Sherlock: Distributed Locks with a choice of backend

Sherlock is a library that provides easy-to-use distributed inter-process locks and also allows you to choose a backend of your choice for lock synchronization.

Overview

When you are working with resources which are accessed by multiple services or distributed services, more than often you need some kind of locking mechanism to make it possible to access some resources at a time.

Distributed Locks or Mutexes can help you with this. Sherlock provides the exact same facility, with some extra goodies. It provides an easy-to-use API that resembles standard library's threading.Lock semantics.

Apart from this, Sherlock gives you the flexibility of using a backend of your choice for managing locks.

Sherlock also makes it simple for you to extend Sherlock to use backends that are not supported.

Features

API similar to standard library's threading.Lock.
Support for With statement, to cleanly acquire and release locks.
Backend agnostic: supports File, Redis, Memcached, Etcd, and Kubernetes as choice of backends.
Extendable: can be easily extended to work with any other of backend of choice by extending base lock class. Read extending.

Supported Backends and Client Libraries

Following client libraries are supported for every supported backend:

File: pathlib
Redis: redis-py
Memcached: pylibmc
Etcd: python-etcd
Kubernetes: kubernetes

As of now, only the above mentioned libraries are supported. Although Sherlock takes custom client objects so that you can easily provide settings that you want to use for that backend store, but Sherlock also checks if the provided client object is an instance of the supported clients and accepts client objects which pass this check, even if the APIs are the same. Sherlock might get rid of this issue later, if need be and if there is a demand for that.

Installation

Installation is simple.

pip install sherlock

Note

Sherlock will install all the client libraries for all the supported backends.

Basic Usage

Sherlock is simple to use as at the API and semantics level, it tries to conform to standard library's threading.Lock APIs.

import sherlock
from sherlock import Lock

# Configure Sherlock's locks to use Redis as the backend,
# never expire locks and retry acquiring an acquired lock after an
# interval of 0.1 second.
sherlock.configure(backend=sherlock.backends.REDIS,
                   expire=None,
                   retry_interval=0.1)

# Note: configuring sherlock to use a backend does not limit you
# another backend at the same time. You can import backend specific locks
# like RedisLock, MCLock and EtcdLock and use them just the same way you
# use a generic lock (see below). In fact, the generic Lock provided by
# sherlock is just a proxy that uses these specific locks under the hood.

# acquire a lock called my_lock
lock = Lock('my_lock')

# acquire a blocking lock
lock.acquire()

# check if the lock has been acquired or not
lock.locked() == True

# release the lock
lock.release()

Support for `with` statement

# using with statement
with Lock('my_lock'):
    # do something constructive with your locked resource here
    pass

Blocking and Non-blocking API

# acquire non-blocking lock
lock1 = Lock('my_lock')
lock2 = Lock('my_lock')

# successfully acquire lock1
lock1.acquire()

# try to acquire lock in a non-blocking way
lock2.acquire(False) == True # returns False

# try to acquire lock in a blocking way
lock2.acquire() # blocks until lock is acquired to timeout happens

Using two backends at the same time

Configuring Sherlock to use a backend does not limit you from using another backend at the same time. You can import backend specific locks like RedisLock, MCLock and EtcdLock and use them just the same way you use a generic lock (see below). In fact, the generic Lock provided by Sherlock is just a proxy that uses these specific locks under the hood.

import sherlock
from sherlock import Lock

# Configure Sherlock's locks to use Redis as the backend
sherlock.configure(backend=sherlock.backends.REDIS)

# Acquire a lock called my_lock, this lock uses Redis
lock = Lock('my_lock')

# Now acquire locks in Memcached
from sherlock import MCLock
mclock = MCLock('my_mc_lock')
mclock.acquire()

Tests

To run all the tests (including integration), you have to make sure that all the databases are running. Make sure all the services are running:

# memcached
memcached

# redis-server
redis-server

# etcd (etcd is probably not available as package, here is the simplest way
# to run it).
wget https://github.com/coreos/etcd/releases/download/<version>/etcd-<version>-<platform>.tar.gz
tar -zxvf etcd-<version>-<platform>.gz
./etcd-<version>-<platform>/etcd

Run tests like so:

python setup.py test

Documentation

Available here.

Roadmap

Support for Zookeeper as backend.
Support for Gevent, Multithreading and Multiprocessing.

License

See LICENSE.

In short: This is an open-source project and exists for anyone to use it and/or modify it for personal use. Just be nice and attribute the credits wherever you can. :)

Questions?

You are encouraged to ask questions using issues as that helps everyone and myself when people with better know-how contribute to the discussion. However, if you wish to get in touch with me personally, then I can be contacted at kapoor.vaidik++github+sherlock@gmail.com.

Distributed Locking in Other Languages

NodeJS - https://github.com/thedeveloper/warlock

violuke/sherlock