How can I retrieve a list of tasks in a queue that are yet to be processed?
EDIT: See other answers for getting a list of tasks in the queue.
You should look here:
Celery Guide - Inspecting Workers
Basically this:
my_app = Celery(...)
# Inspect all nodes.
i = my_app.control.inspect()
# Show the items that have an ETA or are scheduled for later processing
i.scheduled()
# Show tasks that are currently active.
i.active()
# Show tasks that have been claimed by workers
i.reserved()
Depending on what you want
If you are using Celery+Django simplest way to inspect tasks using commands directly from your terminal in your virtual environment or using a full path to celery:
Doc: http://docs.celeryproject.org/en/latest/userguide/workers.html?highlight=revoke#inspecting-workers
$ celery inspect reserved
$ celery inspect active
$ celery inspect registered
$ celery inspect scheduled
Also if you are using Celery+RabbitMQ you can inspect the list of queues using the following command:
More info: https://linux.die.net/man/1/rabbitmqctl
$ sudo rabbitmqctl list_queues
if you are using rabbitMQ, use this in terminal:
sudo rabbitmqctl list_queues
it will print list of queues with number of pending tasks. for example:
Listing queues ...
0b27d8c59fba4974893ec22d478a7093 0
0e0a2da9828a48bc86fe993b210d984f 0
10#torob2.celery.pidbox 0
11926b79e30a4f0a9d95df61b6f402f7 0
15c036ad25884b82839495fb29bd6395 1
celerey_mail_worker#torob2.celery.pidbox 0
celery 166
celeryev.795ec5bb-a919-46a8-80c6-5d91d2fcf2aa 0
celeryev.faa4da32-a225-4f6c-be3b-d8814856d1b6 0
the number in right column is number of tasks in the queue. in above, celery queue has 166 pending task.
If you don't use prioritized tasks, this is actually pretty simple if you're using Redis. To get the task counts:
redis-cli -h HOST -p PORT -n DATABASE_NUMBER llen QUEUE_NAME
But, prioritized tasks use a different key in redis, so the full picture is slightly more complicated. The full picture is that you need to query redis for every priority of task. In python (and from the Flower project), this looks like:
PRIORITY_SEP = '\x06\x16'
DEFAULT_PRIORITY_STEPS = [0, 3, 6, 9]
def make_queue_name_for_pri(queue, pri):
"""Make a queue name for redis
Celery uses PRIORITY_SEP to separate different priorities of tasks into
different queues in Redis. Each queue-priority combination becomes a key in
redis with names like:
- batch1\x06\x163 <-- P3 queue named batch1
There's more information about this in Github, but it doesn't look like it
will change any time soon:
- https://github.com/celery/kombu/issues/422
In that ticket the code below, from the Flower project, is referenced:
- https://github.com/mher/flower/blob/master/flower/utils/broker.py#L135
:param queue: The name of the queue to make a name for.
:param pri: The priority to make a name with.
:return: A name for the queue-priority pair.
"""
if pri not in DEFAULT_PRIORITY_STEPS:
raise ValueError('Priority not in priority steps')
return '{0}{1}{2}'.format(*((queue, PRIORITY_SEP, pri) if pri else
(queue, '', '')))
def get_queue_length(queue_name='celery'):
"""Get the number of tasks in a celery queue.
:param queue_name: The name of the queue you want to inspect.
:return: the number of items in the queue.
"""
priority_names = [make_queue_name_for_pri(queue_name, pri) for pri in
DEFAULT_PRIORITY_STEPS]
r = redis.StrictRedis(
host=settings.REDIS_HOST,
port=settings.REDIS_PORT,
db=settings.REDIS_DATABASES['CELERY'],
)
return sum([r.llen(x) for x in priority_names])
If you want to get an actual task, you can use something like:
redis-cli -h HOST -p PORT -n DATABASE_NUMBER lrange QUEUE_NAME 0 -1
From there you'll have to deserialize the returned list. In my case I was able to accomplish this with something like:
r = redis.StrictRedis(
host=settings.REDIS_HOST,
port=settings.REDIS_PORT,
db=settings.REDIS_DATABASES['CELERY'],
)
l = r.lrange('celery', 0, -1)
pickle.loads(base64.decodestring(json.loads(l[0])['body']))
Just be warned that deserialization can take a moment, and you'll need to adjust the commands above to work with various priorities.
To retrieve tasks from backend, use this
from amqplib import client_0_8 as amqp
conn = amqp.Connection(host="localhost:5672 ", userid="guest",
password="guest", virtual_host="/", insist=False)
chan = conn.channel()
name, jobs, consumers = chan.queue_declare(queue="queue_name", passive=True)
A copy-paste solution for Redis with json serialization:
def get_celery_queue_items(queue_name):
import base64
import json
# Get a configured instance of a celery app:
from yourproject.celery import app as celery_app
with celery_app.pool.acquire(block=True) as conn:
tasks = conn.default_channel.client.lrange(queue_name, 0, -1)
decoded_tasks = []
for task in tasks:
j = json.loads(task)
body = json.loads(base64.b64decode(j['body']))
decoded_tasks.append(body)
return decoded_tasks
It works with Django. Just don't forget to change yourproject.celery.
