Huey Extensions

The huey.contrib package contains modules that provide extra functionality beyond the core APIs.


MiniHuey provides a very lightweight huey-like API that may be useful for certain classes of applications. Unlike Huey, the MiniHuey consumer runs inside a greenlet in your main application process. This means there is no separate consumer process to run, not is there any persistence for the enqueued/scheduled tasks; whenever a task is enqueued or is scheduled to run, a new greenlet is spawned to execute the task.

Usage and task declaration:

class MiniHuey([name='huey'[, interval=1[, pool_size=None]]])
  • name (str) – Name given to this huey instance.
  • interval (int) – How frequently to check for scheduled tasks (seconds).
  • pool_size (int) – Limit number of concurrent tasks to given size.
from huey import crontab
from huey.contrib.minimal import MiniHuey

huey = MiniHuey()

def fetch_url(url):
    return urllib2.urlopen(url).read()

@huey.task(crontab(minute='0', hour='4'))
def run_backup():


There is not a separate decorator for periodic, or crontab, tasks. Just use MiniHuey.task() and pass in a validation function.

When your application starts, be sure to start the MiniHuey scheduler:

from gevent import monkey; monkey.patch_all()

huey.start()  # Kicks off scheduler in a new greenlet.
start_wsgi_server()  # Or whatever your main application is doing...


Tasks enqueued manually for immediate execution will be run regardless of whether the scheduler is running. If you want to be able to schedule tasks in the future or run periodic tasks, you will need to call start().

Calling tasks and getting results works about the same as regular huey:

async_result = fetch_url('')
html = async_result.get()  # Block until task is executed.

# Fetch the Yahoo! page in 30 seconds.
async_result = fetch_url.schedule(args=('',),
html = async_result.get()  # Blocks for ~30s.

SQLite Storage

The SqliteHuey and the associated SqliteStorage can be used instead of the default RedisHuey. SqliteHuey is implemented in such a way that it can safely be used with a multi-process, multi-thread, or multi-greenlet consumer.

Using SqliteHuey is almost exactly the same as using RedisHuey. Begin by instantiating the Huey object, passing in the name of the queue and the filename of the SQLite database:

from huey.contrib.sqlitedb import SqliteHuey

huey = SqliteHuey('my_app', filename='/var/www/my_app/huey.db')


The SQLite storage engine depends on peewee. For information on installing peewee, see the peewee installation documentation, or simply run: pip install peewee.

Simple Server

Huey supports a simple client/server database that can be used for development and testing. The server design is inspired by redis and implements commands that map to the methods described by the storage API. If you’d like to read a technical post about the implementation, check out this blog post.

The server can optionally use gevent, but if gevent is not available you can use threads (use -t for threads).

To obtain the simple server, you can clone the simpledb repository:

$ git clone
$ cd simpledb
$ python install

Running the simple server


Usage: [options]

  -h, --help            show this help message and exit
  -d, --debug           Log debug messages.
  -e, --errors          Log error messages only.
  -t, --use-threads     Use threads instead of gevent.
  -H HOST, --host=HOST  Host to listen on.
  -m MAX_CLIENTS, --max-clients=MAX_CLIENTS
                        Maximum number of clients.
  -p PORT, --port=PORT  Port to listen on.
  -l LOG_FILE, --log-file=LOG_FILE
                        Log file.
  -x EXTENSIONS, --extensions=EXTENSIONS
                        Import path for Python extension module(s).

By default the server will listen on localhost, port 31337.

Example (with logging):

$ python --debug --log-file=/var/log/huey-simple.log

Using simple server with Huey

To use the simple server with Huey, use the SimpleHuey class:

from huey.contrib.simple_storage import SimpleHuey

huey = SimpleHuey('my-app')

def add(a, b):
    return a + b

The SimpleHuey class relies on a SimpleStorage storage backend, which in turn, uses the simple.Client client class.


Huey comes with special integration for use with the Django framework. The integration provides:

  1. Configuration of huey via the Django settings module.
  2. Running the consumer as a Django management command.
  3. Auto-discovery of modules to simplify task importing.
  4. Properly manage database connections.

Supported Django versions are the officially supported at

Setting things up

To use huey with Django, the first step is to add an entry to your project’s settings.INSTALLED_APPS:

# ...
    # ...
    'huey.contrib.djhuey',  # Add this to the list.
    # ...

The above is the bare minimum needed to start using huey’s Django integration. If you like, though, you can also configure both Huey and the consumer using the settings module.


Huey settings are optional. If not provided, Huey will default to using Redis running on localhost:6379 (standard setup).

Configuration is kept in settings.HUEY, which can be either a dictionary or a Huey instance. Here is an example that shows all of the supported options with their default values:

HUEY = {
    'name': settings.DATABASES['default']['NAME'],  # Use db name for huey.
    'result_store': True,  # Store return values of tasks.
    'events': True,  # Consumer emits events allowing real-time monitoring.
    'store_none': False,  # If a task returns None, do not save to results.
    'always_eager': settings.DEBUG,  # If DEBUG=True, run synchronously.
    'store_errors': True,  # Store error info if task throws exception.
    'blocking': False,  # Poll the queue rather than do blocking pop.
    'backend_class': 'huey.RedisHuey',  # Use path to redis huey by default,
    'connection': {
        'host': 'localhost',
        'port': 6379,
        'db': 0,
        'connection_pool': None,  # Definitely you should use pooling!
        # ... tons of other options, see redis-py for details.

