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README.md

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At first you need to create a broker. Broker is an object that can communicate to workers using distributed queues.
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We have differet brokers for different queue backends. For example, we have a broker for [NATS](https://github.com/taskiq-python/taskiq-nats), [Redis](https://github.com/taskiq-python/taskiq-redis), [RabbitMQ](https://github.com/taskiq-python/taskiq-aio-pika), [Kafka](https://github.com/taskiq-python/taskiq-aio-kafka) and even more. Choose the one that fits you and create an instance.
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We have different brokers for different queue backends. For example, we have a broker for [NATS](https://github.com/taskiq-python/taskiq-nats), [Redis](https://github.com/taskiq-python/taskiq-redis), [RabbitMQ](https://github.com/taskiq-python/taskiq-aio-pika), [Kafka](https://github.com/taskiq-python/taskiq-aio-kafka) and even more. Choose the one that fits you and create an instance.
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```python
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from taskiq_nats import JetStreamBroker

docs/README.md

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head:
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- - meta
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- name: "google-site-verification"
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content: "hQCR5w2tmeuOvYIYXsOYU3u4kLNwT86lnqltANYlRQ0"
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content: "hQCR5w2tmeuOvYIYXsYOU3u4kLNwT86lnqltANYlRQ0"
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- - meta
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- name: "msvalidate.01"
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content: "97DC185FE0A2F5B123861F0790FDFB26"

docs/available-components/schedule-sources.md

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@broker.task(
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schedule=[
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{
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"cron": "* * * * *", # type: str, either cron or time shoule be specified.
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"cron_offset": None # type: str | timedelta | None, can be ommited.
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"time": None # type: datetime | None, either cron or time shoule be specified.
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"cron": "* * * * *", # type: str, either cron or time should be specified.
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"cron_offset": None # type: str | timedelta | None, can be omitted.
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"time": None # type: datetime | None, either cron or time should be specified.
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"args": [], # type List[Any] | None, can be omitted.
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"kwargs": {}, # type: Dict[str, Any] | None, can be omitted.
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"labels": {}, # type: Dict[str, Any] | None, can be omitted.

docs/framework_integrations/faststream.md

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# Taskiq + FastStream
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[FastStream](https://faststream.airt.ai/latest/) is a library that allows you to write consumers and producers for different message brokers almost like taskiq. But the differense is that taskiq is more focused on tasks for a specific project and more like celery but async, while FastStream is more focused on events and defining how different systems communicate with each other using distributed brokers.
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[FastStream](https://faststream.airt.ai/latest/) is a library that allows you to write consumers and producers for different message brokers almost like taskiq. But the difference is that taskiq is more focused on tasks for a specific project and more like celery but async, while FastStream is more focused on events and defining how different systems communicate with each other using distributed brokers.
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If you want to declare communication between different projects you can use taskiq, but it might be a bit more complex than using FastStream.
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docs/framework_integrations/taskiq-with-aiogram.md

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# Taskiq calls this function when starting the worker.
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@dp.startup()
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async def setup_taskiq(bot: Bot, *_args, **_kwargs):
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# Here we check if it's a clien-side,
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# Here we check if it's a client-side,
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# Because otherwise you're going to
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# create infinite loop of startup events.
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if not broker.is_worker_process:

docs/framework_integrations/taskiq-with-fastapi.md

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```
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And that's it. Now you can use your taskiq tasks with functions and classes that depend on FastAPI dependenices. You can find bigger examples in the [examples repo](https://github.com/taskiq-python/examples/).
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And that's it. Now you can use your taskiq tasks with functions and classes that depend on FastAPI dependencies. You can find bigger examples in the [examples repo](https://github.com/taskiq-python/examples/).
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## Testing

docs/guide/cli.md

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- `--log-level` is used to set a log level (default `INFO`).
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* `--max-async-tasks` - maximum number of simultaneously running async tasks.
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* `--max-prefetch` - number of tasks to be prefetched before execution. (Useful for systems with high message rates, but brokers should support acknowledgements).
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* `--max-threadpool-threads` - number of threads for sync function exection.
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* `--max-threadpool-threads` - number of threads for sync function execution.
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* `--no-propagate-errors` - if this parameter is enabled, exceptions won't be thrown in generator dependencies.
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* `--receiver` - python path to custom receiver class.
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* `--receiver_arg` - custom args for receiver.

docs/guide/scheduling-tasks.md

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Sometimes, you want to be specific in terms of time zones. We have you covered.
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Our `ScheduledTask` model has fields for that. Use these fields or not, it's up to the specific schedule source.
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Taskiq scheduler assumes that if time has no specific timezone, it's in [UTC](https://www.wikiwand.com/en/Coordinated_Universal_Time). Sometimes, this behavior might not be convinient for developers.
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Taskiq scheduler assumes that if time has no specific timezone, it's in [UTC](https://www.wikiwand.com/en/Coordinated_Universal_Time). Sometimes, this behavior might not be convenient for developers.
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For the `time` field of `ScheduledTask` we use timezone information from datetime to check if a task should run.
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By default, when you start the scheduler it will get all tasks from the schedule source and check whether they should have been executed in this minute. If tasks should have been executed, they will be executed.
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This behaviour might be not convinient for some developers. For example, if you have a task that should be executed on every minute, it will be executed once you start the scheduler, even if it was executed a few seconds ago.
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This behaviour might be not convenient for some developers. For example, if you have a task that should be executed on every minute, it will be executed once you start the scheduler, even if it was executed a few seconds ago.
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To avoid this behaviour, you can pass the `--skip-first-run` flag to the `taskiq scheduler` command. In this case, the scheduler will wait until the start of the next minute and then start executing tasks.
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Or it can be done manually, by calling `delete_schedule` on schedule source providing it whith `schedule_id`.
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Or it can be done manually, by calling `delete_schedule` on schedule source providing it with `schedule_id`.
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```python
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await redis_source.delete_schedule(schedule.schedule_id)

docs/guide/testing-taskiq.md

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:::
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To test the task itself, it's not different to the example without dependencies, but we jsut need to pass all
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To test the task itself, it's not different to the example without dependencies, but we just need to pass all
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expected dependencies manually as function's arguments or key-word arguments.
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```python

taskiq/abc/broker.py

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Mehtod is used to register tasks.
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Method is used to register tasks.
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By default we register tasks in local task registry.
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But this behaviour can be changed in subclasses.

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