This worked for me in my application:
def get_celery_queue_active_jobs(queue_name):
connection = <CELERY_APP_INSTANCE>.connection()
try:
channel = connection.channel()
name, jobs, consumers = channel.queue_declare(queue=queue_name, passive=True)
active_jobs = []
def dump_message(message):
active_jobs.append(message.properties['application_headers']['task'])
channel.basic_consume(queue=queue_name, callback=dump_message)
for job in range(jobs):
connection.drain_events()
return active_jobs
finally:
connection.close()
active_jobs will be a list of strings that correspond to tasks in the queue.
Don't forget to swap out CELERY_APP_INSTANCE with your own.
Thanks to #ashish for pointing me in the right direction with his answer here: https://stackoverflow.com/a/19465670/9843399
The celery inspect module appears to only be aware of the tasks from the workers perspective. If you want to view the messages that are in the queue (yet to be pulled by the workers) I suggest to use pyrabbit, which can interface with the rabbitmq http api to retrieve all kinds of information from the queue.
An example can be found here:
Retrieve queue length with Celery (RabbitMQ, Django)
I think the only way to get the tasks that are waiting is to keep a list of tasks you started and let the task remove itself from the list when it's started.
With rabbitmqctl and list_queues you can get an overview of how many tasks are waiting, but not the tasks itself: http://www.rabbitmq.com/man/rabbitmqctl.1.man.html
If what you want includes the task being processed, but are not finished yet, you can keep a list of you tasks and check their states:
from tasks import add
result = add.delay(4, 4)
result.ready() # True if finished
Or you let Celery store the results with CELERY_RESULT_BACKEND and check which of your tasks are not in there.
As far as I know Celery does not give API for examining tasks that are waiting in the queue. This is broker-specific. If you use Redis as a broker for an example, then examining tasks that are waiting in the celery (default) queue is as simple as:
connect to the broker
list items in the celery list (LRANGE command for an example)
Keep in mind that these are tasks WAITING to be picked by available workers. Your cluster may have some tasks running - those will not be in this list as they have already been picked.
The process of retrieving tasks in particular queue is broker-specific.
I've come to the conclusion the best way to get the number of jobs on a queue is to use rabbitmqctl as has been suggested several times here. To allow any chosen user to run the command with sudo I followed the instructions here (I did skip editing the profile part as I don't mind typing in sudo before the command.)
I also grabbed jamesc's grep and cut snippet and wrapped it up in subprocess calls.
from subprocess import Popen, PIPE
p1 = Popen(["sudo", "rabbitmqctl", "list_queues", "-p", "[name of your virtula host"], stdout=PIPE)
p2 = Popen(["grep", "-e", "^celery\s"], stdin=p1.stdout, stdout=PIPE)
p3 = Popen(["cut", "-f2"], stdin=p2.stdout, stdout=PIPE)
p1.stdout.close()
p2.stdout.close()
print("number of jobs on queue: %i" % int(p3.communicate()[0]))
If you control the code of the tasks then you can work around the problem by letting a task trigger a trivial retry the first time it executes, then checking inspect().reserved(). The retry registers the task with the result backend, and celery can see that. The task must accept self or context as first parameter so we can access the retry count.
#task(bind=True)
def mytask(self):
if self.request.retries == 0:
raise self.retry(exc=MyTrivialError(), countdown=1)
...
This solution is broker agnostic, ie. you don't have to worry about whether you are using RabbitMQ or Redis to store the tasks.
EDIT: after testing I've found this to be only a partial solution. The size of reserved is limited to the prefetch setting for the worker.
from celery.task.control import inspect
def key_in_list(k, l):
return bool([True for i in l if k in i.values()])
def check_task(task_id):
task_value_dict = inspect().active().values()
for task_list in task_value_dict:
if self.key_in_list(task_id, task_list):
return True
return False
With subprocess.run:
import subprocess
import re
active_process_txt = subprocess.run(['celery', '-A', 'my_proj', 'inspect', 'active'],
stdout=subprocess.PIPE).stdout.decode('utf-8')
return len(re.findall(r'worker_pid', active_process_txt))
Be careful to change my_proj with your_proj
To get the number of tasks on a queue you can use the flower library, here is a simplified example:
from flower.utils.broker import Broker
from django.conf import settings
def get_queue_length(queue):
broker = Broker(settings.CELERY_BROKER_URL)
queues_result = broker.queues([queue])
return queues_result.result()[0]['messages']
I'm trying to share a psycopg2.pool.(Simple/Threaded)ConnectionPool among multiple python processes. What is the correct way to approach this?
