This document describes the current stable version of Celery (5.6). For development docs, go here.

Monitoring and Management Guide

Introduction

There are several tools available to monitor and inspect Celery clusters.

This document describes some of these, as well as features related to monitoring, like events and broadcast commands.

Workers

Management Command-line Utilities (inspect/control)

celery can also be used to inspect and manage worker nodes (and to some degree tasks).

To list all the commands available do:

$ celery--help

or to get help for a specific command do:

$ celery<command>--help

Commands

  • shell: Drop into a Python shell.

    The locals will include the celery variable: this is the current app. Also all known tasks will be automatically added to locals (unless the --without-tasks flag is set).

    Uses https://pypi.org/project/Ipython/, https://pypi.org/project/bpython/, or regular python in that order if installed. You can force an implementation using --ipython, --bpython, or --python.

  • status: List active nodes in this cluster

    $ celery-Aprojstatus

  • result: Show the result of a task

    $ celery-Aprojresult-ttasks.add4e196aa4-0141-4601-8138-7aa33db0f577

    Note that you can omit the name of the task as long as the task doesn’t use a custom result backend.

  • purge: Purge messages from all configured task queues.

    This command will remove all messages from queues configured in the CELERY_QUEUES setting:

    Warning

    There’s no undo for this operation, and messages will be permanently deleted!

    $ celery-Aprojpurge

    You can also specify the queues to purge using the -Q option:

    $ celery-Aprojpurge-Qcelery,foo,bar

    and exclude queues from being purged using the -X option:

    $ celery-Aprojpurge-Xcelery

  • inspect active: List active tasks

    $ celery-Aprojinspectactive

    These are all the tasks that are currently being executed.

  • inspect scheduled: List scheduled ETA tasks

    $ celery-Aprojinspectscheduled

    These are tasks reserved by the worker when they have an eta or countdown argument set.

  • inspect reserved: List reserved tasks

    $ celery-Aprojinspectreserved

    This will list all tasks that have been prefetched by the worker, and is currently waiting to be executed (doesn’t include tasks with an ETA value set).

  • inspect revoked: List history of revoked tasks

    $ celery-Aprojinspectrevoked

  • inspect registered: List registered tasks

    $ celery-Aprojinspectregistered

  • inspect stats: Show worker statistics (see Statistics)

    $ celery-Aprojinspectstats

  • inspect query_task: Show information about task(s) by id.

    Any worker having a task in this set of ids reserved/active will respond with status and information.

    $ celery-Aprojinspectquery_taske9f6c8f0-fec9-4ae8-a8c6-cf8c8451d4f8

    You can also query for information about multiple tasks:

    $ celery-Aprojinspectquery_taskid1id2...idN

  • control enable_events: Enable events

    $ celery-Aprojcontrolenable_events

  • control disable_events: Disable events

    $ celery-Aprojcontroldisable_events

  • migrate: Migrate tasks from one broker to another (EXPERIMENTAL).

    $ celery-Aprojmigrateredis://localhostamqp://localhost

    This command will migrate all the tasks on one broker to another. As this command is new and experimental you should be sure to have a backup of the data before proceeding.

Note

All inspect and control commands supports a --timeout argument, This is the number of seconds to wait for responses. You may have to increase this timeout if you’re not getting a response due to latency.

Specifying destination nodes

By default the inspect and control commands operates on all workers. You can specify a single, or a list of workers by using the --destination argument:

$ celery-Aprojinspect-dw1@e.com,w2@e.comreserved
$ celery-Aprojcontrol-dw1@e.com,w2@e.comenable_events

Flower: Real-time Celery web-monitor

Flower is a real-time web based monitor and administration tool for Celery. It’s under active development, but is already an essential tool. Being the recommended monitor for Celery, it obsoletes the Django-Admin monitor, celerymon and the ncurses based monitor.

Flower is pronounced like "flow", but you can also use the botanical version if you prefer.

