Timed execution scheme for automated data analysis workflows
To enable data analysis workflows (such as the example in Data Analysis.7z) to run automatically at regular intervals:
- Using the dedify-schedule plugin: This is a timed task solution developed specifically for Dify, which allows you to set the frequency of execution and trigger conditions for the workflow.
- External dispatch system integration: Combine common scheduling tools such as crontab (Linux), Windows Task Scheduler or Airflow to trigger workflows via APIs
- Event-based triggering: Configure workflows to listen to a number of database changes or file directory changes for quasi-real-time analysis.
- Workflow Chaining Calls: Create a master workflow that calls the data analysis sub-workflow at preset intervals
Specific configuration steps include: 1) enable schedule plugin in Dify's plugin management; 2) add time trigger node for the data analysis workflow; 3) set the execution period (daily, hourly, etc.); and 4) specify the output method of the results (email notification, database storage, etc.).
For complex data analysis processes that include Flask services, it is recommended that the timing logic be deployed on the server side rather than relying on Dify's own scheduling capabilities.
This answer comes from the articleDify Workflow DSL Files Collection DownloadThe
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