I am using Python 2.7 and Postgres 9.
I would like to provide some context. I want to use a connection pool is because I am running an arbitrary, but large (>80) number of processes that initially use the database to query results, perform some actions, then update the database with the results of the actions.
So far, I've tried to use the multiprocessing.managers.BaseManager and pass it to child processes so that the connections that are being used/unused are synchronized across the processes.
from multiprocessing import Manager, Process
from multiprocessing.managers import BaseManager
PASSWORD = 'xxxx'
def f(connection, connection_pool):
with connection.cursor() as curs: # ** LINE REFERENCED BELOW
curs.execute('SELECT * FROM database')
connection_pool.putconn(connection)
BaseManager.register('SimpleConnectionPool', SimpleConnectionPool)
manager = BaseManager()
manager.start()
conn_pool = manager.SimpleConnectionPool(5, 20, dbname='database', user='admin', host='xxxx', password=PASSWORD, port='8080')
with conn_pool.getconn() as conn:
print conn # prints '<connection object at 0x7f48243edb48; dsn:'<unintialized>', closed:0>
proc = Process(target=f, args=(conn, conn_pool))
proc.start()
proc.join()
** raises an error, 'Operational Error: asynchronous connection attempt underway'
If anyone could recommend a method to share the connection pool with numerous processes it would be greatly appreciated.
You don't. You can't pass a socket to another process. The other process must open the connection itself.
Is it possible to use a different message broker with celery?
For example: I would like to use PostgreSQL instead of RabbitMQ.
AFAIK it is only supported in the result backend: http://docs.celeryproject.org/en/latest/userguide/configuration.html#database-backend-settings
Since PostgreSQL 9.5 there is SKIP LOCKED which enables implementing robust message/work queues. See https://blog.2ndquadrant.com/what-is-select-skip-locked-for-in-postgresql-9-5/
Yes, you can use postgres as broker instead of rabbitmq. Here is a simple example to demonstrate it.
from celery import Celery
broker = 'sqla+postgresql://user:pass#host/dbname'
app = Celery(broker=broker)
#app.task
def add(x, y):
return x + y
Queuing tasks
In [1]: from demo import add
In [2]: add.delay(1,2)
Out[2]: <AsyncResult: 4853190f-d355-48ae-8aba-6169d38fad39>
Worker results:
[2017-12-02 08:11:08,483: INFO/MainProcess] Received task: t.add[809060c0-dc7e-4a38-9e4e-9fdb44dd6a31]
[2017-12-02 08:11:08,496: INFO/ForkPoolWorker-1] Task t.add[809060c0-dc7e-4a38-9e4e-9fdb44dd6a31] succeeded in 0.0015781960000822437s: 3
Tested on latest(celery==4.1.0, kombu==4.1.0, SQLAlchemy==1.1.1) versions.
Is it possible to use a different message broker with celery?
before Version 4, it's sure yes! i have ever use mongodb for message broker in Celery 3, following the official document。
so if want to use PostgreSQL as the broker，it's ok，Celery also support SQLAlchemy.
However, if you want to use it in Celery 4.0, maybe it's a little difficult，one way in my mind is change the code for Kombu，yes，it's Kombu，not Celery！
I am writing an apache-camel RabbitMQ consumer. I would like to react somehow to connection problems (i.e. try to reconnect). Is it possible to configure apache-camel to automatically reconnect?
If not, how can I find out that a connection to the queue was interrupted? I've done the following test:
start the queue (and some producer)
start my consumer (it was getting messages as expected)
stop the queue (the messages stopped arriving, as expected, but no exception was thrown)
start the queue (no new messages were received)
I am using camel in Scala (via akka-camel), but a Java solution would be probably also OK
You can pass in the flag automaticRecoveryEnabled=true to the URI, Camel will reconnect if the connection is lost.
For automatic RabbitMQ resource recovery (Connections/Channels/Consumers/Queues/Exchanages/Bindings) when failures occur, check out Lyra (which I authored). Example usage:
Config config = new Config()
.withRecoveryPolicy(new RecoveryPolicy()
.withMaxAttempts(20)
.withInterval(Duration.seconds(1))
.withMaxDuration(Duration.minutes(5)));
ConnectionOptions options = new ConnectionOptions().withHost("localhost");
Connection connection = Connections.create(options, config);
The rest of the API is just the amqp-client API, except your resources are automatically recovered when failures occur.
I'm not sure about camel-rabbitmq specifically, but hopefully there's a way you can swap in your own resource creation via Lyra.
Current camel-rabbitmq just create a connection and the channel when the consumer or producer is started. So it don't have a chance to catch the connection exception :(.
