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SQS Workers.

Project description

SQS Workers

How can I use it?

Unless you are the part of the Doist development team, you most likely don't need it. It's something opinionated, built out of our own internal needs and probably provides little value for outside developers.

Queue processors are in abundance (see for examples), and there is no shortage of SQS queue processors on PyPI, so please don't put your high hopes on this particular implementation

Got it, but how can I start using it anyway?

Install the package with

pip install sqs-workers

Configure your boto3 library to provide access requisites for your installation with something like this:

aws configure

Don't forget to set your preferred AWS region.

Then you will start managing two systems (most likely, from the same codebase): one of them adds messages to the queue and another one executes them.

from sqs_workers import SQSEnv, create_standard_queue

# This environment will use AWS requisites from ~/.aws/ directory
sqs = SQSEnv()

# Create a new queue.
# Note that you can use AWS web interface for the same action as well, the
# web interface provides more options. You only need to do it once.
create_standard_queue(sqs, "emails")

# Get the queue object
queue = sqs.queue("emails")

# Register a queue processor
def send_email(to, subject, body):
    print(f"Sending email {subject} to {to}")

Then there are two ways of adding tasks to the queue. Classic (aka "explicit"):

queue.add_job("send_email", to="", subject="Hello world", body="hello world")

And the "Celery way" (we mimic the Celery API to some extent)

send_email.delay(to="", subject="Hello world", body="hello world")

To process the queue you have to run workers manually. Create a new file which will contain the definition of the sqs object and register all processors (most likely, by importing necessary modules from your project), and then run SQS

from sqs_workers import SQSEnv
sqs = SQSEnv()

In production we usually don't handle multiple queues in the same process, but for the development environment it's easier to tackle with all the queues at once with



There are two serializers: json and pickle.

Baked tasks

You can create so-called "baked async tasks", entities which besides the task itself, contain arguments which have to be used to call the task.

Think of baked tasks as of light version of Celery signatures

task = send_email.bake(to='', subject='Hello world', body='hello world')

Is the same as

send_email.delay(to='', subject='Hello world', body='hello world')

Synchronous task execution

In Celery you can run any task synchronously if you just call it as a function with arguments. Our AsyncTask raises a RuntimeError for this case.

send_email(to='', subject='Hello world', body='hello world')
RuntimeError: Async task email.send_email called synchronously (probably,
by mistake). Use either to run the task synchronously
or AsyncTask.delay(...) to add it to the queue

If you want to run a task synchronously, use run() method of the task.'', subject='Hello world', body='hello world')

FIFO queues

Fifo queues can be created with create_fifo_queue and has to have the name which ends with ".fifo".

from sqs_workers import SQSEnv, create_fifo_queue
sqs = SQSEnv()

create_fifo_queue(sqs, 'emails_dead.fifo')
create_fifo_queue(sqs, 'emails.fifo',
    redrive_policy=sqs.redrive_policy('emails_dead.fifo', 3)

Unless the flag content_based_deduplication is set, every message has to be sent with an attribute _deduplication_id. By default all messages have the same message group default, but you can change it with _group_id.

    'send_email', _deduplication_id=subject, _group_id=email, **kwargs)

More about FIFO queues on AWS

Exception processing

If task processing ended up with an exception, the error is logged and the task is returned back to the queue after a while. The exact behavior is defined by queue settings.

Custom processors

You can define your own processor if you need to perform some specific actions before of after executing a specific task.

Example for the custom processor

from sqs_workers import SQSEnv
from sqs_workers.processors import Processor

class CustomProcessor(Processor):
    def process(self, job_kwargs):
        print(f'Processing {self.queue_name}.{self.job_name} with {job_kwargs}')
        super(CustomProcessor, self).process(job_kwargs)

sqs = SQSEnv(processor_maker=CustomProcessor)

Working with contexts

Context which is implicitly passed to the worker via the job message. Can be used there for logging or profiling purposes, for example.

Usage example.

queue = sqs.queue("q1")

@queue.processor('q1', 'hello_world', pass_context=True)
def hello_world(username=None, context=None):
    print(f'Hello {username} from {context["remote_addr"]}')

with sqs.context(remote_addr=''):


Alternatively, you can set the context like this.

sqs.context['remote_addr'] = ''

And then, when the context needs to be cleared:


In a web application you usually call it at the end of the processing of the web request.

Automatic applying of the context for all tasks

Instead of dealing with the context inside every processing function, you can perform this in processors by subclassing them.

import os
from sqs_workers import SQSEnv
from sqs_workers.processors import Processor

class CustomProcessor(Processor):
    def process(self, job_kwargs, job_context):
        os.environ['REMOTE_ADDR'] = job_context['remote_addr']
        super(CustomProcessor, self).process(job_kwargs, job_context)

sqs = SQSEnv(

Raw queues

Raw queues can have only one processor, and this should be a function, accepting message as its only argument.

Raw queues are helpful to process messages, added to SQS from external sources, such as CloudWatch events.

