Job Manager Service - API Overview¶

Offers execution mechanisms for models stored in Model Management module. Jobs can use optional input and output parameters; the parameters used in jobs have four different types: Data Exchange folders, IoT Timeseries dataset exports, Predictive Learning Storage bucket locations or Integrated Data Lake paths. The locations of these will be passed to the executing models via variables with fixed names: inputFolder, outputFolder and datasetName. Job scheduling requires a model and a cron expression; this run the model multiple times according to the cron expression. Each time a model runs a new job is generated.
When the schedule is created it will start according to the cron expression. If a user wants to stop the executions it can update the status of the schedule and the executions will stop. Also the user has the possibility to update again the status and trigger again the schedule.
If a user does not want to run a model multiple times or based on a schedule expression it can start a new job.

Offers execution mechanisms for running models stored in Model Management module. A schedule is created based on a model identifier and a cron expression so the model will run one or multiple times.
Each time a model runs a new job is generated.
When the schedule is created it will start according to the cron expression. If a user wants to stop the executions it can update the status of the schedule and the executions will stop. Also the user has the possibility to update again the status and trigger again the schedule.
If a user does not want to run a model multiple times or based on a schedule expression it can start immediately a new job.