I'm trying to setup an application webserver using uWSGI + Nginx, which runs a Flask application using SQLAlchemy to communicate to a Postgres database.
When I make requests to the webserver, every other response will be a 500 error.
The error is:
Traceback (most recent call last):
File "/var/env/argos/lib/python3.3/site-packages/sqlalchemy/engine/base.py", line 867, in _execute_context
context)
File "/var/env/argos/lib/python3.3/site-packages/sqlalchemy/engine/default.py", line 388, in do_execute
cursor.execute(statement, parameters)
psycopg2.OperationalError: SSL error: decryption failed or bad record mac
The above exception was the direct cause of the following exception:
sqlalchemy.exc.OperationalError: (OperationalError) SSL error: decryption failed or bad record mac
The error is triggered by a simple Flask-SQLAlchemy method:
result = models.Event.query.get(id)
uwsgi is being managed by supervisor, which has a config:
[program:my_app]
command=/usr/bin/uwsgi --ini /etc/uwsgi/apps-enabled/myapp.ini --catch-exceptions
directory=/path/to/my/app
stopsignal=QUIT
autostart=true
autorestart=true
and uwsgi's config looks like:
[uwsgi]
socket = /tmp/my_app.sock
logto = /var/log/my_app.log
plugins = python3
virtualenv = /path/to/my/venv
pythonpath = /path/to/my/app
wsgi-file = /path/to/my/app/application.py
callable = app
max-requests = 1000
chmod-socket = 666
chown-socket = www-data:www-data
master = true
processes = 2
no-orphans = true
log-date = true
uid = www-data
gid = www-data
The furthest that I can get is that it has something to do with uwsgi's forking. But beyond that I'm not clear on what needs to be done.
The issue ended up being uwsgi's forking.
When working with multiple processes with a master process, uwsgi initializes the application in the master process and then copies the application over to each worker process. The problem is if you open a database connection when initializing your application, you then have multiple processes sharing the same connection, which causes the error above.
The solution is to set the lazy configuration option for uwsgi, which forces a complete loading of the application in each process:
lazy
Set lazy mode (load apps in workers instead of master).
This option may have memory usage implications as Copy-on-Write semantics can not be used. When lazy is enabled, only workers will be reloaded by uWSGI’s reload signals; the master will remain alive. As such, uWSGI configuration changes are not picked up on reload by the master.
There's also a lazy-apps option:
lazy-apps
Load apps in each worker instead of the master.
This option may have memory usage implications as Copy-on-Write semantics can not be used. Unlike lazy, this only affects the way applications are loaded, not master’s behavior on reload.
This uwsgi configuration ended up working for me:
[uwsgi]
socket = /tmp/my_app.sock
logto = /var/log/my_app.log
plugins = python3
virtualenv = /path/to/my/venv
pythonpath = /path/to/my/app
wsgi-file = /path/to/my/app/application.py
callable = app
max-requests = 1000
chmod-socket = 666
chown-socket = www-data:www-data
master = true
processes = 2
no-orphans = true
log-date = true
uid = www-data
gid = www-data
# the fix
lazy = true
lazy-apps = true
As an alternative you might dispose the engine. This is how I solved the problem.
Such issues may happen if there is a query during the creation of the app, that is, in the module that creates the app itself. If that states, the engine allocates a pool of connections and then uwsgi forks.
By invoking 'engine.dispose()', the connection pool itself is closed and new connections will come up as soon as someone starts making queries again. So if you do that at the end of the module where you create your app, new connections will be created after the UWSGI fork.
I am running a flask app using gunicorn on Heroku. My application started exhibiting this problem when I added the --preload option to my Procfile. When I removed that option, my application resumed functioning as normal.
Not sure whether to add this as an answer to this question or ask a separate question and put this as an answer there. I was getting this exact same error for reasons that are slightly different from the people who have posted and answered. In my setup, I using gunicorn as a wsgi for a Flask application. In this application, I was offloading some intense database operations off to a celery worker. The error would come from the celery worker.
From reading a lot of the answers here and looking at the psycopg2 as well as sqlalchemy session documentation, it became apparent to me that it is a bad idea to share an SQLAlchemy session between separate processes (the gunicorn worker and the sqlalchemy worker in my case).
What ended up solving this for me was creating a new session in the celery worker function so it used a new session each time it was called and also destroying the session after every web request so flask used a session per request. The overall solution looked like this:
Flask_app.py
#app.teardown_appcontext
def shutdown_session(exception=None):
session.close()
celery_func.py
#celery_app.task(bind=True, throws=(IntegrityError))
def access_db(self,entity_dict, tablename):
with Session() as session:
try:
session.add(ORM_obj)
session.commit()
except IntegrityError as e:
session.rollback()
print('primary key violated')
raise e