Metadata-Version: 2.1
Name: sqs-workers
Version: 0.3.4
Summary: SQS Workers.
Home-page: https://github.com/Doist/sqs-workers
Author: Doist Developers
Author-email: dev@doist.com
License: MIT
Description: 
        SQS Workers
        ===========
        
        How can I use it?
        -----------------
        
        Unless you are the part of the [Doist development team](https://github.com/orgs/Doist/people), 
        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 http://queues.io/ for examples), and
        there is no shortage of SQS queue processors on
        [PyPI](https://pypi.org/search/?q=SQS), 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
        
        ```bash
        pip install sqs-workers
        ```
        
        Configure your boto3 library to provide access requisites for your installation
        with [something like this](https://boto3.readthedocs.io/en/latest/guide/quickstart.html#configuration):
        
        ```bash
        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.
        
        ```python
        from sqs_workers import SQSEnv
        
        # 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.
        sqs.create_standard_queue('emails')
        
        # Register a queue processor
        @sqs.processor('emails', 'send_email')
        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"):
        
        ```python
        sqs.add_job(
            'emails', 'send_email', to='user@examile.com', subject='Hello world', body='hello world')
        ```
        
        
        And the "Celery way" (we mimic the Celery API to some extent)
        
        ```python
        send_email.delay(to='user@examile.com', 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
        
        ```python
        from sqs_workers import SQSEnv
        sqs = SQSEnv()
        ...
        sqs.process_queue('emails')
        ```
        
        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
        
        ```python
        sqs.process_queues()
        ```
        
        Serialization
        -------------
        
        There are two serializers: json and pickle.
        
        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.
        
        Batch processing
        ----------------
        
        Instead of using `sqs.processor` decorator you can use `sqs.batch_processor`.
        In this case the function must accept parameter "messages" containing
        the list of dicts.
        
        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
        
        ```python
        from sqs_workers import SQSEnv
        sqs = SQSEnv()
        sqs.create_standard_queue('emails_deadletters')
        sqs.create_standard_queue('emails', 
            redrive_policy=sqs.redrive_policy('emails_deadletters', 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
        processor. 
        
        
        ```python
        @sqs.processor('emails', 'send_email', 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.
        
        ```python
        @sqs.processor('emails', 'send_email', 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 seend 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:
        
        ```python
        from sqs_workers import SQSEnv
        from sqs_workers.shutdown_policies import IdleShutdown
        
        sqs = SQSEnv()
        sqs.process_queue('foo', shutdown_policy=IdleShutdown(idle_seconds=300))
        ```
        
        Processing dead-letter queue
        ----------------------------
        
        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. It can be performed with a special
        fallback processor called DeadLetterProcessor.
        
        The dead-letter processor has opinion on how queues are organized and uses some
        hard-coded options.
        
        It is supposed to process queues "something_dead" which is supposed to be
        a configured dead-letter queue for "something". While processing the queue,
        the processor takes every message and push it back to the queue "something"
        with a hard-coded delay of 1 second.
        
        If the queue name does't end with "_dead", the DeadLetterProcessor behaves
        like generic FallbackProcessor: shows the error message and keep message in
        the queue. It's made to prevent from creating infinite loops when the
        message from the dead letter queue is pushed back to the same queue, then
        immediately processed by the same processor again, etc.
        
        Usage example:
        
        ```python
        from sqs_workers import SQSEnv
        from sqs_workers.processors import DeadLetterProcessor
        from sqs_workers.shutdown_policies import IdleShutdown
        
        env = SQSEnv(fallback_processor_maker=DeadLetterProcessor)
        env.process_queue("foo_dead", 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 MemoryEnv
        ----------------------------------
        
        There is a special MemoryEnv 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 can technically 
        write your tests like this:
        
        ```python
        from sqs_workers import SQSEnv
        env = SQSEnv()
        
        def test_task_creation_side_effects():
            create_task()
            env.process_batch('foo')
            ...
        ```
        
        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 replace SQSEnv with a MemoryEnv() and rewrite your tests like this.
        
        ```python
        from sqs_workers.memory_env import MemoryEnv
        env = MemoryEnv()
        ```
        
        Please note that MemoryEnv has some serious limitations, and may not fit well
        your use-case. Namely, when you work with MemoryEnv:
        
        - 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](https://boto3.readthedocs.io/en/latest/guide/quickstart.html#configuration))
        and then run
        
        ```bash
        pytest -k aws
        ```
        
        Alternatively, to test all supported versions, run
        
        ```bash
        tox -- -k aws
        ```
        
        Testing with localstack
        -----------------------
        
        Localstack tests should perform faster than testing against AWS, and besides,
        they work well in offline.
        
        Run [localstack](https://github.com/localstack/localstack) and make sure
        that the SQS endpoint is available by its default address http://localhost:4576
        
        Then run
        
        ```bash
        pytest -k localstack
        ```
        
        or
        
        ```bash
        tox -- -k localstack
        ```
        
        
        Why it depends on werkzeug? 😱
        ------------------------------
        
        The only reason is [werkzeug.utils.validate_arguments](http://werkzeug.pocoo.org/docs/dev/utils/#werkzeug.utils.validate_arguments)
        which we love and we are lazy enough to move it to this codebase.
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=2.7
Description-Content-Type: text/markdown
