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 am very new to quartz scheduler. I am aware that we can enable logs for quartz jobs and triggers by doing the following configuration
org.quartz.plugin.jobHistory.class: org.quartz.plugins.history.LoggingJobHistoryPlugin
# Format of Log Generated
org.quartz.plugin.jobHistory.jobSuccessMessage= Job [{1}.{0}] execution complete and reports: { 8 }
org.quartz.plugin.jobHistory.jobToBeFiredMessage= Job [{1}.{0}] to be fired by trigger [{4}.{3}], re-fire: { 7 }
org.quartz.plugin.triggHistory.class= org.quartz.plugins.history.LoggingTriggerHistoryPlugin
# Format of Log Generated
org.quartz.plugin.triggHistory.triggerFiredMessage= Trigger \{1\}.\{0\} fired job \{6\}.\{5\} at: \{4, date, HH:mm:ss MM/dd/yyyy\}
org.quartz.plugin.triggHistory.triggerCompleteMessage= Trigger \{1\}.\{0\} completed firing job \{6\}.\{5\} at \{4, date, HH:mm:ss MM/dd/yyyy\}
But I am trying to understand if there is any way to directly get the quantitative metrics like how many jobs are currently running or duration for each job etc.
I am also aware of various tools like quartz-dask which gives a ui for the said metrics. But I am more interested in the metrics which in turn I could push to my prometheus instance
I am trying to use apache-airflow, with google cloud-composer, to shedule batch processing that result in the training of a model with google ai platform. I failed to use airflow operators as I explain in this question unable to specify master_type in MLEngineTrainingOperator
Using the command line I managed to launch a job successfully.
So now my issue is to integrate this command in airflow.
Using BashOperator I can train the model but I need to wait for the job to be completed before creating a version and setting it as the default. This DAG create a version before the job is done
bash_command_train = "gcloud ai-platform jobs submit training training_job_name " \
"--packages=gs://path/to/the/package.tar.gz " \
"--python-version=3.5 --region=europe-west1 --runtime-version=1.14" \
" --module-name=trainer.train --scale-tier=CUSTOM --master-machine-type=n1-highmem-16"
bash_train_operator = BashOperator(task_id='train_with_bash_command',
bash_command=bash_command_train,
dag=dag,)
...
create_version_op = MLEngineVersionOperator(
task_id='create_version',
project_id=PROJECT,
model_name=MODEL_NAME,
version={
'name': version_name,
'deploymentUri': export_uri,
'runtimeVersion': RUNTIME_VERSION,
'pythonVersion': '3.5',
'framework': 'SCIKIT_LEARN',
},
operation='create')
set_version_default_op = MLEngineVersionOperator(
task_id='set_version_as_default',
project_id=PROJECT,
model_name=MODEL_NAME,
version={'name': version_name},
operation='set_default')
# Ordering the tasks
bash_train_operator >> create_version_op >> set_version_default_op
The training result in updating of a file in Gcloud storage. So I am looking for an operator or a sensor that will wait until this file is updated, I noticed GoogleCloudStorageObjectUpdatedSensor, but I dont know how to make it retry until this file is updated.
An other solution would be to check for the job to be completed, but I can't find how too.
Any help would be greatly appreciated.
The Google Cloud documentation for the --stream-logs flag:
"Block until job completion and stream the logs while the job runs."
Add this flag to bash_command_train and I think it should solve your problem. The command should only release once the job is finished, then Airflow will mark it as success. It will also let you monitor your training job's logs in Airflow.
using visual studio team services online with an in house build agent. The build agent while running a job will randomly just freeze, the job is still active but there are no updates to the console, not errors in event logs etc. If I open the agent's _diag folder and look it will just repeat what is below until it decides to continue work.
17:02:19.850546 LogFileTimer_Callback - enter (20)
17:02:19.850546 LogFileTimer_Callback - processing job 7b9229d0-524e-4138-b6b3-33f630d109c6
17:02:19.850546 LogFileTimer_Callback - found 0 records for job 7b9229d0-524e-4138-b6b3-33f630d109c6
17:02:19.850546 LogFileTimer_Callback - leave
17:02:20.100159 StatusTimer_Callback - enter (27)
17:02:20.100159 StatusTimer_Callback - processing job 7b9229d0-524e-4138-b6b3-33f630d109c6
17:02:20.100159 StatusTimer_Callback - leave
17:02:20.240566 ConsoleTimer_Callback - enter (17)
17:02:20.240566 ConsoleTimer_Callback - Inside Lock
17:02:20.240566 ConsoleTimer_Callback - processing job 7b9229d0-524e-4138-b6b3-33f630d109c6
17:02:20.240566 ConsoleTimer_Callback - leave
17:02:20.755392 ConsoleTimer_Callback - enter (22)
17:02:20.755392 ConsoleTimer_Callback - Inside Lock
17:02:20.755392 ConsoleTimer_Callback - processing job 7b9229d0-524e-4138-b6b3-33f630d109c6
17:02:20.755392 ConsoleTimer_Callback - leave
17:02:20.864598 StatusTimer_Callback - enter (18)
17:02:20.864598 StatusTimer_Callback - processing job 7b9229d0-524e-4138-b6b3-33f630d109c6
17:02:20.864598 StatusTimer_Callback - leave
We have tried deleting the work folder, uninstalling the agent and reinstalling and it still just seems to freeze on random jobs. Any idea what else I could look into as why this is happening?
Just checked one log, and found these information existed in the log file here and there. Such as restore packages, upload logs, or retrieve files, etc.
These information don't mean there is an error. You may try to create a new agent on another machine to see whether this phenomenon would occur.