Beaker 1.3 is juicy caching goodness
Beaker 1.3 is out, actually, its been out for awhile and I’m just not getting around to blogging the fact. It’s a shame I’ve been a bit too busy lately to blog this earlier because in addition to some bug fixes it has some nice new features that make it even easier to use in any Python script/application/framework.
First, to air my dirty laundry, the important bug fixes in Beaker 1.3:
- Fixed bug with (non-cookie-only) sessions not being timed out
- Fixed bug with cookie-only sessions sending cookies when they weren’t supposed to
- Fixed bug with non-auto sessions not properly storing their last accessed time
The worst thing with the first two of these is that they were regressions that snuck in despite unit tests that exercised the code fairly decently. They’re fixed now along with more comprehensive tests to help prevent such regressions occurring again.
Beaker has always had session’s, and caching, but except for Pylons I’ve yet to see anyone actually use Beaker’s caching utility. I’ve seen the SessionMiddleware used in other WSGI based frameworks, but not the caching, which is kind of a shame since it:
- Supports various backends: database, file-based, dbm file-based, memcached
- Has locking code to ensure a single-writer, multiple reader model (This avoids the dreaded dog-pile effect that caching systems such as the one in Django experience!)
For clients that hit the cached function while its already being regenerated, Beaker serves the old copy until the new content is ready. This avoids the dog-pile effect, and keeps the site snappy for as many users as possible. Since the lock used is disk-based though, this does mean you only avoid the effect per machine (unless you’re locking against NFS or a SAN), so if you have 20 machines in a cluster, the worst the dog-pile effect can get is that you’ll have 20 new copies generated and stored.
Now, in Beaker 1.3, to try and encourage its use a bit more, I’ve added a few decorators to make it easier to cache function results. Also with Mike Bayer’s suggestion, there is now cache regions to make it easier to define various caching policy short-cuts.
Cache Regions
Cache regions are just pre-defined sets of cache instructions to make it easier to use with your code. For example many people have a few common types of cache parameters they want to use:
- Long-term, likely to a database back-end (if used in a cluster)
- Short-term, not cached as long, perhaps to memcached
To set these up, just tell Beaker that about the regions you’re going to define, and give them the normal Beaker cache parameters for each region. For example, in this Pylons app, I define 2 cache regions in the INI:
beaker.cache.regions = short_term, long_term
beaker.cache.short_term.type = ext:memcached
beaker.cache.short_term.url = 127.0.0.1:11211
beaker.cache.short_term.expire = 3600
beaker.cache.long_term.type = file
beaker.cache.long_term.expire = 86400
Note: For those wondering about multiple memcached servers, just put them in as the url with a semi-colon separating them.
If you want to use the caching outside of Pylons without middleware (ie, as a plain library), that’s a bit easier now as well:
from beaker.cache import CacheManager
from beaker.util import parse_cache_config_options
cache_opts = {'cache.data_dir’: './cache’,
'cache.type’: 'file’,
'cache.regions’: 'short_term’, 'long_term’,
'cache.short_term.type’: 'ext:memcached’,
'cache.short_term.url’: ’127.0.0.1:11211’,
'cache.short_term.expire’: '3600’,
'cache.long_term.type’: 'file’,
'cache.long_term.expire’: '86400’,
}
cache = CachManager(**parse_cache_config_options(cache_opts))
And your cache instance is now ready to use. Note that using this cache object is thread-safe already, so you just need to keep one around in your framework/app (Can someone using Django explain where you’d keep a reference to this object around so that you could get to it in a Django view?).
New Cache Decorators
To make it easier to use caching in your app, Beaker now includes decorators for use with the cache object. Given the above caching setup, lets assume you want to cache the output of an expensive operation:
# Get that cache object from wherever you put it, maybe its in environ or request?
# In Pylons, this will be: from pylons import cache
from wherever import cache
def regular_function():
# Do some boring stuff
# Cache something
@cache.region('short_term’, 'mysearch’)
def expensive_search(phrase):
# Do lookup with the phrase variable
return something
return expensive_search('frogs’)
The second argument to the region decorator, ‘mysearch’. That isn’t required unless you have two function’s of the same name in the same module, since Beaker records the namespace of the cache using the function name + module + extra args. For those wondering what a Beaker namespace is, its a single cache ‘block’. That is, lets say you wanted to cache 4 versions of the same thing, but change them differently depending on the input parameter. Beaker considers the thing to be a namespace, and the things that change the thing being cached are the cache keys.
Only un-named arguments are allowed on the function being cached. These act as the cache keys so that if the arguments change, a new copy is cached to those arguments. This way you can have multiple versions of the function output cached depending on the argument it was called with.
If you want to use arbitrary cache parameters, use the other decorator:
# Get that cache object from wherever you put it, maybe its in environ or request?
# In Pylons, this will be: from pylons import cache
from wherever import cache
def regular_function():
# Do some boring stuff
# Cache something
@cache.cache('mysearch’, type='file’, expire=3600)
def expensive_search(phrase):
# Do lookup with the phrase variable
return something
return expensive_search('frogs’)
This allows you to toggle the cache options per use as desired.
If there’s anything else I can do to make it easier to use Beaker in your application, be sure to let me know (Yes, I know more docs would help, this blog post was a first attempt to help out on that front, more docs on the way!).
