Advanced Task Management in CeleryAdvanced Task Management in Celery

Mahendra MMahendra M

@mahendra@mahendra

https://github.com/mahendrahttps://github.com/mahendra

@mahendra@mahendra

● Python developer for 6 yearsPython developer for 6 years● FOSS enthusiast/volunteer for 14 yearsFOSS enthusiast/volunteer for 14 years

● Bangalore LUG and Infosys LUGBangalore LUG and Infosys LUG● FOSS.in and LinuxBangalore/200xFOSS.in and LinuxBangalore/200x

● Celery user for 3 yearsCelery user for 3 years● ContributionsContributions

● patches, testing new releasespatches, testing new releases● Zookeeper msg transport for kombuZookeeper msg transport for kombu● Kafka support (in-progress)Kafka support (in-progress)

Quick Intro to CeleryQuick Intro to Celery

● Asynchronous task/job queueAsynchronous task/job queue● Uses distributed message passingUses distributed message passing● Tasks are run asynchronously on worker nodesTasks are run asynchronously on worker nodes● Results are passed back to the caller (if any)Results are passed back to the caller (if any)

OverviewOverview

Sender Msg Q

Worker 1

Worker 2

Worker N

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.

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Sample CodeSample Code

from celery.task import taskfrom celery.task import task

@task@task

def add(x, y):def add(x, y):

return x + yreturn x + y

result = add.delay(5,6)result = add.delay(5,6)

result.get()result.get()

Uses of CeleryUses of Celery

● Asynchronous task processingAsynchronous task processing● Handling long running / heavy jobsHandling long running / heavy jobs

● Image resizing, video transcode, PDF generationImage resizing, video transcode, PDF generation

● Offloading heavy web backend operationsOffloading heavy web backend operations● Scheduling tasks to be run at a particular timeScheduling tasks to be run at a particular time

● Cron for pythonCron for python

Advanced UsesAdvanced Uses

● Task RoutingTask Routing● Task retries, timeout and revokingTask retries, timeout and revoking● Task Canvas – combining tasksTask Canvas – combining tasks

● Task co-ordinationTask co-ordination● DependenciesDependencies● Task trees or graphsTask trees or graphs● Batch tasksBatch tasks● Progress monitoringProgress monitoring

● TricksTricks● DB conflict managementDB conflict management

Sending tasks to a particular workerSending tasks to a particular worker

Sender Msg Q

Worker 1(Windows)

Worker 2(Windows)

Worker N(Linux)

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windows

windows

linux

Routing tasks – Use casesRouting tasks – Use cases

● Priority executionPriority execution● Based on hardware capabilitiesBased on hardware capabilities

● Special cards available for video captureSpecial cards available for video capture● Making use of GPUs (CUDA)Making use of GPUs (CUDA)

● Based on OS (for eg. Playready encryption)Based on OS (for eg. Playready encryption)● Based on locationBased on location

● Moving compute closer to data (Hadoop-ish)Moving compute closer to data (Hadoop-ish)● Sending tasks to different data centersSending tasks to different data centers

● Sequencing operations (CouchDB conflicts)Sequencing operations (CouchDB conflicts)

Sample CodeSample Code

from celery.task import taskfrom celery.task import task

@task(queue = 'windows')@task(queue = 'windows')

def drm_encrypt(audio_file, key_phrase):def drm_encrypt(audio_file, key_phrase):

......

r = drm_encrypt.apply_async( args = [afile, key],r = drm_encrypt.apply_async( args = [afile, key],

queue = 'windows' )queue = 'windows' )

#Start celery worker with queues options#Start celery worker with queues options

$ celery worker -Q windows$ celery worker -Q windows

Retrying tasksRetrying tasks

@task( default_retry_delay = 60,@task( default_retry_delay = 60,

max_retries = 3 )max_retries = 3 )

def drm_encrypt(audio_file, key_phrase):def drm_encrypt(audio_file, key_phrase):

try:try:

playready.encrypt(...)playready.encrypt(...)

except Exception, exc:except Exception, exc:

raise drm_encrypt.retry(exc=exc, countdown=5)raise drm_encrypt.retry(exc=exc, countdown=5)

Retrying tasksRetrying tasks

● You can specify the number of times a task can You can specify the number of times a task can be retried.be retried.

● The cases for retrying a task must be handled The cases for retrying a task must be handled within code. Celery will not do it automaticallywithin code. Celery will not do it automatically

● The tasks should be designed to be idempotentThe tasks should be designed to be idempotent

Handling worker failuresHandling worker failures

@task( acks_late = True )@task( acks_late = True )

def drm_encrypt(audio_file, key_phrase):def drm_encrypt(audio_file, key_phrase):

try:try:

playready.encrypt(...)playready.encrypt(...)

except Exception, exc:except Exception, exc:

raise drm_encrypt.retry(exc=exc, countdown=5)raise drm_encrypt.retry(exc=exc, countdown=5)

● This is used where the task must be resend in case of This is used where the task must be resend in case of worker or node failureworker or node failure

● The ack message to the message queue is sent after the The ack message to the message queue is sent after the task finishes executingtask finishes executing

Worker processesWorker processes

Sender Msg Q

Worker 1(Windows)

Worker 2(Windows)

Worker N(Linux)

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.

windows

windows

linux

Process 1

Process 2

Process N

Worker processesWorker processes

Sender Msg Q

Worker 1(Windows)

Worker 2(Windows)

Worker N(Linux)

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.