You start very much the same way, creating a new standard queue if needed.

from sqs_workers import SQSEnv, create_standard_queue
sqs = SQSEnv()
create_standard_queue(sqs, 'cron')

Then you get a queue, but provide a queue_maker parameter to it, to create a queue of the necessary type, and you define a processor for it.

from sqs_workers import RawQueue

cron = sqs.queue('cron', RawQueue)

def processor(message):

Then start processing your queue as usual:


You can also send raw messages to the queue, but this is probably less useful:

cron.add_raw_job("Hello world")

Processing Messages from CloudWatch

By default message body by CloudWatch scheduler has following JSON structure.

  "version": "0",
  "id": "a9a10406-9a1f-0ddc-51ae-08db551fac42",
  "detail-type": "Scheduled Event",
  "source": "",
  "account": "NNNNNNNNNN",
  "time": "2019-09-20T09:19:56Z",
  "region": "eu-west-1",
  "resources": [
  "detail": {}

Headers of the message:

    'SenderId': 'AIDAJ2E....',
    'ApproximateFirstReceiveTimestamp': '1568971264367',
    'ApproximateReceiveCount': '1',
    'SentTimestamp': '1568971244845',

You can pass any valid JSON as a message though, and it will be passed as is to the message body. Something like this:

{"message": "Hello world"}

Dead-letter queues and redrive

On creating the queue you can set the fallback dead-letter queue and redrive policy, which can look like this

from sqs_workers import SQSEnv, create_standard_queue
sqs = SQSEnv()

create_standard_queue(sqs, 'emails_dead')
create_standard_queue(sqs, 'emails',
    redrive_policy=sqs.redrive_policy('emails_dead', 3)

This means "move the message to the email_deadletters queue after four (3 + 1) failed attempts to send it to the recipient"

Backoff policies

You can define the backoff policy for the entire environment or for specific queue.

queue = sqs.queue("emails", backoff_policy=DEFAULT_BACKOFF)

def send_email(to, subject, body):
    print(f"Sending email {subject} to {to}")

Default policy is the exponential backoff. It's recommended to always set both backoff policy and dead-letter queue to limit the maximum number of execution attempts.

Alternatively you can set the backoff to IMMEDIATE_RETURN to re-execute failed task immediately.

queue = sqs.queue("emails", backoff_policy=IMMEDIATE_RETURN)

def send_email(to, subject, body):
    print(f"Sending email {subject} to {to}")

Shutdown policies

When launching the queue processor with process_queue(), it's possible to optionally set when it has to be stopped.

Following shutdown policies are supported:

  • IdleShutdown(idle_seconds): return from function when no new tasks are sent for specific period of time

  • MaxTasksShutdown(max_tasks): return from function after processing at least max_task jobs. Can be helpful to prevent memory leaks

Default policy is NeverShutdown. It's also possible to combine two previous policies with OrShutdown or AndShutdown policies, or create custom classes for specific behavior.

Example of stopping processing the queue after 5 minutes of inactivity:

from sqs_workers import SQSEnv
from sqs_workers.shutdown_policies import IdleShutdown

sqs = SQSEnv()

Processing dead-letter queue by pushing back failed messages

The most common way to process a dead-letter queue is to fix the main bug causing messages to appear there in the first place, and then to re-process these messages again.

With sqs-workers in can be done by putting back all the messages from a dead-letter queue back to the main one. While processing the queue, the processor takes every message and push it back to the upstream queue with a hard-coded delay of 1 second.

Usage example:

>>> from sqs_workers import JobQueue
>>> from sqs_workers.shutdown_policies IdleShutdown
>>> from sqs_workers.deadletter_queue import DeadLetterQueue
>>> env = SQSEnv()
>>> foo = env.queue("foo")
>>> foo_dead = env.queue("foo_dead", DeadLetterQueue.maker(foo))
>>> foo_dead.process_queue(shutdown_policy=IdleShutdown(10))

This code takes all the messages in foo_dead queue and push them back to the foo queue. Then it waits 10 seconds to ensure no new messages appear, and quit.

Using in unit tests with MemorySession

There is a special MemorySession which can be used as a quick'n'dirty replacement for real queues in unit tests. If you have a function create_task which adds some tasks to the queue and you want to test how it works, you ca technically write your tests like this:

from sqs_workers import SQSEnv
env = SQSEnv()

def test_task_creation_side_effects():

The problem is that your test starts depending on AWS (or localstack) infrastructure, which you don't always need. What you can do instead is you can pass MemorySession to your SQSEnv instance.

from sqs_workers import SQSEnv, MemorySession
env = SQSEnv(MemorySession())

Please note that MemorySession has some serious limitations, and may not fit well your use-case. Namely, when you work with MemorySession:

  • Redrive policy doesn't work
  • There is no differences between standard and FIFO queues
  • FIFO queues don't support content-based deduplication
  • Delayed tasks executed ineffectively: the task is gotten from the queue, and if the time hasn't come yet, the task is put back.
  • API can return slightly different results

Testing with AWS

Make sure you have all dependencies installed, and boto3 client configured (ref) and then run

pytest -k aws

Alternatively, to test all supported versions, run

tox -- -k aws

Testing with localstack

Localstack tests should perform faster than testing against AWS, and besides, they work well in offline.

Run localstack and make sure that the SQS endpoint is available by its default address http://localhost:4576

Then run

pytest -k localstack


tox -- -k localstack

Why it depends on werkzeug? 😱

The only reason is werkzeug.utils.validate_arguments which we love and we are lazy enough to move it to this codebase.

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