Lock context manager implemented via redis SETNX/BLPOP.

• Free software: BSD license

Interface targeted to be exactly like threading.Lock.

Because we don't want to require users to share the lock instance across processes you will have to give them names. Eg:

conn = StrictRedis()
with redis_lock.Lock(conn, "name-of-the-lock"):
    print("Got the lock. Doing some work ...")
    time.sleep(5)

Eg:

lock = redis_lock.Lock(conn, "name-of-the-lock")
if lock.acquire(blocking=False):
    print("Got the lock.")
else:
    print("Someone else has the lock.")

You can also associate an identifier along with the lock so that it can be retrieved later by the same process, or by a different one. This is useful in cases where the application needs to identify the lock owner (find out who currently owns the lock). Eg:

import socket
host_id = "owned-by-%s" % socket.gethostname()
lock = redis_lock.Lock(conn, "name-of-the-lock", id=host_id)
if lock.acquire(blocking=False):
    print("Got the lock.")
else:
    if lock.get_owner_id() == host_id:
        print("I already acquired this in another process.")
    else:
        print("The lock is held on another machine.")

The dogpile is also known as the thundering herd effect or cache stampede. Here's a pattern to avoid the problem without serving stale data. The work will be performed a single time and every client will wait for the fresh data.

To use this you will need django-redis, however, python-redis-lock provides you a cache backend that has a cache method for your convenience. Just install python-redis-lock like this:

pip install "python-redis-lock[django]"

Now put something like this in your settings:

CACHES = {
    'default': {
        'BACKEND': 'redis_lock.django_cache.RedisCache',
        'LOCATION': '127.0.0.1:6379',
        'OPTIONS': {
            'DB': 1
        }
    }
}

This backend just adds a convenient .lock(name, expire=None) function to django-redis's cache backend.

You would write your functions like this:

from django.core.cache import cache

def function():
    val = cache.get(key)
    if val:
        return val
    else:
        with cache.lock(key):
            val = cache.get(key)
            if val:
                return val
            else:
                # DO EXPENSIVE WORK
                val = ...

                cache.set(key, value)
                return val

In some cases, the lock remains in redis forever (like a server blackout / redis or application crash / an unhandled exception). In such cases, the lock is not removed by restarting the application. One solution is to use the reset_all() function when the application starts:

# On application start/restart
import redis_lock
redis_lock.reset_all()

Alternativelly, you can reset individual locks via the reset method.

Use these carefully, if you understand what you do.

• based on the standard SETNX recipe
• optional expiry
• no spinloops at acquire

redis_lock will use 2 keys for each lock named <name>:

• lock:<name> - a string value for the actual lock
• lock-signal:<name> - a list value for signaling the waiters when the lock is released

This is how it works:

https://python-redis-lock.readthedocs.org/

To run the all tests run:

tox

OS

Any

Runtime

Python 2.6, 2.7, 3.2, 3.3 or PyPy

Services

Redis 2.6.12 or later.

• bbangert/retools - acquire does spinloop
• distributing-locking-python-and-redis - acquire does polling
• cezarsa/redis_lock - acquire does not block
• andymccurdy/redis-py - acquire does spinloop
• mpessas/python-redis-lock - blocks fine but no expiration