        # huey-specific connection parameters.
        'read_timeout': 1,  # If not polling (blocking pop), use timeout.
        'max_errors': 1000,  # Only store the 1000 most recent errors.
        'url': None,  # Allow Redis config via a DSN.
    'consumer': {
        'workers': 1,
        'worker_type': 'thread',
        'initial_delay': 0.1,  # Smallest polling interval, same as -d.
        'backoff': 1.15,  # Exponential backoff using this rate, -b.
        'max_delay': 10.0,  # Max possible polling interval, -m.
        'utc': True,  # Treat ETAs and schedules as UTC datetimes.
        'scheduler_interval': 1,  # Check schedule every second, -s.
        'periodic': True,  # Enable crontab feature.
        'check_worker_health': True,  # Enable worker health checks.
        'health_check_interval': 1,  # Check worker health every second.

Alternatively, you can simply set settings.HUEY to a Huey instance and do your configuration directly. In the example below, I’ve also shown how you can create a connection pool:

# -- alternative configuration method
from huey import RedisHuey
from redis import ConnectionPool

pool = ConnectionPool(host='', port=6379, max_connections=20)
HUEY = RedisHuey('my-app', connection_pool=pool)

Running the Consumer

To run the consumer, use the run_huey management command. This command will automatically import any modules in your INSTALLED_APPS named The consumer can be configured using both the django settings module and/or by specifying options from the command-line.


Options specified on the command line take precedence over those specified in the settings module.

To start the consumer, you simply run:

$ ./ run_huey

In addition to the HUEY.consumer setting dictionary, the management command supports all the same options as the standalone consumer. These options are listed and described in the Options for the consumer section.

For quick reference, the most important command-line options are briefly listed here.

-w, --workers
Number of worker threads/processes/greenlets. Default is 1, but most applications should use at least 2.
-k, --worker-type
Worker type, must be “thread”, “process” or “greenlet”. The default is thread, which provides good all-around performance. For CPU-intensive workloads, process is likely to be more performant. The greenlet worker type is suited for IO-heavy workloads. When using greenlet you can specify tens or hundreds of workers since they are extremely lightweight compared to threads/processes. See note below on using gevent/greenlet.


Due to a conflict with Django’s base option list, the “verbose” option is set using -V or --huey-verbose. When enabled, huey logs at the DEBUG level.

For more information, read the Options for the consumer section.

Using gevent

When using worker type greenlet, it’s necessary to apply a monkey-patch before any libraries or system modules are imported. Gevent monkey-patches things like socket to provide non-blocking I/O, and if those modules are loaded before the patch is applied, then the resulting code will execute synchronously.

Unfortunately, because of Django’s design, the only way to reliably apply this patch is to create a custom bootstrap script that mimics the functionality of Here is the patched code:

#!/usr/bin/env python
import os
import sys

# Apply monkey-patch if we are running the huey consumer.
if 'run_huey' in sys.argv:
    from gevent import monkey

if __name__ == "__main__":
    os.environ.setdefault("DJANGO_SETTINGS_MODULE", "conf")
    from import execute_from_command_line

How to create tasks

The task() and periodic_task() decorators can be imported from the huey.contrib.djhuey module. Here is how you might define two tasks:

from huey import crontab
from huey.contrib.djhuey import periodic_task, task

def count_beans(number):
    print('-- counted %s beans --' % number)
    return 'Counted %s beans' % number

def every_five_mins():
    print('Every five minutes this will be printed by the consumer')

Tasks that execute queries

If you plan on executing queries inside your task, it is a good idea to close the connection once your task finishes. To make this easier, huey provides a special decorator to use in place of task and periodic_task which will automatically close the connection for you.

from huey import crontab
from huey.contrib.djhuey import db_periodic_task, db_task

def do_some_queries():
    # This task executes queries. Once the task finishes, the connection
    # will be closed.

def every_five_mins():
    # This is a periodic task that executes queries.

DEBUG and Synchronous Execution

When settings.DEBUG = True, tasks will be executed synchronously just like regular function calls. The purpose of this is to avoid running both Redis and an additional consumer process while developing or running tests. If, however, you would like to enqueue tasks regardless of whether DEBUG = True, then explicitly specify always_eager=False in your huey settings:

HUEY = {
    'name': 'my-app',
    # Other settings ...
    'always_eager': False,

Configuration Examples

This section contains example HUEY configurations.

# Redis running locally with four worker threads.
HUEY = {
    'name': 'my-app',
    'consumer': {'workers': 4, 'worker_type': 'thread'},
# Redis on network host with 64 worker greenlets and connection pool
# supporting up to 100 connections.
from redis import ConnectionPool

pool = ConnectionPool(

HUEY = {
    'name': 'my-app',
    'connection': {'connection_pool': pool},
    'consumer': {'workers': 64, 'worker_type': 'greenlet'},

It is also possible to specify the connection using a Redis URL, making it easy to configure this setting using a single environment variable:

HUEY = {
    'name': 'my-app',
    'url': os.environ.get('REDIS_URL', 'redis://localhost:6379/?db=1')

Alternatively, you can just assign a Huey instance to the HUEY setting:

from huey import RedisHuey

HUEY = RedisHuey('my-app')