.

windows

windows

linux

Process 1

Process 2

Process N

Worker processWorker process

● In every worker node, celery starts a pool of In every worker node, celery starts a pool of worker processesworker processes

● The number is determined by the concurrency The number is determined by the concurrency setting (or autodetected – for full CPU usage)setting (or autodetected – for full CPU usage)

● Each processes can be configured to restart Each processes can be configured to restart after running x number of tasksafter running x number of tasks● Disabled by defaultDisabled by default

● Alternately eventlet can be used instead of Alternately eventlet can be used instead of processes (discuss later)processes (discuss later)

Revoking tasksRevoking tasks

celery.control.revoke( task_id,celery.control.revoke( task_id,

terminate = False,terminate = False,

signal = 'SIGKILL' )signal = 'SIGKILL' )

● revoke()revoke() works by sending a broadcast works by sending a broadcast message to all workersmessage to all workers

● If a task has not yet run, workers will keep this If a task has not yet run, workers will keep this task_idtask_id in memory and ensure that it does not in memory and ensure that it does not runrun

● If a task is running, If a task is running, revoke()revoke() will not work will not work unless unless terminate = Trueterminate = True

Task expirationTask expiration

task.apply_async( expires = x )task.apply_async( expires = x )

x can bex can be

* in seconds * in seconds

* a specific datetime()* a specific datetime()

● Global time limits can be configured in settingsGlobal time limits can be configured in settings● Soft time limit – the task receives an exception Soft time limit – the task receives an exception

which can be used to cleanupwhich can be used to cleanup● Hard time limit – the worker running the task is Hard time limit – the worker running the task is

killed and is replaced with another one.killed and is replaced with another one.

Handling soft time limitHandling soft time limit

@task()@task()

def drm_encrypt(audio_file, key_phrase):def drm_encrypt(audio_file, key_phrase):

Try:Try:

setup_tmp_files()setup_tmp_files()

playready.encrypt(...)playready.encrypt(...)

except SoftTimeLimitExceeded:except SoftTimeLimitExceeded:

cleanup_tmp_files()cleanup_tmp_files()

except Exception, exc:except Exception, exc:

raise drm_encrypt.retry(exc=exc, countdown=5)raise drm_encrypt.retry(exc=exc, countdown=5)

SoftTimeLimitExceeded:

Task CanvasTask Canvas

● Chains – Linking one task to anotherChains – Linking one task to another● Groups – Execute several tasks in parallelGroups – Execute several tasks in parallel● Chord – execute a task after a set of tasks has Chord – execute a task after a set of tasks has

finishedfinished● Map and starmap – Similar to map() functionMap and starmap – Similar to map() function● Chunks – divide an iterable of work into chunksChunks – divide an iterable of work into chunks● Chunks + Chord/chain can be used for map-Chunks + Chord/chain can be used for map-

reducereduce

Best shown in a demoBest shown in a demo

Task treesTask trees

[ task 1 ] --- spawns --- [ task 2 ] ---- spawns --> [ task 2_1 ] | [ task 2_3 ] | +------ [ task 3 ] ---- spawns --> [ task 3_1 ] | [ task 3_2 ] | +------ [ task 4 ] ---- links ---> [ task 5 ] |(spawns) | | [ task 8 ] <--- links <--- [ task 6 ] |(spawns) [ task 7 ]

Task TreesTask Trees

● Home grown solution (our current approach)Home grown solution (our current approach)● Use db models and keep track of treesUse db models and keep track of trees

● Better approachBetter approach● Use celery-tasktreeUse celery-tasktree● http://pypi.python.org/pypi/celery-tasktreehttp://pypi.python.org/pypi/celery-tasktree

Celery BatchesCelery Batches

● Collect jobs and execute it in a batch.Collect jobs and execute it in a batch.● Can be used for stats collectionCan be used for stats collection● Batch execution is done onceBatch execution is done once

● a configured timeout is reached ORa configured timeout is reached OR● a configured number of tasks have been receiveda configured number of tasks have been received

● Useful for reducing n/w and db loadsUseful for reducing n/w and db loads

Celery BatchesCelery Batches

from celery.contrib.batches import Batchesfrom celery.contrib.batches import Batches

@task( base=Batches, flush_every=50, flush_interval=10 )@task( base=Batches, flush_every=50, flush_interval=10 )

def collect_stats( requests ):def collect_stats( requests ):

items = {}items = {}

for request in requests:for request in requests:

item_id = request.kwargs['item_id']item_id = request.kwargs['item_id']

items[ item_id ] = get_obj( item_id )items[ item_id ] = get_obj( item_id )

items[ item_id ].count += 1items[ item_id ].count += 1

# Sync to db# Sync to db

collect_stats.delay( item_id = 45 )collect_stats.delay( item_id = 45 )

collect_stats.delay( item_id = 57 )collect_stats.delay( item_id = 57 )

Celery monitoringCelery monitoring

● Celery FlowerCelery Flower

https://github.com/mher/flower https://github.com/mher/flower ● Django admin monitorDjango admin monitor● Celery jobstaticCelery jobstatic

http://pypi.python.org/pypi/jobtastichttp://pypi.python.org/pypi/jobtastic

Celery deploymentCelery deployment

● Cyme – celery instance managerCyme – celery instance manager

https://github.com/celery/cyme https://github.com/celery/cyme ● Celery autoscalingCelery autoscaling● Use celery eventlet where requiredUse celery eventlet where required