tayaatom.blogg.se - Python enqueue

-retry-interval Interval between retries in seconds.-at-front Will place the job at the front of the queue, instead.-depends-on Specifies another job id that must complete before this.-description Additional description of the job.-failure-ttl Specifies how long failed jobs are kept.-ttl Specifies the maximum queued time of the job before.

-result-ttl Specifies how long successful jobs and their results.-timeout Specifies the maximum runtime of the job before it is.To your job and not to RQ’s enqueue function, this is what you do: In the last case, if you want to pass description and ttl keyword arguments This is useful if your function happens to haveĬonflicting argument names with RQ, for example description or ttl. args and kwargs: use these to explicitly pass arguments and keyword to the.on_failure allows you to run a function after a job fails.on_success allows you to run a function after a job completes successfully.description to add additional description to enqueued jobs.at_front will place the job at the front of the queue, instead of the.job_id allows you to manually specify this job’s job_id.depends_on specifies another job (or list of jobs) that must complete before this.failure_ttl specifies how long failed jobs are kept (defaults to 1 year).

This argument defaults to None (infinite TTL).

ttl specifies the maximum queued time (in seconds) of the job before it’s discarded.

Expired jobs will be automatically deleted.

result_ttl specifies how long (in seconds) successful jobs and their.

Furthermore, it can be a string with specify unit including hour, minute, second(e.g. Its default unit is second and it can be an integer or a string representing an integer(e.g.

job_timeout specifies the maximum runtime of the job before it’s interruptedĪnd marked as failed.

By default, these are popped out of the kwargs that will be passed to the In addition, you can add a few options to modify the behaviour of the queued Pattern is to name your queues after priorities (e.g. You can quite flexibly distribute work to your own desire. Notice the Queue('low') in the example above? You can use any queue name, so Q = Queue ( 'low', connection = redis_conn ) q.