Notebook Trigger
The Notebook Trigger will fire a workflow whenever the selected WEGnology Notebook has completed an execution (either successfully, or with errors).
Node Properties
The trigger only takes one parameter:
Notebook Configuration
A notebook that you previously created. Then, whenever an execution of that notebook completes, the workflow will trigger with the result of that execution.
Payload
{
"time": <time of the message>,
"data": {
"success": < true / false >,
"notebook": {
..information about the notebook...
},
"execution": {
...information about the particular execution of the notebook...
}
},
"applicationId": <id of the current application>,
"applicationName": <name of the current application>,
"triggerId": <id of the notebook>,
"triggerType": "notebook",
"flowId": <id of the current workflow>,
"flowName": <name of the current workflow>,
"flowVersion": <version of the current workflow>,
"globals": <object of workflow globals>
}
In addition to the standard workflow payload information, the payload will include an object in the data
field, which will include the following properties:
- success: This will be
true
if the notebook execution succeeded, andfalse
if the notebook execution failed. If the notebook execution failed, more details can be found in theexecutionErrors
array under theexecution
object. - notebook: This is an object containing all the information about the notebook that was run. It conforms to the Notebook API Schema.
- execution: This is an object containing all the information about the particular execution of the notebook. It conforms to the Notebook Execution Schema.
For the example workflow above, a specific payload for a triggered workflow might look like this:
{
"time": Fri Feb 19 2016 17:26:00 GMT-0500 (EST),
"data": {
"success": true,
"notebook": {
"id": "56c74add0d3d350100043380",
"notebookId": "56c74add0d3d350100043380",
"applicationId": "568beedeb436ab01007be53d",
"name": "Your Notebook",
"creationDate": "2019-03-10T21:04:44.293Z",
"lastUpdated": "2019-03-12T20:17:49.879Z",
"jupyterFileName": "yourNotebook.ipynb",
"jupyterFileUrl": "https://example.com/yourNotebook.ipynb",
"inputs": [{
"queryJson": "{ }",
"dataTableId": "5c9e2e4eab635b000620d17d",
"inputType": "dataTable",
"fileName": "example.csv"
}],
"outputs": [{
"outputType":"executionResult",
"fileName":"result.ipynb"
}]
},
"execution": {
"id": "5cb53464d0103e0006f34c74",
"notebookExecutionId": "5cb53464d0103e0006f34c74",
"notebookId": "56c74add0d3d350100043380",
"applicationId": "568beedeb436ab01007be53d",
"outputsCompletedAt": "2019-04-18T18:30:34.368Z",
"notebookCompletedAt": "2019-04-18T18:30:32.368Z",
"inputsCompletedAt": "2019-04-18T18:30:31.368Z",
"runStartedAt": "2019-04-18T18:30:30.368Z",
"runQueuedAt": "2019-04-18T18:30:29.368Z",
"maxAllowedRunMinutes": 5,
"executionRelativeTo": "2019-04-18T18:30:29.368Z",
"status": "completed",
"sourceType": "user",
"sourceId": "58c314ec27f78200017d4ee1",
"executionErrors": [],
"inputInfo": {
"example.csv": {
"contentType": "text/csv",
"url": "https://example.com/example.csv",
"size": 12345
}
},
"outputInfo": {
"result.ipynb": {
"url": "https://example.com/result.ipynb",
"size": 54321
}
}
}
},
"applicationId": "568beedeb436ab01007be53d",
"applicationName": "Equipment Monitor",
"triggerId": "56c74add0d3d350100043380",
"triggerType": "notebook",
"flowId": "56c74add04d0b50100043381",
"flowName": "Model Trained",
"flowVersion": "develop",
"globals": {}
}
Related Nodes
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