Features

  • Real-time monitoring using Celery Events

    • Task progress and history

    • Ability to show task details (arguments, start time, run-time, and more)

    • Graphs and statistics

  • Remote Control

    • View worker status and statistics

    • Shutdown and restart worker instances

    • Control worker pool size and autoscale settings

    • View and modify the queues a worker instance consumes from

    • View currently running tasks

    • View scheduled tasks (ETA/countdown)

    • View reserved and revoked tasks

    • Apply time and rate limits

    • Configuration viewer

    • Revoke or terminate tasks

  • HTTP API

    • List workers

    • Shut down a worker

    • Restart worker’s pool

    • Grow worker’s pool

    • Shrink worker’s pool

    • Autoscale worker pool

    • Start consuming from a queue

    • Stop consuming from a queue

    • List tasks

    • List (seen) task types

    • Get a task info

    • Execute a task

    • Execute a task by name

    • Get a task result

    • Change soft and hard time limits for a task

    • Change rate limit for a task

    • Revoke a task

  • OpenID authentication

Screenshots

../_images/dashboard.png

More screenshots:

Usage

You can use pip to install Flower:

$ pipinstallflower

Running the flower command will start a web-server that you can visit:

$ celery-Aprojflower

The default port is http://localhost:5555, but you can change this using the –port argument:

$ celery-Aprojflower--port=5555

Broker URL can also be passed through the --broker argument :

$ celery--broker=amqp://guest:guest@localhost:5672//flower
or
$ celery--broker=redis://guest:guest@localhost:6379/0flower

Then, you can visit flower in your web browser :

$ openhttp://localhost:5555

Flower has many more features than are detailed here, including authorization options. Check out the official documentation for more information.

celery events: Curses Monitor

Added in version 2.0.

celery events is a simple curses monitor displaying task and worker history. You can inspect the result and traceback of tasks, and it also supports some management commands like rate limiting and shutting down workers. This monitor was started as a proof of concept, and you probably want to use Flower instead.

Starting:

$ celery-Aprojevents

You should see a screen like:

../_images/celeryevshotsm.jpg

celery events is also used to start snapshot cameras (see Snapshots:

$ celery-Aprojevents--camera=<camera-class>--frequency=1.0

and it includes a tool to dump events to stdout:

$ celery-Aprojevents--dump

For a complete list of options use --help:

$ celeryevents--help

RabbitMQ

To manage a Celery cluster it is important to know how RabbitMQ can be monitored.

RabbitMQ ships with the rabbitmqctl(1) command, with this you can list queues, exchanges, bindings, queue lengths, the memory usage of each queue, as well as manage users, virtual hosts and their permissions.

Note

The default virtual host ("/") is used in these examples, if you use a custom virtual host you have to add the -p argument to the command, for example: rabbitmqctl list_queues -p my_vhost ...

Inspecting queues

Finding the number of tasks in a queue:

$ rabbitmqctllist_queuesnamemessagesmessages_ready\
messages_unacknowledged

Here messages_ready is the number of messages ready for delivery (sent but not received), messages_unacknowledged is the number of messages that’s been received by a worker but not acknowledged yet (meaning it is in progress, or has been reserved). messages is the sum of ready and unacknowledged messages.

Finding the number of workers currently consuming from a queue:

$ rabbitmqctllist_queuesnameconsumers

Finding the amount of memory allocated to a queue:

$ rabbitmqctllist_queuesnamememory
Tip:

Adding the -q option to rabbitmqctl(1) makes the output easier to parse.

Redis

If you’re using Redis as the broker, you can monitor the Celery cluster using the redis-cli(1) command to list lengths of queues.

Inspecting queues

Finding the number of tasks in a queue:

$ redis-cli-hHOST-pPORT-nDATABASE_NUMBERllenQUEUE_NAME

The default queue is named celery. To get all available queues, invoke:

$ redis-cli-hHOST-pPORT-nDATABASE_NUMBERkeys\*

Note

Queue keys only exists when there are tasks in them, so if a key doesn’t exist it simply means there are no messages in that queue. This is because in Redis a list with no elements in it is automatically removed, and hence it won’t show up in the keys command output, and llen for that list returns 0.

