retools is a package of Redis tools. It's aim is to
provide a variety of Redis backed Python tools that are always 100% unit
tested, fast, efficient, and utilize the capabilities of Redis.
retools is available on PyPI at https://pypi.python.org/pypi/retools
Current tools in retools:
- Caching
- Global Lock
- Queues - A worker/job processing system similar to Celery but based on how Ruby's Resque system works.
A high performance caching system that can act as a drop-in replacement for Beaker's caching. Unlike Beaker's caching, this utilizes Redis for distributed write-locking dogpile prevention. It also collects hit/miss cache statistics along with recording what regions are used by which functions and arguments.
Example:
from retools.cache import CacheRegion, cache_region, invalidate_function
CacheRegion.add_region('short_term', expires=3600)
@cache_region('short_term')
def slow_function(*search_terms):
# Do a bunch of work
return results
my_results = slow_function('bunny')
# Invalidate the cache for 'bunny'
invalidate_function(slow_function, [], 'bunny')Unlike Beaker's caching system, this is built strictly for Redis. As such, it adds several features that Beaker doesn't possess:
- A distributed write-lock so that only one writer updates the cache at a time across a cluster.
- Hit/Miss cache statistics to give you insight into what caches are less effectively utilized (and may need either higher expiration times, or just not very worthwhile to cache).
- Very small, compact code-base with 100% unit test coverage.
A Redis based lock implemented as a Python context manager, based on Chris Lamb's example.
Example:
from retools.lock import Lock
with Lock('a_key', expires=60, timeout=10):
# do something that should only be done one at a timeretools is offered under the MIT license.
retools is made available by Ben Bangert.