Replies: 6 comments 6 replies
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HealthcheckThe general idea is to provide a way to get a healthcheck from the broker. I start this thread where it would be nice to be able to ask the broker if things are health. It in turn check for the backends (as configured by the application) |
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Interval based schedulesMy current use case requires that tasks be executed in intervals set by a user. If I understood correctly the time parameter is only useful for one off tasks, and cron only has minutes as it's lowest value. Celery does this by allowing setting the schedule as seconds or a timedelta for more precise timings. |
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Retry JobsThere should be option to manually retry the job by some mechanism cc: @s3rius |
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A way to get job intermediate statusFor example: import taskiq_redis
import taskiq
# ... Broker construction
broker = ...
@broker.task()
async def my_task(x: int)->int:
return int*2
job = await my_task.find_job('job_id') # Same object returned when kicking
assert job.status == taskiq.JobStatus.RUNNING
schedule = await my_task.get_schedule('schedule_id')
assert schedule.status == taskiq.ScheduleStatus.PENDING
assert schedule.status == taskiq.ScheduleStatus.KICKED
print(schedule.next_time) # Calculated from cron expression or a raw datetime, if periodic or not kicked, `None` otherwise. The schedule_id can be a |
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Hi It would be nice to have worker-scoped generator dependencies. I was kinda expecting generator dependencies to be this out of the box, based on experience with other projects. |
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Feature Request: Support Python 3.14 Subinterpreters for Running Workers Hi Team, With Python 3.14 significantly improving subinterpreter support (especially after PEP 684, which enables better memory isolation and shared resources between subinterpreters), I’m wondering if there’s a plan to add subinterpreters as a new executor option for running Taskiq workers. Why this matters: Compared to multiprocess workers: Subinterpreters are lighter (share part of the main process memory, e.g., core Python interpreter and read-only dependencies), reducing redundant memory overhead when scaling workers. For scenarios where we need many workers (e.g., 10+), subinterpreters could balance performance and memory efficiency. For example, in a chatbot command-processing service, subinterpreters would avoid the high memory cost of 10+ independent processes while still leveraging for concurrent task execution. Questions:
Thanks for building such a great task queue tool! |
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Here we want to discuss new feature Ideas we want to see in taskiq.
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