Application Workflow Jobs

The following topics describe job types for application workflow platforms and services:

• Job:Airflow

• Job:Apache NiFi

• Job:AWS Step Functions

• Job:AWS:StepFunction

• Job:Azure Logic Apps

• Job:Azure:LogicApps

• Job:GCP Workflows

Job:Airflow

Airflow enables you to monitor and manage DAG workflows in Control-M. You can monitor DAG executions in the Airflow tab in the Monitoring domain. You can also view the specific details of each task, open the DAG in the Airflow web server user interface, and view XCom variables from the Airflow tab.

To deploy and run this job, ensure that you have done the following:

• Installed Airflow, as described in Control-M for Airflow Installation.

• Created a connection profile, as described in ConnectionProfile:AWS.

The following example shows how to define an Airflow job:

"AirflowJob": 
{
   "Type": "Job:Airflow",
   "Host": "AgentHost",
   "ConnectionProfile": "AIRFLOW_CONNECTION_PROFILE",
   "DagId": "example_bash_operator",
   "ConfigurationJson": "\{\"key1\":1, \"key2\":2, \"key3\":\"value3\"\}",
   "OutputDetails": "FAILED_TASKS"
}

The following table describes the Airflow job parameters.

Job:Apache NiFi

Apache NiFi is an open-source tool that automates data flow across systems in real time.

To deploy and run an Apache NiFi job, ensure that you have done the following:

To deploy and run an Apache NiFi job, ensure that you have installed the AWS Step Functions plug-in with the provision image command or the provision agent::update command.

The following example shows how to run an Apache NiFi job:

"Apache NiFi_Job_1": 
{
   "Type": "Job:Apache NiFi",
   "ConnectionProfile": "NFI",
   "Processor Group ID": "3b315648-c11b-1ff4-c672-770c0ba49da3",
   "Processor ID": "3b316c50-c11b-1ff4-99f2-690aa6f35952v",
   "Action": "Run Processor",
   "Disconnected Node Ack": "unchecked",
   "Status Polling Frequency":"5",
   "Failure Tolerance":"0"
}

The following table describes the Apache NiFi job parameters.

Job:AWS Step Functions

AWS Step Functions enables you to create visual workflows that can integrate other AWS services.

To deploy and run an AWS Step Functions job, ensure that you have installed the AWS Step Functions plug-in with the provision image command or the provision agent::update command.

The following example shows how to define an AWS Step Functions job:

"AWS Step Functions_Job_2":
{
   "Type": "Job:AWS Step Functions",
   "ConnectionProfile": "STEPFUNCTIONSCCP",
   "Execution Name": "Step Functions Exec",
   "State Machine ARN": "arn:aws:states:us-east-1:155535555553:stateMachine:MyStateMachine",
   "Parameters": "{\\\"parameter1\\\":\\\"value1\\\"}",
   "Show Execution Logs": "checked",
   "Status Polling Frequency":"10",
   "Failure Tolerance":"2"
}

The following table describes the AWS Step Functions job parameters.

Job:AWS:StepFunction

AWS Step Function enables you to create visual workflows that can integrate other AWS services.

To deploy and run this type of AWS job, ensure that you have done the following:

• Installed the Application Pack, which includes the Control-M for AWS plug-in.

• Created a connection profile, as described in ConnectionProfile:AWS.

The following example shows how to define an AWS Step Function job:

"AwsStepFunctionJob": 
{
   "Type": "Job:AWS:StepFunction",
   "ConnectionProfile": "AWS_CONNECTION",
   "StateMachine": "StateMachine1",
   "ExecutionName": "Execution1",
   "Input": ""{\"myVar\" :\"value1\" \\n\"myOtherVar\" : \"value2\"}" ",
   "AppendLog": true
}

The following table describes the AWS Step Function job parameters.

Job:Azure Logic Apps

Azure Logic Apps enables you to design and automate cloud-based workflows and integrations.

To deploy and run an Azure Logic Apps job, ensure that you have installed the Azure Logic Apps plug-in with the provision image command or the provision agent::update command.

The following example shows how to define an Azure Logic Apps job:

"Azure Logic Apps Job": 
{
   "Type": "Job:Azure Logic Apps",
   "ConnectionProfile": "AZURE_LOGIC_APPS",
   "Workflow": "tb-logic",
   "Parameters": "{\"bodyinfo\":\"hello from CM\",\"param2\":\"value2\"}",
   "Get Logs": "unchecked",
   "Status Polling Frequency": "20",
   "Failure Tolerance": "2"
}

The following table describes the Azure Logic Apps job parameters.

Job:Azure:LogicApps

Azure Logic Apps enables you to design and automate cloud-based workflows and integrations.

To deploy and run this type of Azure job, ensure that you have done the following:

• Installed the Application Pack, which includes the Control-M for Azure plug-in.

• Created a connection profile, as described in ConnectionProfile:Azure.

The following example shows how to define a Azure Logic Apps job:

"AzureLogicAppJob": 
{
   "Type": "Job:Azure:LogicApps",
   "ConnectionProfile": "AZURE_CONNECTION",
   "LogicAppName": "MyLogicApp",
   "RequestBody": "{\\n  \"name\": \"BMC\"\\n}",
   "AppendLog": false
}

The following table describes the Azure Logic Apps job parameters.

To deploy and run a GCP Composer job, ensure that you have installed the GCP Composer plug-in with the provision image command or the provision agent::update command.

Job:GCP Workflows

GCP Workflows enables you to design and automate cloud-based workflows and integrations.

To deploy and run a GCP Workflows job, ensure that you have installed the GCP Workflows plug-in with the provision image command or the provision agent::update command.

The following example shows how to define a GCP Workflows job:

"GCP Workflows_Job": 
{
   "Type": "Job:GCP Workflows",
   "ConnectionProfile": "GCPWF",
   "Project ID": "12345id",
   "Location": "us-central1",
   "Workflow Name": "workflow-1",
   "Parameters JSON Input": "argument" : {\"var1\":\"value1\",\"var2\":\"value2\"},
   "ExecutionLabel" : "{"labelName":"name"}",
   "Show Workflow Results": "checked",    
   "Status Polling Frequency": "20",
   "Failure Tolerance": "3"
}

The following table describes the GCP Workflows job parameters.

"argument" : {\"var1\":\"value1\",\"var2\":\"value2\"}