Release/2.5.0

docs/_sources/index.rst.txt

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

docs/_sources/notebooks/get_jobs.ipynb.txt

@@ -0,0 +1,189 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "060c8aab",
+   "metadata": {},
+   "source": [
+    "# Get Jobs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06976a85",
+   "metadata": {},
+   "source": [
+    "[AutomationAPI Documentation](https://documents.bmc.com/supportu/API/Monthly/en-US/Documentation/API_Services_DeployService.htm#deploy3)\n",
+    "\n",
+    "\n",
+    "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",
+    "\n",
+    "This guide demonstrates how to use get_jobs() to fetch jobs from the Control-M server."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "93e45a7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"Folder_Sanity_1\": {\n",
+      "    \"Type\": \"Folder\",\n",
+      "    \"ControlmServer\": \"workbench\",\n",
+      "    \"LoadForecasts\": {\n",
+      "      \"Type\": \"Job:Databricks\",\n",
+      "      \"RunAs\": \"workbench\",\n",
+      "      \"RunAsDummy\": true,\n",
+      "      \"ConnectionProfile\": \"DATABRICKS\",\n",
+      "      \"Databricks Job ID\": \"991955986358417\",\n",
+      "      \"Parameters\": \"\\\"params\\\":{}\",\n",
+      "      \"Idempotency Token\": \"tokeni_%%ORDERID\"\n",
+      "    },\n",
+      "    \"RunAs\": \"workbench\"\n",
+      "  },\n",
+      "  \"Folder_Sanity_2\": {\n",
+      "    \"Type\": \"Folder\",\n",
+      "    \"ControlmServer\": \"workbench\",\n",
+      "    \"InventoryForecastModel\": {\n",
+      "      \"Type\": \"Job:AWS SageMaker\",\n",
+      "      \"RerunLimit\": {\n",
+      "        \"Times\": \"5\"\n",
+      "      },\n",
+      "      \"RunAs\": \"workbench\",\n",
+      "      \"RunAsDummy\": true,\n",
+      "      \"ConnectionProfile\": \"SAGEMAKER\",\n",
+      "      \"Pipeline Name\": \"InferencePipeline\",\n",
+      "      \"Idempotency Token\": \"Token_ControlM_for_SageMaker%%ORDERID\",\n",
+      "      \"Add Parameters\": \"checked\",\n",
+      "      \"Parameters\": \"{\\\"Name\\\":\\\"input_file\\\", \\\"Value\\\": \\\"file\\\"}\",\n",
+      "      \"Retry Pipeline Execution\": \"unchecked\"\n",
+      "    },\n",
+      "    \"RunAs\": \"workbench\"\n",
+      "  }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from ctm_python_client.core.workflow import Workflow, WorkflowDefaults\n",
+    "from ctm_python_client.core.comm import Environment\n",
+    "from aapi import *\n",
+    "\n",
+    "# Step 1: Define the environment (Control-M Workbench in this case)\n",
+    "env = Environment.create_workbench('workbench')\n",
+    "\n",
+    "# Step 2: Define your workflow with jobs and folders\n",
+    "workflow = Workflow(env, WorkflowDefaults(run_as='workbench'))\n",
+    "workflow.clear_all()\n",
+    "run_as_dummy = True\n",
+    "\n",
+    "# Define jobs\n",
+    "databricksLoad = JobDatabricks(\n",
+    "    'LoadForecasts',\n",
+    "    connection_profile='DATABRICKS',\n",
+    "    databricks_job_id='991955986358417',\n",
+    "    parameters='\"params\":{}',\n",
+    "    idempotency_token='tokeni_%%ORDERID',\n",
+    "    run_as_dummy=run_as_dummy\n",
+    ")\n",
+    "\n",
+    "sagemaker_job = JobAwsSageMaker(\n",
+    "    'InventoryForecastModel',\n",
+    "    connection_profile='SAGEMAKER',\n",
+    "    pipeline_name='InferencePipeline',\n",
+    "    idempotency_token='Token_ControlM_for_SageMaker%%ORDERID',\n",
+    "    add_parameters='checked',\n",
+    "    parameters='{\"Name\":\"input_file\", \"Value\": \"file\"}',\n",
+    "    run_as_dummy=run_as_dummy,\n",
+    "    retry_pipeline_execution='unchecked',\n",
+    "    rerun_limit=Job.RerunLimit(times='5')\n",
+    ")\n",
+    "\n",
+    "# Define folders\n",
+    "folder1 = Folder('Folder_Sanity_1', controlm_server='workbench', job_list=[databricksLoad])\n",
+    "folder2 = Folder('Folder_Sanity_2', controlm_server='workbench', job_list=[sagemaker_job])\n",
+    "\n",
+    "workflow.add(folder1)\n",
+    "workflow.add(folder2)\n",
+    "\n",
+    "# Step 3: Deploy workflow\n",
+    "workflow.build()\n",
+    "workflow.deploy()\n",
+    "\n",
+    "# Step 4: Retrieve jobs after deployment\n",
+    "workflow_actual = Workflow.get_jobs(env, server=\"workbench\", folder=\"Folder_Sanity_*\")\n",
+    "\n",
+    "print(workflow.dumps_json(indent=2))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c0baf86",
+   "metadata": {},
+   "source": [
+    "## Arguments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "feb81b89",
+   "metadata": {},
+   "source": [
+    "- **environment: Environment** :\n",
+    "  The Control-M environment to connect to. Required.  \n",
+    "  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",
+    "  You can create an Environment instance using one of the following static methods:  \n",
+    "  - `create_workbench()` – for local development with Workbench.  \n",
+    "  - `create_onprem(host, username, password)` – for on-premises Control-M using username/password authentication.  \n",
+    "  - `create_saas(endpoint, api_key)` – for Helix Control-M (SaaS) using an API key.  \n",
+    "  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",
+    "\n",
+    "\n",
+    "- **server: str** :\n",
+    "The exact Control-M/Server name to query. Required.  \n",
+    "No wildcards allowed.\n",
+    "\n",
+    "- **folder: str** :\n",
+    "The folder name or pattern to fetch. Required.  \n",
+    "Supports wildcards (e.g., \"MyFolder_*\"). Filters jobs by folder name.\n",
+    "\n",
+    "- **job: str** (Not supported yet)  \n",
+    "  Currently not supported. The API ignores this parameter if provided."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59899dfc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}