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Python task queue

Project description

TaskTiger is a Python task queue using Redis.

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  • Per-task fork or synchronous worker

    By default, TaskTiger forks a subprocess for each task, This comes with several benefits: Memory leaks caused by tasks are avoided since the subprocess is terminated when the task is finished. A hard time limit can be set for each task, after which the task is killed if it hasn’t completed. To ensure performance, any necessary Python modules can be preloaded in the parent process.

    TaskTiger also supports synchronous workers, which allows for better performance due to no forking overhead, and tasks have the ability to reuse network connections. To prevent memory leaks from accumulating, workers can be set to shutdown after a certain amount of time, at which point a supervisor can restart them. Workers also automatically exit on on hard timeouts to prevent an inconsistent process state.

  • Unique queues

    TaskTiger has the option to avoid duplicate tasks in the task queue. In some cases it is desirable to combine multiple similar tasks. For example, imagine a task that indexes objects (e.g. to make them searchable). If an object is already present in the task queue and hasn’t been processed yet, a unique queue will ensure that the indexing task doesn’t have to do duplicate work. However, if the task is already running while it’s queued, the task will be executed another time to ensure that the indexing task always picks up the latest state.

  • Task locks

    TaskTiger can ensure to never execute more than one instance of tasks with similar arguments by acquiring a lock. If a task hits a lock, it is requeued and scheduled for later executions after a configurable interval.

  • Task retrying

    TaskTiger lets you retry exceptions (all exceptions or a list of specific ones) and comes with configurable retry intervals (fixed, linear, exponential, custom).

  • Flexible queues

    Tasks can be easily queued in separate queues. Workers pick tasks from a randomly chosen queue and can be configured to only process specific queues, ensuring that all queues are processed equally. TaskTiger also supports subqueues which are separated by a period. For example, you can have per-customer queues in the form process_emails.CUSTOMER_ID and start a worker to process process_emails and any of its subqueues. Since tasks are picked from a random queue, all customers get equal treatment: If one customer is queueing many tasks it can’t block other customers’ tasks from being processed. A maximum queue size can also be enforced.

  • Batch queues

    Batch queues can be used to combine multiple queued tasks into one. That way, your task function can process multiple sets of arguments at the same time, which can improve performance. The batch size is configurable.

  • Scheduled and periodic tasks

    Tasks can be scheduled for execution at a specific time. Tasks can also be executed periodically (e.g. every five seconds).

  • Structured logging

    TaskTiger supports JSON-style logging via structlog, allowing more flexibility for tools to analyze the log. For example, you can use TaskTiger together with Logstash, Elasticsearch, and Kibana.

    The structlog processor tasktiger.logging.tasktiger_processor can be used to inject the current task id into all log messages.

  • Reliability

    TaskTiger atomically moves tasks between queue states, and will re-execute tasks after a timeout if a worker crashes.

  • Error handling

    If an exception occurs during task execution and the task is not set up to be retried, TaskTiger stores the execution tracebacks in an error queue. The task can then be retried or deleted manually. TaskTiger can be easily integrated with error reporting services like Rollbar.

  • Admin interface

    A simple admin interface using flask-admin exists as a separate project (tasktiger-admin).

Quick start

It is easy to get started with TaskTiger.

Create a file that contains the task(s).

def my_task():

Queue the task using the delay method.

In [1]: import tasktiger, tasks
In [2]: tiger = tasktiger.TaskTiger()
In [3]: tiger.delay(tasks.my_task)

Run a worker (make sure the task code can be found, e.g. using PYTHONPATH).

% PYTHONPATH=. tasktiger
{"timestamp": "2015-08-27T21:00:09.135344Z", "queues": null, "pid": 69840, "event": "ready", "level": "info"}
{"task_id": "6fa07a91642363593cddef7a9e0c70ae3480921231710aa7648b467e637baa79", "level": "debug", "timestamp": "2015-08-27T21:03:56.727051Z", "pid": 69840, "queue": "default", "child_pid": 70171, "event": "processing"}
{"task_id": "6fa07a91642363593cddef7a9e0c70ae3480921231710aa7648b467e637baa79", "level": "debug", "timestamp": "2015-08-27T21:03:56.732457Z", "pid": 69840, "queue": "default", "event": "done"}


A TaskTiger object keeps track of TaskTiger’s settings and is used to decorate and queue tasks. The constructor takes the following arguments:

  • connection

    Redis connection object. The connection should be initialized with decode_responses=True to avoid encoding problems on Python 3.

