{
 "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."
   ]
  }
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