Skip to content
Easy distributed locking using PostgreSQL Advisory Locks.
Python Makefile
Branch: master
Clone or download
Latest commit 4efba90 Nov 13, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.ci
.circleci
pals release 0.3.0 Nov 14, 2019
requirements
scripts
.coveragerc initial commit Sep 21, 2018
.gitignore fix tests so they work locally too Sep 22, 2018
MANIFEST.in
changelog.rst
docker-compose.yaml
license.txt initial commit Sep 21, 2018
pyp.ini
readme.rst
setup.cfg adjust flake8 ignore and other tox project warning Nov 14, 2019
setup.py
tox.ini

readme.rst

PostgreSQL Advisory Locks (PALs)

https://circleci.com/gh/level12/pals.svg?style=shield

Introduction

PALs makes it easy to use PostgreSQL Advisory Locks to do distributed application level locking.

Do not confuse this type of locking with table or row locking in PostgreSQL. It's not the same thing.

Distributed application level locking can be implemented by using Redis, Memcache, ZeroMQ and others. But for those who are already using PostgreSQL, setup & management of another service is unnecessary.

Usage

Install with:

pip install PALs

Then usage is as follows:

import datetime as dt
import pals

# Think of the Locker instance as a Lock factory.
locker = pals.Locker('my-app-name', 'postgresql://user:pass@server/dbname')

lock1 = locker.lock('my-lock')
lock2 = locker.lock('my-lock')

# The first acquire works
assert lock1.acquire() is True

# Non blocking version should fail immediately
assert lock2.acquire(blocking=False) is False

# Blocking version should fail after a short time
start = dt.datetime.now()
acquired = lock2.acquire(acquire_timeout=300)
waited_ms = duration(start)

assert acquired is False
assert waited_ms >= 300 and waited_ms < 350

# Release the lock
lock1.release()

# Non-blocking usage pattern
if not lock1.acquire(blocking=False):
    # Aquire returned False, indicating we did not get the lock.
    return
try:
    # do your work here
finally:
    lock1.release()

# If you want to block, you can use a context manager:
try:
    with lock1:
        # Do your work here
        pass
except pals.AcquireFailure:
    # This indicates the aquire_timeout was reached before the lock could be aquired.
    pass

Docs

Just this readme, the code, and tests. It a small project, should be easy to understand.

Feel free to open an issue with questions.

Running Tests Locally

Setup Database Connection

We have provided a docker-compose file to ease running the tests:

$ docker-compose up -d
$ export PALS_DB_URL=postgresql://postgres:password@localhost:54321/postgres

Run the Tests

With tox:

$ tox

Or, manually (assuming an activated virtualenv):

$ pip install -r requirements/dev.txt
$ pip install -e .
$ pytest pals/tests/

Lock Releasing & Expiration

Unlike locking systems built on cache services like Memcache and Redis, whose keys can be expired by the service, there is no faculty for expiring an advisory lock in PostgreSQL. If a client holds a lock and then sleeps/hangs for mins/hours/days, no other client will be able to get that lock until the client releases it. This actually seems like a good thing to us, if a lock is acquired, it should be kept until released.

But what about accidental failures to release the lock?

  1. If a developer uses lock.acquire() but doesn't later call lock.release()?
  2. If code inside a lock accidentally throws an exception (and .release() is not called)?
  3. If the process running the application crashes or the process' server dies?

PALs helps #1 and #2 above in a few different ways:

  • Locks work as context managers. Use them as much as possible to guarantee a lock is released.
  • Locks release their lock when garbage collected.
  • PALs uses a dedicated SQLAlchemy connection pool. When a connection is returned to the pool, either because a connection .close() is called or due to garbage collection of the connection, PALs issues a pg_advisory_unlock_all(). It should therefore be impossible for an idle connection in the pool to ever still be holding a lock.

Regarding #3 above, pg_advisory_unlock_all() is implicitly invoked by PostgreSQL whenever a connection (a.k.a session) ends, even if the client disconnects ungracefully. So if a process crashes or otherwise disappears, PostgreSQL should notice and remove all locks held by that connection/session.

The possibility could exist that PostgreSQL does not detect a connection has closed and keeps a lock open indefinitely. However, in manual testing using scripts/hang.py no way was found to end the Python process without PostgreSQL detecting it.

See Also

You can’t perform that action at this time.