Also, if you’re using Redis for other purposes, the output of the keys command will include unrelated values stored in the database. The recommended way around this is to use a dedicated DATABASE_NUMBER for Celery, you can also use database numbers to separate Celery applications from each other (virtual hosts), but this won’t affect the monitoring events used by for example Flower as Redis pub/sub commands are global rather than database based.

Munin

This is a list of known Munin plug-ins that can be useful when maintaining a Celery cluster.

Events

The worker has the ability to send a message whenever some event happens. These events are then captured by tools like Flower, and celery events to monitor the cluster.

Snapshots

Added in version 2.1.

Even a single worker can produce a huge amount of events, so storing the history of all events on disk may be very expensive.

A sequence of events describes the cluster state in that time period, by taking periodic snapshots of this state you can keep all history, but still only periodically write it to disk.

To take snapshots you need a Camera class, with this you can define what should happen every time the state is captured; You can write it to a database, send it by email or something else entirely.

celery events is then used to take snapshots with the camera, for example if you want to capture state every 2 seconds using the camera myapp.Camera you run celery events with the following arguments:

$ celery-Aprojevents-cmyapp.Camera--frequency=2.0

Custom Camera

Cameras can be useful if you need to capture events and do something with those events at an interval. For real-time event processing you should use app.events.Receiver directly, like in Real-time processing.

Here is an example camera, dumping the snapshot to screen:

frompprintimport pformat
fromcelery.events.snapshotimport Polaroid
classDumpCam(Polaroid):
 clear_after = True # clear after flush (incl, state.event_count).
 defon_shutter(self, state):
 if not state.event_count:
 # No new events since last snapshot.
 return
 print('Workers: {0}'.format(pformat(state.workers, indent=4)))
 print('Tasks: {0}'.format(pformat(state.tasks, indent=4)))
 print('Total: {0.event_count} events, {0.task_count} tasks'.format(
 state))

See the API reference for celery.events.state to read more about state objects.

Now you can use this cam with celery events by specifying it with the -c option:

$ celery-Aprojevents-cmyapp.DumpCam--frequency=2.0

Or you can use it programmatically like this:

fromceleryimport Celery
frommyappimport DumpCam
defmain(app, freq=1.0):
 state = app.events.State()
 with app.connection() as connection:
 recv = app.events.Receiver(connection, handlers={'*': state.event})
 with DumpCam(state, freq=freq):
 recv.capture(limit=None, timeout=None)
if __name__ == '__main__':
 app = Celery(broker='amqp://guest@localhost//')
 main(app)

Real-time processing

To process events in real-time you need the following

  • An event consumer (this is the Receiver)

  • A set of handlers called when events come in.

    You can have different handlers for each event type, or a catch-all handler can be used (‘*’)

  • State (optional)

    app.events.State is a convenient in-memory representation of tasks and workers in the cluster that’s updated as events come in.

    It encapsulates solutions for many common things, like checking if a worker is still alive (by verifying heartbeats), merging event fields together as events come in, making sure time-stamps are in sync, and so on.

Combining these you can easily process events in real-time:

fromceleryimport Celery
defmy_monitor(app):
 state = app.events.State()
 defannounce_failed_tasks(event):
 state.event(event)
 # task name is sent only with -received event, and state
 # will keep track of this for us.
 task = state.tasks.get(event['uuid'])
 print('TASK FAILED: %s[%s] %s' % (
 task.name, task.uuid, task.info(),))
 with app.connection() as connection:
 recv = app.events.Receiver(connection, handlers={
 'task-failed': announce_failed_tasks,
 '*': state.event,
 })
 recv.capture(limit=None, timeout=None, wakeup=True)
if __name__ == '__main__':
 app = Celery(broker='amqp://guest@localhost//')
 my_monitor(app)

Note

The wakeup argument to capture sends a signal to all workers to force them to send a heartbeat. This way you can immediately see workers when the monitor starts.

