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As such, you need to have a \nPostgreSQL database that the scheduler can interact with. Database \ntables will be created automatically if they don't exist yet. You can\npass the database connection parameters to the `Scheduler()` \nconstructor with the `dbname`, `dbhost`, `dbuser`, `dbpassword` and\n`dbport` keyword arguments. \n\n## Example\n\n```\nimport time\nfrom dmi_scheduler.scheduler import Scheduler\n\nscheduler = Scheduler(config=\"scheduler.yml\")\nfor i in range(0, 100):\n    scheduler.queue.add_job(\"log_number.py\")\n\nwhile scheduler.has_jobs():\n    time.sleep(1)\n\nscheduler.end()\nprint(\"Done! Results can be found in the log file.\") \n```\n\n`log_number.py`:\n```\nfrom random import choice\nfrom dmi_scheduler.worker import BasicWorker\n\nclass SomeWorker(BasicWorker):\n\tmax_workers = 3\n\n\tdef work(self):\n\t\tself.log.info(\"Here is a random number: %i\" % choice(range(0,1000)))\n```\n\n## License\nThis scraper was developed by the \n[Digital Methods Initiative](https://digitalmethods.net), and is distributed\nunder the MIT license. See LICENSE for details.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdigitalmethodsinitiative%2Fdmi-scheduler","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdigitalmethodsinitiative%2Fdmi-scheduler","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdigitalmethodsinitiative%2Fdmi-scheduler/lists"}