  • config

    Dict with config options. Most configuration options don’t need to be changed, and a full list can be seen within TaskTiger’s __init__ method.

    Here are a few commonly used options:


      If set to True, all tasks except future tasks (when is a future time) will be executed locally by blocking until the task returns. This is useful for testing purposes.


      Set up queues that will be processed in batch, i.e. multiple jobs are taken out of the queue at the same time and passed as a list to the worker method. Takes a dict where the key represents the queue name and the value represents the batch size. Note that the task needs to be declared as batch=True. Also note that any subqueues will be automatically treated as batch queues, and the batch value of the most specific subqueue name takes precedence.


      If set to a non-empty list of queue names, a worker only processes the given queues (and their subqueues), unless explicit queues are passed to the command line.

  • setup_structlog

    If set to True, sets up structured logging using structlog when initializing TaskTiger. This makes writing custom worker scripts easier since it doesn’t require the user to set up structlog in advance.


import tasktiger
from redis import Redis
conn = Redis(db=1, decode_responses=True)
tiger = tasktiger.TaskTiger(connection=conn, config={
        # Batch up to 50 tasks that are queued in the my_batch_queue or any
        # of its subqueues, except for the send_email subqueue which only
        # processes up to 10 tasks at a time.
        'my_batch_queue': 50,
        'my_batch_queue.send_email': 10,

Task decorator

TaskTiger provides a task decorator to specify task options. Note that simple tasks don’t need to be decorated. However, decorating the task allows you to use an alternative syntax to queue the task, which is compatible with Celery:


import tasktiger
tiger = tasktiger.TaskTiger()

def my_task(name, n=None):
    print('Hello', name)
In [1]: import tasks
# The following are equivalent. However, the second syntax can only be used
# if the task is decorated.
In [2]: tasks.tiger.delay(my_task, args=('John',), kwargs={'n': 1})
In [3]: tasks.my_task.delay('John', n=1)

Task options

Tasks support a variety of options that can be specified either in the task decorator, or when queueing a task. For the latter, the delay method must be called on the TaskTiger object, and any options in the task decorator are overridden.

@tiger.task(queue='myqueue', unique=True)
def my_task():
# The task will be queued in "otherqueue", even though the task decorator
# says "myqueue".
tiger.delay(my_task, queue='otherqueue')

When queueing a task, the task needs to be defined in a module other than the Python file which is being executed. In other words, the task can’t be in the __main__ module. TaskTiger will give you back an error otherwise.

The following options are supported by both delay and the task decorator:

  • queue

    Name of the queue where the task will be queued.

  • hard_timeout

    If the task runs longer than the given number of seconds, it will be killed and marked as failed.

  • unique

    Boolean to indicate whether the task will only be queued if there is no similar task with the same function, arguments, and keyword arguments in the queue. Note that multiple similar tasks may still be executed at the same time since the task will still be inserted into the queue if another one is being processed. Requeueing an already scheduled unique task will not change the time it was originally scheduled to execute at.

  • unique_key

    If set, this implies unique=True and specifies the list of kwargs to use to construct the unique key. By default, all args and kwargs are serialized and hashed.

  • lock

    Boolean to indicate whether to hold a lock while the task is being executed (for the given args and kwargs). If a task with similar args/kwargs is queued and tries to acquire the lock, it will be retried later.

  • lock_key

    If set, this implies lock=True and specifies the list of kwargs to use to construct the lock key. By default, all args and kwargs are serialized and hashed.

  • max_queue_size

    A maximum queue size can be enforced by setting this to an integer value. The QueueFullException exception will be raised when queuing a task if this limit is reached. Tasks in the active, scheduled, and queued states are counted against this limit.

  • when

    Takes either a datetime (for an absolute date) or a timedelta (relative to now). If given, the task will be scheduled for the given time.

  • retry

    Boolean to indicate whether to retry the task when it fails (either because of an exception or because of a timeout). To restrict the list of failures, use retry_on. Unless retry_method is given, the configured DEFAULT_RETRY_METHOD is used.

