# redlock **Repository Path**: mirrors/redlock ## Basic Information - **Project Name**: redlock - **Description**: RedLock 是一个使用 Python 和 Redis 实现的分布式锁 - **Primary Language**: Python - **License**: MIT - **Default Branch**: master - **Homepage**: https://www.oschina.net/p/redlock - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-07-07 - **Last Updated**: 2025-12-06 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ![RedLock logo](https://github.com/glasslion/redlock/raw/master/docs/assets/redlock-small.png) ## RedLock - Distributed locks with Redis and Python [![Build Status](https://travis-ci.org/glasslion/redlock.svg?branch=master)](https://travis-ci.org/glasslion/redlock) This library implements the RedLock algorithm introduced by [@antirez](http://antirez.com/) ### Yet another ... There are already a few redis based lock implementations in the Python world, e.g. [retools](https://github.com/bbangert/retools), [redis-lock](https://pypi.python.org/pypi/redis-lock/0.2.0). However, these libraries can only work with *single-master* redis server. When the Redis master goes down, your application has to face a single point of failure. We can't rely on the master-slave replication, because Redis replication is asynchronous. > This is an obvious race condition with the master-slave replication model : > 1. Client A acquires the lock into the master. > 2. The master crashes before the write to the key is transmitted to the slave. > 3. The slave gets promoted to master. > 4. Client B acquires the lock to the same resource A already holds a lock for. SAFETY VIOLATION! ### A quick introduction to the RedLock algorithm To resolve this problem, the Redlock algorithm assume we have `N` Redis masters. These nodes are totally independent (no replications). In order to acquire the lock, the client will try to acquire the lock in all the N instances sequentially. If and only if the client was able to acquire the lock in the majority (`(N+1)/2`)of the instances, the lock is considered to be acquired. The detailed description of the RedLock algorithm can be found in the Redis documentation: [Distributed locks with Redis](http://redis.io/topics/distlock). ### APIs The `redlock.RedLock` class shares a similar API with the `threading.Lock` class in the Python Standard Library. #### Basic Usage ```python from redlock import RedLock # By default, if no redis connection details are # provided, RedLock uses redis://127.0.0.1:6379/0 lock = RedLock("distributed_lock") lock.acquire() do_something() lock.release() ``` #### With Statement / Context Manager As with `threading.Lock`, `redlock.RedLock` objects are context managers thus support the [With Statement](https://docs.python.org/2/reference/datamodel.html#context-managers). This way is more pythonic and recommended. ```python from redlock import RedLock with RedLock("distributed_lock"): do_something() ``` #### Specify multiple Redis nodes ```python from redlock import RedLock with RedLock("distributed_lock", connection_details=[ {'host': 'xxx.xxx.xxx.xxx', 'port': 6379, 'db': 0}, {'host': 'xxx.xxx.xxx.xxx', 'port': 6379, 'db': 0}, {'host': 'xxx.xxx.xxx.xxx', 'port': 6379, 'db': 0}, {'host': 'xxx.xxx.xxx.xxx', 'port': 6379, 'db': 0}, ] ): do_something() ``` The `connection_details` parameter expects a list of keyword arguments for initializing Redis clients. Other acceptable Redis client arguments can be found on the [redis-py doc](http://redis-py.readthedocs.org/en/latest/#redis.StrictRedis). #### Reuse Redis clients with the RedLockFactory Usually the connection details of the Redis nodes are fixed. `RedLockFactory` can help reuse them, create multiple RedLocks but only initialize the clients once. ```python from redlock import RedLockFactory factory = RedLockFactory( connection_details=[ {'host': 'xxx.xxx.xxx.xxx'}, {'host': 'xxx.xxx.xxx.xxx'}, {'host': 'xxx.xxx.xxx.xxx'}, {'host': 'xxx.xxx.xxx.xxx'}, ]) with factory.create_lock("distributed_lock"): do_something() with factory.create_lock("another_lock"): do_something() ```