arazzo: 1.0.1
info:
  title: Amazon Neptune ML Stop Data Processing Job
  summary: Inspect a Neptune ML data processing job and stop it with cleanup only if it is still running.
  description: >-
    A guarded stop flow for Neptune ML data processing jobs. The workflow reads
    the current status of a data processing job, and then branches: when the job
    is not yet Completed or Failed it stops the job and deletes its S3 artifacts
    via the clean flag, and when the job has already reached a terminal state it
    ends without action. Every step spells out its request inline so the flow can
    be read and executed without opening the underlying OpenAPI description.
  version: 1.0.0
sourceDescriptions:
- name: neptuneMlApi
  url: ../openapi/amazon-neptune-ml-openapi.yml
  type: openapi
workflows:
- workflowId: ml-stop-dataprocessing-job
  summary: Stop a data processing job and clean its artifacts only if still running.
  description: >-
    Reads a data processing job's status and stops it with cleanup only when it
    is not yet Completed or Failed.
  inputs:
    type: object
    required:
    - id
    properties:
      id:
        type: string
        description: The unique identifier of the data processing job.
  steps:
  - stepId: getStatus
    description: >-
      Read the current status of the data processing job so the stop decision
      can be made.
    operationId: getDataProcessingJobStatus
    parameters:
    - name: id
      in: path
      value: $inputs.id
    successCriteria:
    - condition: $statusCode == 200
    outputs:
      status: $response.body#/status
    onSuccess:
    - name: stillRunning
      type: goto
      stepId: stopJob
      criteria:
      - context: $response.body
        condition: $.status != "Completed" && $.status != "Failed"
        type: jsonpath
    - name: alreadyTerminal
      type: end
      criteria:
      - context: $response.body
        condition: $.status == "Completed" || $.status == "Failed"
        type: jsonpath
  - stepId: stopJob
    description: >-
      Stop the running data processing job and delete its S3 artifacts via the
      clean flag.
    operationId: stopDataProcessingJob
    parameters:
    - name: id
      in: path
      value: $inputs.id
    - name: clean
      in: query
      value: true
    successCriteria:
    - condition: $statusCode == 200
    outputs:
      stopStatus: $statusCode
  outputs:
    status: $steps.getStatus.outputs.status
    stopStatus: $steps.stopJob.outputs.stopStatus