  • retry_on

    If a list is given, it implies retry=True. The task will be only retried on the given exceptions (or its subclasses). To retry the task when a hard timeout occurs, use JobTimeoutException.

  • retry_method

    If given, implies retry=True. Pass either:

    • a function that takes the retry number as an argument, or,

    • a tuple (f, args), where f takes the retry number as the first argument, followed by the additional args.

    The function needs to return the desired retry interval in seconds, or raise StopRetry to stop retrying. The following built-in functions can be passed for common scenarios and return the appropriate tuple:

    • fixed(delay, max_retries)

      Returns a method that returns the given delay (in seconds) or raises StopRetry if the number of retries exceeds max_retries.

    • linear(delay, increment, max_retries)

      Like fixed, but starts off with the given delay and increments it by the given increment after every retry.

    • exponential(delay, factor, max_retries)

      Like fixed, but starts off with the given delay and multiplies it by the given factor after every retry.

    For example, to retry a task 3 times (for a total of 4 executions), and wait 60 seconds between executions, pass retry_method=fixed(60, 3).

  • runner_class

    If given, a Python class can be specified to influence task running behavior. The runner class should inherit tasktiger.runner.BaseRunner and implement the task execution behavior. The default implementation is available in tasktiger.runner.DefaultRunner. The following behavior can be achieved:

    • Execute specific code before or after the task is executed (in the forked child process), or customize the way task functions are called in either single or batch processing.

      Note that if you want to execute specific code for all tasks, you should use the CHILD_CONTEXT_MANAGERS configuration option.

    • Control the hard timeout behavior of a task.

    • Execute specific code in the main worker process after a task failed permanently.

    This is an advanced feature and the interface and requirements of the runner class can change in future TaskTiger versions.

The following options can be only specified in the task decorator:

  • batch

    If set to True, the task will receive a list of dicts with args and kwargs and can process multiple tasks of the same type at once. Example: [{"args": [1], "kwargs": {}}, {"args": [2], "kwargs": {}}] Note that the list will only contain multiple items if the worker has set up BATCH_QUEUES for the specific queue (see the Configuration section).

  • schedule

    If given, makes a task execute periodically. Pass either:

    • a function that takes the current datetime as an argument.

    • a tuple (f, args), where f takes the current datetime as the first argument, followed by the additional args.

    The schedule function must return the next task execution datetime, or None to prevent periodic execution. The function is executed to determine the initial task execution date when a worker is initialized, and to determine the next execution date when the task is about to get executed.

    For most common scenarios, the below mentioned built-in functions can be passed:

    • periodic(seconds=0, minutes=0, hours=0, days=0, weeks=0, start_date=None, end_date=None)

      Use equal, periodic intervals, starting from start_date (defaults to 2000-01-01T00:00Z, a Saturday, if not given), ending at end_date (or never, if not given). For example, to run a task every five minutes indefinitely, use schedule=periodic(minutes=5). To run a task every every Sunday at 4am UTC, you could use schedule=periodic(weeks=1, start_date=datetime.datetime(2000, 1, 2, 4)).

    • cron_expr(expr, start_date=None, end_date=None)

      start_date, to specify the periodic task start date. It defaults to 2000-01-01T00:00Z, a Saturday, if not given. end_date, to specify the periodic task end date. The task repeats forever if end_date is not given. For example, to run a task every hour indefinitely, use schedule=cron_expr("0 * * * *"). To run a task every Sunday at 4am UTC, you could use schedule=cron_expr("0 4 * * 0").

Custom retrying

In some cases the task retry options may not be flexible enough. For example, you might want to use a different retry method depending on the exception type, or you might want to like to suppress logging an error if a task fails after retries. In these cases, RetryException can be raised within the task function. The following options are supported:

  • method

    Specify a custom retry method for this retry. If not given, the task’s default retry method is used, or, if unspecified, the configured DEFAULT_RETRY_METHOD. Note that the number of retries passed to the retry method is always the total number of times this method has been executed, regardless of which retry method was used.

  • original_traceback

    If RetryException is raised from within an except block and original_traceback is True, the original traceback will be logged (i.e. the stacktrace at the place where the caught exception was raised). False by default.

  • log_error

    If set to False and the task fails permanently, a warning will be logged instead of an error, and the task will be removed from Redis when it completes. True by default.

