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']
As far as I know, when a packet arrives at the NIC, the DMAC will copy the packet to the kernel space. When the DMAC completes its work, it notifies the CPU, and then the CPU copies the data to the user space. Doing so will cause the memory to be read once and to be written twice. I wrote a simple program to simulate this process. This is the code:
# server.py
import socket
import sys
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
host = "70.202.0.116"
port = 12306
server.bind((host, port))
server.listen(5)
while True:
conn,addr = server.accept()
print(conn,addr)
while True:
data = conn.recv(4096)
if not data:
print("client has lost")
conn.close()
break
server.close()
# client.py
import socket
import sys
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
host = "70.202.0.116"
port = 12306
client.connect((host, port))
data = ''
for i in range(4096):
data += 'a'
while True:
client.send(data.encode())
client.close()
My machine has two numa nodes. At the first time, I disabled NIC Multi-Queue by ethtool -L eno1 combined 1, thus there is only one network interrupt left, and set the affanity by ehco 22 > /proc/irq/137/smp_affinity_list. Core 22 is on numa 1. Then I ran server.py. I use pcm-memory to moniter system memory bandwidth, and I got the expected output, the read-write ratio is close to 1:2.
But when I changed the affanity to core 0 which is on numa 0, I got totally different result. The read-write ratio is close to 1:1.
I want to know what does the interrput handler do during this process, why did I get different result?
increase read latency could be because device belongs to different numa_node. Check device where server and client is running belongs to which numa node
# cat /sys/bus/pci/devices/<PCI device>/numa_node
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.
I'm using Celery with an AMQP broker to call tasks, but the response needs to be passed back with a different queue architecture than Celery uses, so I want to pass the messages back using Kombu only. I've been able to do this, but I'm creating a new connection every time. Does Celery use a broker connection pool, and if so, how do you access it?
It took a lot of searching because Celery's documentation is... wonderful... but I found the answer.
Celery does use a broker connection pool for calling subtasks. The celery application has a pool attribute that you can access through <your_app>.pool or celery.current_app.pool. You can then grab a connection from the pool using pool.acquire().
Also, it's possible by using Bootsteps https://docs.celeryproject.org/en/stable/userguide/extending.html
Let me copy-paste code from documentation (e.g. prevent 404 error in future)
from celery import Celery
from celery import bootsteps
from kombu import Consumer, Exchange, Queue
my_queue = Queue('custom', Exchange('custom'), 'routing_key')
app = Celery(broker='amqp://')
class MyConsumerStep(bootsteps.ConsumerStep):
def get_consumers(self, channel):
return [Consumer(channel,
queues=[my_queue],
callbacks=[self.handle_message],
accept=['json'])]
def handle_message(self, body, message):
print('Received message: {0!r}'.format(body))
message.ack()
app.steps['consumer'].add(MyConsumerStep)
def send_me_a_message(who, producer=None):
with app.producer_or_acquire(producer) as producer:
producer.publish(
{'hello': who},
serializer='json',
exchange=my_queue.exchange,
routing_key='routing_key',
declare=[my_queue],
retry=True,
)
if __name__ == '__main__':
send_me_a_message('world!')
I am using rabbitmctl using pika library.
I use the following code to create a Producer
#!/usr/bin/env python
import pika
import time
import json
import datetime
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='localhost'))
channel = connection.channel()
channel.queue_declare(queue='hello')
def callback(ch, method, properties, body):
#print " current time: %s " % (str(int((time.time())*1000)))
print body
channel.basic_consume(callback,
queue='hello',
no_ack=True)
channel.start_consuming()
Since I create an existing queue everytime (Over-write the creation of queue in case if queue is not created) The queue has been corrupted due to this.and now I want to delete the queue..how do i do that?
Since this seems to be a maintenance procedure, and not something you'll be doing routinely on your code, you should probably be using the RabbitMQ management plugin and delete the queue from there.
Anyway, you can delete it from pika with:
channel.queue_delete(queue='hello')
https://pika.readthedocs.org/en/latest/modules/channel.html#pika.channel.Channel.queue_delete
The detailed answer is as follows (with reference to above very helpful and useful answer)
import pika
connection = pika.BlockingConnection(pika.ConnectionParameters(
'localhost'))
channel = connection.channel()
channel.queue_delete(queue='hello')
connection.close()
GUI rabbitMQ mgm't made that easy
$ sudo rabbitmq-plugins enable rabbitmq_management
http://localhost:15672/#/queues
Username : guest
password : guest
inspired by this