retools 0.1

Redis Tools

Redis Tools (retools)

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.

Current tools in retools:

• Caching
• Global Lock

On the horizon for future implementation:

• A worker/job processing system similar to Celery but based on how Ruby’s Resque system works.

Caching

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')

Differences from Beaker

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.

Locking

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 time

License

retools is offered under the MIT license.

Authors

retools is made available by Ben Bangert.

retools

Next release

• Job/Worker system based on Ruby’s Resque model.

0.1 (07/08/2011)

Features

• Caching in a similar style to Beaker, with hit/miss statistics, backed by a Redis global write-lock with old values served to prevent the dogpile effect
• Redis global lock