Close popover

Table of Contents

Monitoring data flows

Version:

Use the Call instruction with several activities to track the status of data flows that were run in batch mode with the Call Data-Decision-DDF-RunOptions.pxRunDDFWithProgressPage method or submitted on the Data Flows landing page. You can track the number of processed records, and the elapsed or the remaining time of the data flow run.

  1. Create an instance of the Activity rule in the Dev Studio navigation panel by clicking Records Technical Activity .

  2. In the activity steps, provide the pyWorkObjectID property in order to identify which data flow run you want to monitor.

  3. In the activity steps, enter one of the following methods to monitor a data flow:

    • Call Data-Decision-DDF-RunOptions.pxInitializeProgressPage - Creates the progress page that consists of a top level page named Progress of the Data-Decision-DDF-Progress data type.
    • Call Data-Decision-DDF-Progress.pxLoadProgress - Updates the current status.
  4. Click Save.

Apart from the API methods for data flows, you can use a default section and harness to display and control execution progress of data flow runs:

  • The Data-Decision-DDF-Progress.pyProgress section displays recent information. This section, which is also used on the Data Flows landing page, refreshes periodically to update the progress information.
  • The Data-Decision-DDF-RunOptions.pxDDFProgress harness, which is also used in the run dialog box of the Data Flow rule, displays the complete harness for the data flow run. It provides the progress section and the action buttons that you use to start, stop, and restart the data flow run.

Have a question? Get answers now.

Visit the Collaboration Center to ask questions, engage in discussions, share ideas, and help others.