Example usage:

from tasktiger.exceptions import RetryException
from tasktiger.retry import exponential, fixed

def my_task():
    if not ready():
        # Retry every minute up to 3 times if we're not ready. An error will
        # be logged if we're out of retries.
        raise RetryException(method=fixed(60, 3))

    except NetworkException:
        # Back off exponentially up to 5 times in case of a network failure.
        # Log the original traceback (as a warning) and don't log an error if
        # we still fail after 5 times.
        raise RetryException(method=exponential(60, 2, 5),


The tasktiger command is used on the command line to invoke a worker. To invoke multiple workers, multiple instances need to be started. This can be easily done e.g. via Supervisor. The following Supervisor configuration file can be placed in /etc/supervisor/tasktiger.ini and runs 4 TaskTiger workers as the ubuntu user. For more information, read Supervisor’s documentation.


Workers support the following options:

  • -q, --queues

    If specified, only the given queue(s) are processed. Multiple queues can be separated by comma. Any subqueues of the given queues will be also processed. For example, -q first,second will process items from first, second, and subqueues such as first.CUSTOMER1, first.CUSTOMER2.

  • -e, --exclude-queues

    If specified, exclude the given queue(s) from processing. Multiple queues can be separated by comma. Any subqueues of the given queues will also be excluded unless a more specific queue is specified with the -q option. For example, -q email,email.incoming.CUSTOMER1 -e email.incoming will process items from the email queue and subqueues like email.outgoing.CUSTOMER1 or email.incoming.CUSTOMER1, but not email.incoming or email.incoming.CUSTOMER2.

  • -m, --module

    Module(s) to import when launching the worker. This improves task performance since the module doesn’t have to be reimported every time a task is forked. Multiple modules can be separated by comma.

    Another way to preload modules is to set up a custom TaskTiger launch script, which is described below.

  • -h, --host

    Redis server hostname (if different from localhost).

  • -p, --port

    Redis server port (if different from 6379).

  • -a, --password

    Redis server password (if required).

  • -n, --db

    Redis server database number (if different from 0).

  • -M, --max-workers-per-queue

    Maximum number of workers that are allowed to process a queue.

  • --store-tracebacks/--no-store-tracebacks

    Store tracebacks with execution history (config defaults to True).

  • --executor

    Can be fork (default) or sync. Whether to execute tasks in a separate process via fork, or execute them synchronously in the same proces. See “Features” section for the benefits of either approach.

  • --exit-after

    Exit the worker after the time in minutes has elapsed. This is mainly useful with the synchronous executor to prevent memory leaks from accumulating.

In some cases it is convenient to have a custom TaskTiger launch script. For example, your application may have a command that sets up the environment and you may want to launch TaskTiger workers using that script. To do that, you can use the run_worker_with_args method, which launches a TaskTiger worker and parses any command line arguments. Here is an example:

import sys
from tasktiger import TaskTiger

    command = sys.argv[1]
except IndexError:
    command = None

if command == 'tasktiger':
    tiger = TaskTiger(setup_structlog=True)
    # Strip the "tasktiger" arg when running via manage, so we can run e.g.
    # ./ tasktiger --help

Inspect, requeue and delete tasks

TaskTiger provides access to the Task class which lets you inspect queues and perform various actions on tasks.

Each queue can have tasks in the following states:

  • queued: Tasks that are queued and waiting to be picked up by the workers.

  • active: Tasks that are currently being processed by the workers.

  • scheduled: Tasks that are scheduled for later execution.

  • error: Tasks that failed with an error.

To get a list of all tasks for a given queue and state, use Task.tasks_from_queue. The method gives you back a tuple containing the total number of tasks in the queue (useful if the tasks are truncated) and a list of tasks in the queue, latest first. Using the skip and limit keyword arguments, you can fetch arbitrary slices of the queue. If you know the task ID, you can fetch a given task using Task.from_id. Both methods let you load tracebacks from failed task executions using the load_executions keyword argument, which accepts an integer indicating how many executions should be loaded.

Tasks can also be constructed and queued using the regular constructor, which takes the TaskTiger instance, the function name and the options described in the Task options section. The task can then be queued using its delay method. Note that the when argument needs to be passed to the delay method, if applicable. Unique tasks can be reconstructed using the same arguments.

