+
+

Get Jobs

+

AutomationAPI Documentation

+

The get_jobs() method in the Control-M Python Client provides an easy way to retrieve job and folder definitions from the Control-M Server. It wraps the deploy jobs::get AAPI command and deserializes the resulting JSON into Python objects, allowing users to fetch and work with jobs and folders in a way that mirrors the state before deployment.

+

This guide demonstrates how to use get_jobs() to fetch jobs from the Control-M server.

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+
[2]:
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+
from ctm_python_client.core.workflow import Workflow, WorkflowDefaults
+from ctm_python_client.core.comm import Environment
+from aapi import *
+
+# Step 1: Define the environment (Control-M Workbench in this case)
+env = Environment.create_workbench('workbench')
+
+# Step 2: Define your workflow with jobs and folders
+workflow = Workflow(env, WorkflowDefaults(run_as='workbench'))
+workflow.clear_all()
+run_as_dummy = True
+
+# Define jobs
+databricksLoad = JobDatabricks(
+    'LoadForecasts',
+    connection_profile='DATABRICKS',
+    databricks_job_id='991955986358417',
+    parameters='"params":{}',
+    idempotency_token='tokeni_%%ORDERID',
+    run_as_dummy=run_as_dummy
+)
+
+sagemaker_job = JobAwsSageMaker(
+    'InventoryForecastModel',
+    connection_profile='SAGEMAKER',
+    pipeline_name='InferencePipeline',
+    idempotency_token='Token_ControlM_for_SageMaker%%ORDERID',
+    add_parameters='checked',
+    parameters='{"Name":"input_file", "Value": "file"}',
+    run_as_dummy=run_as_dummy,
+    retry_pipeline_execution='unchecked',
+    rerun_limit=Job.RerunLimit(times='5')
+)
+
+# Define folders
+folder1 = Folder('Folder_Sanity_1', controlm_server='workbench', job_list=[databricksLoad])
+folder2 = Folder('Folder_Sanity_2', controlm_server='workbench', job_list=[sagemaker_job])
+
+workflow.add(folder1)
+workflow.add(folder2)
+
+# Step 3: Deploy workflow
+workflow.build()
+workflow.deploy()
+
+# Step 4: Retrieve jobs after deployment
+workflow_actual = Workflow.get_jobs(env, server="workbench", folder="Folder_Sanity_*")
+
+print(workflow.dumps_json(indent=2))
+
+
+
+
+
+
+
+
+{
+  "Folder_Sanity_1": {
+    "Type": "Folder",
+    "ControlmServer": "workbench",
+    "LoadForecasts": {
+      "Type": "Job:Databricks",
+      "RunAs": "workbench",
+      "RunAsDummy": true,
+      "ConnectionProfile": "DATABRICKS",
+      "Databricks Job ID": "991955986358417",
+      "Parameters": "\"params\":{}",
+      "Idempotency Token": "tokeni_%%ORDERID"
+    },
+    "RunAs": "workbench"
+  },
+  "Folder_Sanity_2": {
+    "Type": "Folder",
+    "ControlmServer": "workbench",
+    "InventoryForecastModel": {
+      "Type": "Job:AWS SageMaker",
+      "RerunLimit": {
+        "Times": "5"
+      },
+      "RunAs": "workbench",
+      "RunAsDummy": true,
+      "ConnectionProfile": "SAGEMAKER",
+      "Pipeline Name": "InferencePipeline",
+      "Idempotency Token": "Token_ControlM_for_SageMaker%%ORDERID",
+      "Add Parameters": "checked",
+      "Parameters": "{\"Name\":\"input_file\", \"Value\": \"file\"}",
+      "Retry Pipeline Execution": "unchecked"
+    },
+    "RunAs": "workbench"
+  }
+}
+
+
+
+

Arguments

+
    +
  • +
    environment: Environment : The Control-M environment to connect to. Required.
    +
    This object defines the Control-M endpoint (Automation API endpoint, same as Control-M/EM), the authentication method, and the environment mode (on-prem or SaaS).
    +
    You can create an Environment instance using one of the following static methods:
    +
    +
      +

    • create_workbench() – for local development with Workbench.

      • +

    • create_onprem(host, username, password) – for on-premises Control-M using username/password authentication.

      • +

    • create_saas(endpoint, api_key) – for Helix Control-M (SaaS) using an API key. +The environment determines how to authenticate and which API variant to use, depending on whether the backend is Control-M or Helix Control-M.

      • +
    +
    • +
  • +
    server: str : The exact Control-M/Server name to query. Required.
    +
    No wildcards allowed.
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    +
    • +
  • +
    folder: str : The folder name or pattern to fetch. Required.
    +
    Supports wildcards (e.g., “MyFolder_*”). Filters jobs by folder name.
    +
    +
    • +
  • +
    job: str (Not supported yet)
    +
    Currently not supported. The API ignores this parameter if provided.
    +
    +
    • +
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