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Release/2.5.0 (#133)
* docs: update documentation and bump version for release * fix comment --------- Co-authored-by: shacharl <[email protected]>
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‎docs/_sources/index.rst.txt

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@@ -22,6 +22,7 @@ Control-M Python Client is built for data scientists and developers who prefer a
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notebooks/jobproperties
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notebooks/connectionprofiles
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notebooks/run_ondemand
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notebooks/get_jobs
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beyond
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.. toctree::

‎docs/_sources/notebooks/get_jobs.ipynb.txt

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "060c8aab",
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"metadata": {},
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"source": [
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"# Get Jobs"
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]
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},
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{
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"cell_type": "markdown",
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"id": "06976a85",
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"metadata": {},
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"source": [
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"[AutomationAPI Documentation](https://documents.bmc.com/supportu/API/Monthly/en-US/Documentation/API_Services_DeployService.htm#deploy3)\n",
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"\n",
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"\n",
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"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.\n",
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"\n",
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"This guide demonstrates how to use get_jobs() to fetch jobs from the Control-M server."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "93e45a7e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\n",
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" \"Folder_Sanity_1\": {\n",
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" \"Type\": \"Folder\",\n",
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" \"ControlmServer\": \"workbench\",\n",
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" \"LoadForecasts\": {\n",
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" \"Type\": \"Job:Databricks\",\n",
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" \"RunAs\": \"workbench\",\n",
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" \"RunAsDummy\": true,\n",
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" \"ConnectionProfile\": \"DATABRICKS\",\n",
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" \"Databricks Job ID\": \"991955986358417\",\n",
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" \"Parameters\": \"\\\"params\\\":{}\",\n",
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" \"Idempotency Token\": \"tokeni_%%ORDERID\"\n",
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" },\n",
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" \"RunAs\": \"workbench\"\n",
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" },\n",
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" \"Folder_Sanity_2\": {\n",
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" \"Type\": \"Folder\",\n",
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" \"ControlmServer\": \"workbench\",\n",
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" \"InventoryForecastModel\": {\n",
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" \"Type\": \"Job:AWS SageMaker\",\n",
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" \"RerunLimit\": {\n",
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" \"Times\": \"5\"\n",
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" },\n",
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" \"RunAs\": \"workbench\",\n",
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" \"RunAsDummy\": true,\n",
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" \"ConnectionProfile\": \"SAGEMAKER\",\n",
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" \"Pipeline Name\": \"InferencePipeline\",\n",
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" \"Idempotency Token\": \"Token_ControlM_for_SageMaker%%ORDERID\",\n",
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" \"Add Parameters\": \"checked\",\n",
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" \"Parameters\": \"{\\\"Name\\\":\\\"input_file\\\", \\\"Value\\\": \\\"file\\\"}\",\n",
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" \"Retry Pipeline Execution\": \"unchecked\"\n",
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" },\n",
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" \"RunAs\": \"workbench\"\n",
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" }\n",
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"}\n"
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]
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}
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],
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"source": [
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"from ctm_python_client.core.workflow import Workflow, WorkflowDefaults\n",
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"from ctm_python_client.core.comm import Environment\n",
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"from aapi import *\n",
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"\n",
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"# Step 1: Define the environment (Control-M Workbench in this case)\n",
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"env = Environment.create_workbench('workbench')\n",
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"\n",
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"# Step 2: Define your workflow with jobs and folders\n",
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"workflow = Workflow(env, WorkflowDefaults(run_as='workbench'))\n",
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"workflow.clear_all()\n",
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"run_as_dummy = True\n",
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"\n",
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"# Define jobs\n",
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"databricksLoad = JobDatabricks(\n",
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" 'LoadForecasts',\n",
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" connection_profile='DATABRICKS',\n",
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" databricks_job_id='991955986358417',\n",
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" parameters='\"params\":{}',\n",
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" idempotency_token='tokeni_%%ORDERID',\n",
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" run_as_dummy=run_as_dummy\n",
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")\n",
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"\n",
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"sagemaker_job = JobAwsSageMaker(\n",
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" 'InventoryForecastModel',\n",
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" connection_profile='SAGEMAKER',\n",
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" pipeline_name='InferencePipeline',\n",
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" idempotency_token='Token_ControlM_for_SageMaker%%ORDERID',\n",
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" add_parameters='checked',\n",
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" parameters='{\"Name\":\"input_file\", \"Value\": \"file\"}',\n",
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" run_as_dummy=run_as_dummy,\n",
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" retry_pipeline_execution='unchecked',\n",
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" rerun_limit=Job.RerunLimit(times='5')\n",
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")\n",
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"\n",
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"# Define folders\n",
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"folder1 = Folder('Folder_Sanity_1', controlm_server='workbench', job_list=[databricksLoad])\n",
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"folder2 = Folder('Folder_Sanity_2', controlm_server='workbench', job_list=[sagemaker_job])\n",
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"\n",
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"workflow.add(folder1)\n",
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"workflow.add(folder2)\n",
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"\n",
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"# Step 3: Deploy workflow\n",
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"workflow.build()\n",
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"workflow.deploy()\n",
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"\n",
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"# Step 4: Retrieve jobs after deployment\n",
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"workflow_actual = Workflow.get_jobs(env, server=\"workbench\", folder=\"Folder_Sanity_*\")\n",
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"\n",
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"print(workflow.dumps_json(indent=2))\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7c0baf86",
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"metadata": {},
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"source": [
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"## Arguments"
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]
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},
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{
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"cell_type": "markdown",
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"id": "feb81b89",
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"metadata": {},
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"source": [
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"- **environment: Environment** :\n",
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" The Control-M environment to connect to. Required. \n",
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" 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). \n",
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" You can create an Environment instance using one of the following static methods: \n",
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" - `create_workbench()` – for local development with Workbench. \n",
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" - `create_onprem(host, username, password)` – for on-premises Control-M using username/password authentication. \n",
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" - `create_saas(endpoint, api_key)` – for Helix Control-M (SaaS) using an API key. \n",
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" The environment determines how to authenticate and which API variant to use, depending on whether the backend is Control-M or Helix Control-M.\n",
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"\n",
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"\n",
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"- **server: str** :\n",
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"The exact Control-M/Server name to query. Required. \n",
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"No wildcards allowed.\n",
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"\n",
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"- **folder: str** :\n",
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"The folder name or pattern to fetch. Required. \n",
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"Supports wildcards (e.g., \"MyFolder_*\"). Filters jobs by folder name.\n",
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"\n",
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"- **job: str** (Not supported yet) \n",
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" Currently not supported. The API ignores this parameter if provided."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "59899dfc",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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