The Task object has the following properties:

  • id: The task ID.

  • data: The raw data as a dict from Redis.

  • executions: A list of failed task executions (as dicts). An execution dict contains the processing time in time_started and time_failed, the worker host in host, the exception name in exception_name and the full traceback in traceback.

  • serialized_func, args, kwargs: The serialized function name with all of its arguments.

  • func: The imported (executable) function

The Task object has the following methods:

  • cancel: Cancel a scheduled task.

  • delay: Queue the task for execution.

  • delete: Remove the task from the error queue.

  • execute: Run the task without queueing it.

  • n_executions: Queries and returns the number of past task executions.

  • retry: Requeue the task from the error queue for execution.

  • update_scheduled_time: Updates a scheduled task’s date to the given date.

The current task can be accessed within the task function while it’s being executed: In case of a non-batch task, the current_task property of the TaskTiger instance returns the current Task instance. In case of a batch task the current_tasks property must be used which returns a list of tasks that are currently being processed (in the same order as they were passed to the task).

Example 1: Queueing a unique task and canceling it without a reference to the original task.

from tasktiger import TaskTiger, Task

tiger = TaskTiger()

# Send an email in five minutes.
task = Task(tiger, send_mail, args=['email_id'], unique=True)

# Unique tasks get back a task instance referring to the same task by simply
# creating the same task again.
task = Task(tiger, send_mail, args=['email_id'], unique=True)

Example 2: Inspecting queues and retrying a task by ID.

from tasktiger import TaskTiger, Task

QUEUE_NAME = 'default'
TASK_STATE = 'error'
TASK_ID = '6fa07a91642363593cddef7a9e0c70ae3480921231710aa7648b467e637baa79'

tiger = TaskTiger()

n_total, tasks = Task.tasks_from_queue(tiger, QUEUE_NAME, TASK_STATE)

for task in tasks:
    print(, task.func)

task = Task.from_id(tiger, QUEUE_NAME, TASK_STATE, TASK_ID)

Example 3: Accessing the task instances within a batch task function to determine how many times the currently processing tasks were previously executed.

from tasktiger import TaskTiger

tiger = TaskTiger()

def my_task(args):
    for task in tiger.current_tasks:

Pause queue processing

The --max-workers-per-queue option uses queue locks to control the number of workers that can simultaneously process the same queue. When using this option a system lock can be placed on a queue which will keep workers from processing tasks from that queue until it expires. Use the set_queue_system_lock() method of the TaskTiger object to set this lock.

Rollbar error handling

TaskTiger comes with Rollbar integration for error handling. When a task errors out, it can be logged to Rollbar, grouped by queue, task function name and exception type. To enable logging, initialize rollbar with the StructlogRollbarHandler provided in the tasktiger.rollbar module. The handler takes a string as an argument which is used to prefix all the messages reported to Rollbar. Here is a custom worker launch script:

import logging
import rollbar
import sys
from tasktiger import TaskTiger
from tasktiger.rollbar import StructlogRollbarHandler

tiger = TaskTiger(setup_structlog=True)

rollbar_handler = StructlogRollbarHandler('TaskTiger')


Cleaning Up Error’d Tasks

Error’d tasks occasionally need to be purged from Redis, so TaskTiger exposes a purge_errored_tasks method to help. It might be useful to set this up as a periodic task as follows:

from tasktiger import TaskTiger, periodic

tiger = TaskTiger()

def purge_errored_tasks():
            datetime.datetime.utcnow() - datetime.timedelta(weeks=12)

Running The Test Suite

Tests can be run locally using the provided docker compose file. After installing docker, tests should be runnable with:

docker-compose run --rm tasktiger pytest

Tests can be more granularly run using normal pytest flags. For example:

docker-compose run --rm tasktiger pytest tests/

Releasing a New Version

  1. Make sure the code has been thoroughly reviewed and tested in a realistic production environment.

  2. Update and Make sure you include any breaking changes.

  3. Run python sdist and twine upload dist/<PACKAGE_TO_UPLOAD>.

  4. Push a new tag pointing to the released commit, format: v0.13 for example.

  5. Mark the tag as a release in GitHub’s UI and include in the description the changelog entry for the version. An example would be:

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