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PQ is a transactional queue for PostgreSQL.

Project description

The library provides a simple interface where queues are created on-demand and items added using the put() and get() methods. In addition, queues are iterable, pulling items out as they become available.

In addition, the put() method provides scheduling and prioritization options. An item can be scheduled for work at a particular time, and its work priority defined in terms of an expected time where the item must be worked.

The library expects a connection implementation which is compatible with the psycopg2 library, e.g. psycopg2cffi.

The basic queue implementation is similar to Ryan Smith’s queue_classic library written in Ruby, but uses advisory locks for concurrency control.

In terms of performance, the implementation clock in at about 1,000 operations per second. Using the PyPy interpreter, this scales linearly with the number of cores available.

Getting started

All functionality is encapsulated in a single class PQ.

class PQ(conn=None, pool=None, table="queue", debug=False)

Example usage:

from psycopg2 import connect
from pq import PQ

conn = connect("dbname=example user=postgres")
pq = PQ(conn)

For multi-threaded operation, use a connection pool such as psycopg2.pool.ThreadedConnectionPool.

You probably want to make sure your database is created with the utf-8 encoding.

To create and configure the queue table, call the create() method.


The table name defaults to "queue". To use a different name, pass it as a string value as the table argument for the `PQ class (illustrated above).


The pq object exposes queues through Python’s dictionary interface:

queue = pq["apples"]

The queue object provides get and put methods as explained below, and in addition, it also works as a context manager where it manages a transaction:

with queue as cursor:

The statements inside the context manager are either committed as a transaction or rejected, atomically. This is useful when a queue is used to manage tasks because it allows you to retrieve a task from the queue, perform a task and write a result, with transactional semantics.


Use the put(data) method to insert an item into the queue. It takes a JSON-compatible object such as a Python dictionary:

queue.put({'kind': 'Cox'})
queue.put({'kind': 'Arthur Turner'})
queue.put({'kind': 'Golden Delicious'})

Items are pulled out of the queue using get(block=True). The default behavior is to block until an item is available with a default timeout of one second after which a value of None is returned.

def eat(kind):
    print "umm, %s apples taste good." % kind

task = queue.get()

The task object provides additional metadata in addition to the data attribute as illustrated by the string representation:

>>> task
<pq.Task id=77709 size=1 enqueued_at="2014-02-21T16:22:06Z" schedule_at=None>

The get operation is also available through iteration:

for task in queue:
    if task is None:


The iterator blocks if no item is available. Again, there is a default timeout of one second, after which the iterator yields a value of None.

An application can then choose to break out of the loop, or wait again for an item to be ready.

for task in queue:
    if task is not None:

    # This is an infinite loop!


Items can be scheduled such that they’re not pulled until a later time:

queue.put({'kind': 'Cox'}, "5m")

In this example, the item is ready for work five minutes later. The method also accepts datetime and timedelta objects.


If some items are more important than others, a time expectation can be expressed:

queue.put({'kind': 'Cox'}, expected_at="5m")

This tells the queue processor to give priority to this item over an item expected at a later time, and conversely, to prefer an item with an earlier expected time.

The scheduling and priority options can be combined:

queue.put({'kind': 'Cox'}, "1h", "2h")

This item won’t be pulled out until after one hour, and even then, it’s only processed subject to it’s priority of two hours.


If a queue name is provided as <name>/pickle (e.g. "jobs/pickle"), items are automatically pickled and unpickled using Python’s built-in cPickle module:

queue = pq["apples/pickle"]

class Apple(object):
    def __init__(self, kind):
       self.kind = kind


The old pickle protocol 0 is used to ensure the pickled data is encoded as ascii which should be compatible with any database encoding.


All objects are thread-safe as long as a connection pool is provided where each thread receives its own database connection.


In next release …


  • A queue is now also a context manager, providing transactional semantics.
  • A queues now returns task objects which provide metadata and allows reading and writing task data.


  • The same connection pool can now be used with different queues.


  • The transaction manager now correctly returns connections to the pool.

1.0 (2013-11-20)

  • Initial public release.

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