creation thread is completed.
Do I Need to Learn the dogpile Core API Directly?¶
It’s anticipated that most users of
dogpile will be using it indirectly
front-end. If you fall into this category, then the short answer is no.
Using the core
dogpile APIs described here directly implies you’re building your own
resource-usage system outside, or in addition to, the one
The primary API dogpile provides is the
Lock object. This object allows for
functions that provide mutexing, value creation, as well as value retrieval.
An example usage is as follows:
from dogpile import Lock, NeedRegenerationException import threading import time # store a reference to a "resource", some # object that is expensive to create. the_resource = [None] def some_creation_function(): # call a value creation function value = create_some_resource() # get creationtime using time.time() creationtime = time.time() # keep track of the value and creation time in the "cache" the_resource = tup = (value, creationtime) # return the tuple of (value, creationtime) return tup def retrieve_resource(): # function that retrieves the resource and # creation time. # if no resource, then raise NeedRegenerationException if the_resource is None: raise NeedRegenerationException() # else return the tuple of (value, creationtime) return the_resource # a mutex, which needs here to be shared across all invocations # of this particular creation function mutex = threading.Lock() with Lock(mutex, some_creation_function, retrieve_resource, 3600) as value: # some function that uses # the resource. Won't reach # here until some_creation_function() # has completed at least once. value.do_something()
some_creation_function() will be called
Lock is first invoked as a context manager. The value returned by this
function is then passed into the
with block, where it can be used
by application code. Concurrent threads which
Lock during this initial period
will be blocked until
Once the creation function has completed successfully the first time,
new calls to
Lock will call
in order to get the current cached value as well as its creation
time; if the creation time is older than the current time minus
an expiration time of 3600, then
will be called again, but only by one thread/process, using the given
mutex object as a source of synchronization. Concurrent threads/processes
Lock during this period will fall through,
and not be blocked; instead, the “stale” value just returned by
retrieve_resource() will continue to be returned until the creation
function has finished.
Lock API is designed to work with simple cache backends
like Memcached. It addresses such issues as:
Values can disappear from the cache at any time, before our expiration time is reached. The
NeedRegenerationExceptionclass is used to alert the
Lockobject that a value needs regeneration ahead of the usual expiration time.
There’s no function in a Memcached-like system to “check” for a key without actually retrieving it. The usage of the
retrieve_resource()function allows that we check for an existing key and also return the existing value, if any, at the same time, without the need for two separate round trips.
The “creation” function used by
Lockis expected to store the newly created value in the cache, as well as to return it. This is also more efficient than using two separate round trips to separately store, and re-retrieve, the object.
Example: Using dogpile directly for Caching¶
The following example approximates Beaker’s “cache decoration” function, to
decorate any function and store the value in Memcached. Note that
normally, we’d just use dogpile.cache here, however for the purposes
of example, we’ll illustrate how the
Lock object is used
We create a Python decorator function called
cached() which will provide
caching for the output of a single function. It’s given the “key” which we’d
like to use in Memcached, and internally it makes usage of
along with a thread based mutex (we’ll see a distributed mutex in the next
import pylibmc import threading import time from dogpile import Lock, NeedRegenerationException mc_pool = pylibmc.ThreadMappedPool(pylibmc.Client("localhost")) def cached(key, expiration_time): """A decorator that will cache the return value of a function in memcached given a key.""" mutex = threading.Lock() def get_value(): with mc_pool.reserve() as mc: value_plus_time = mc.get(key) if value_plus_time is None: raise NeedRegenerationException() # return a tuple (value, createdtime) return value_plus_time def decorate(fn): def gen_cached(): value = fn() with mc_pool.reserve() as mc: # create a tuple (value, createdtime) value_plus_time = (value, time.time()) mc.put(key, value_plus_time) return value_plus_time def invoke(): with Lock(mutex, gen_cached, get_value, expiration_time) as value: return value return invoke return decorate
Using the above, we can decorate any function as:
@cached("some key", 3600) def generate_my_expensive_value(): return slow_database.lookup("stuff")
Lock object will ensure that only one thread at a time performs
slow_database.lookup(), and only every 3600 seconds, unless Memcached has
removed the value, in which case it will be called again as needed.
In particular, dogpile.core’s system allows us to call the memcached get() function at most once per access, instead of Beaker’s system which calls it twice, and doesn’t make us call get() when we just created the value.
For the mutex object, we keep a
threading.Lock object that’s local
to the decorated function, rather than using a global lock. This localizes
the in-process locking to be local to this one decorated function. In the next section,
we’ll see the usage of a cross-process lock that accomplishes this differently.
Using a File or Distributed Lock with Dogpile¶
The examples thus far use a
threading.Lock() object for synchronization.
If our application uses multiple processes, we will want to coordinate creation
operations not just on threads, but on some mutex that other processes can access.
In this example we’ll use a file-based lock as provided by the lockfile package, which uses a unix-symlink
concept to provide a filesystem-level lock (which also has been made
threadsafe). Another strategy may base itself directly off the Unix
os.flock() call, or use an NFS-safe file lock like flufl.lock, and still another approach is to
lock against a cache server, using a recipe such as that described at Using
Memcached as a Distributed Locking Service.
What all of these locking schemes have in common is that unlike the Python
threading.Lock object, they all need access to an actual key which acts as
the symbol that all processes will coordinate upon. So here, we will also
need to create the “mutex” which we pass to
Lock using the
import lockfile import os from hashlib import sha1 # ... other imports and setup from the previous example def cached(key, expiration_time): """A decorator that will cache the return value of a function in memcached given a key.""" lock_path = os.path.join("/tmp", "%s.lock" % sha1(key).hexdigest()) # ... get_value() from the previous example goes here def decorate(fn): # ... gen_cached() from the previous example goes here def invoke(): # create an ad-hoc FileLock mutex = lockfile.FileLock(lock_path) with Lock(mutex, gen_cached, get_value, expiration_time) as value: return value return invoke return decorate
For a given key “some_key”, we generate a hex digest of the key,
lockfile.FileLock() to create a lock against the file
/tmp/53def077a4264bd3183d4eb21b1f56f883e1b572.lock. Any number of
objects in various processes will now coordinate with each other, using this common
filename as the “baton” against which creation of a new value proceeds.
Unlike when we used
threading.Lock, the file lock is ultimately locking
on a file, so multiple instances of
FileLock() will all coordinate on
that same file - it’s often the case that file locks that rely upon
require non-threaded usage, so a unique filesystem lock per thread is often a good
idea in any case.