You can listen to specific events by specifying the handlers:

fromceleryimport Celery
defmy_monitor(app):
 state = app.events.State()
 defannounce_failed_tasks(event):
 state.event(event)
 # task name is sent only with -received event, and state
 # will keep track of this for us.
 task = state.tasks.get(event['uuid'])
 print('TASK FAILED: %s[%s] %s' % (
 task.name, task.uuid, task.info(),))
 with app.connection() as connection:
 recv = app.events.Receiver(connection, handlers={
 'task-failed': announce_failed_tasks,
 })
 recv.capture(limit=None, timeout=None, wakeup=True)
if __name__ == '__main__':
 app = Celery(broker='amqp://guest@localhost//')
 my_monitor(app)

Event Reference

This list contains the events sent by the worker, and their arguments.

Task Events

task-sent

signature:

task-sent(uuid, name, args, kwargs, retries, eta, expires, queue, exchange, routing_key, root_id, parent_id)

Sent when a task message is published and the task_send_sent_event setting is enabled.

task-received

signature:

task-received(uuid, name, args, kwargs, retries, eta, hostname, timestamp, root_id, parent_id)

Sent when the worker receives a task.

task-started

signature:

task-started(uuid, hostname, timestamp, pid)

Sent just before the worker executes the task.

task-succeeded

signature:

task-succeeded(uuid, result, runtime, hostname, timestamp)

Sent if the task executed successfully.

Run-time is the time it took to execute the task using the pool. (Starting from the task is sent to the worker pool, and ending when the pool result handler callback is called).

task-failed

signature:

task-failed(uuid, exception, traceback, hostname, timestamp)

Sent if the execution of the task failed.

task-rejected

signature:

task-rejected(uuid, requeue)

The task was rejected by the worker, possibly to be re-queued or moved to a dead letter queue.

task-revoked

signature:

task-revoked(uuid, terminated, signum, expired)

Sent if the task has been revoked (Note that this is likely to be sent by more than one worker).

  • terminated is set to true if the task process was terminated,

    and the signum field set to the signal used.

  • expired is set to true if the task expired.

task-retried

signature:

task-retried(uuid, exception, traceback, hostname, timestamp)

Sent if the task failed, but will be retried in the future.

Worker Events

worker-online

signature:

worker-online(hostname, timestamp, freq, sw_ident, sw_ver, sw_sys)

The worker has connected to the broker and is online.

  • hostname: Nodename of the worker.

  • timestamp: Event time-stamp.

  • freq: Heartbeat frequency in seconds (float).

  • sw_ident: Name of worker software (e.g., py-celery).

  • sw_ver: Software version (e.g., 2.2.0).

  • sw_sys: Operating System (e.g., Linux/Darwin).

worker-heartbeat

signature:

worker-heartbeat(hostname, timestamp, freq, sw_ident, sw_ver, sw_sys, active, processed)

Sent every minute, if the worker hasn’t sent a heartbeat in 2 minutes, it is considered to be offline.

  • hostname: Nodename of the worker.

  • timestamp: Event time-stamp.

  • freq: Heartbeat frequency in seconds (float).

  • sw_ident: Name of worker software (e.g., py-celery).

  • sw_ver: Software version (e.g., 2.2.0).

  • sw_sys: Operating System (e.g., Linux/Darwin).

  • active: Number of currently executing tasks.

  • processed: Total number of tasks processed by this worker.

worker-offline

signature:

worker-offline(hostname, timestamp, freq, sw_ident, sw_ver, sw_sys)

The worker has disconnected from the broker.

Mailbox Configuration (Advanced)

Celery uses kombu.pidbox.Mailbox internally to send control and broadcast commands to workers.

Added in version Kombu: 5.6.0

Advanced users can configure the behavior of this mailbox by customizing how it is created. The following parameters are now supported by Mailbox:

  • durable (default: False): If set to True, the control exchanges will survive broker restarts.

  • exclusive (default: False): If set to True, the exchanges will be usable by only one connection.

Warning

Setting both durable=True and exclusive=True is not permitted and will raise an error, as these two options are mutually incompatible in AMQP.

See event_queue_durable and event_queue_exclusive for advanced configuration.