"Managed Service for Apache Spark" is the new name for the product formerly known as "Dataproc on Compute Engine" (cluster deployment) and "Google Cloud Serverless for Apache Spark" (serverless deployment).

Use inline workflows

Unlike standard workflows that instantiate a previously created workflow template resource, inline workflows use a YAML file or an embedded WorkflowTemplate definition to run a workflow.

Create and run an inline workflow

gcloud

See Instantiate a workflow using a YAML file .

REST

Before using any of the request data, make the following replacements:

project-id : Google Cloud project ID
region : cluster region , such as "us-central1"
zoneUri : Specify a zone within the cluster's region , such as "us-central1-b", or leave empty ("") to use Managed Service for Apache Spark Auto Zone placement
clusterName : cluster name

HTTP method and URL:

POST https://dataproc.googleapis.com/v1/projects/ project-id 
/regions/ region 
/workflowTemplates:instantiateInline

Request JSON body:

{
  "jobs": [
    {
      "hadoopJob": {
        "mainJarFileUri": "file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar",
        "args": [
          "teragen",
          "1000",
          "hdfs:///gen/"
        ]
      },
      "stepId": "teragen"
    },
    {
      "hadoopJob": {
        "mainJarFileUri": "file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar",
        "args": [
          "terasort",
          "hdfs:///gen/",
          "hdfs:///sort/"
        ]
      },
      "stepId": "terasort",
      "prerequisiteStepIds": [
        "teragen"
      ]
    }
  ],
  "placement": {
    "managedCluster": {
      "clusterName": " cluster-name 
",
      "config": {
        "gceClusterConfig": {
          "zoneUri": " zone 
"
        }
      }
    }
  }
}

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell , which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json , and execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://dataproc.googleapis.com/v1/projects/ project-id 
/regions/ region 
/workflowTemplates:instantiateInline"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list .

Save the request body in a file named request.json , and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://dataproc.googleapis.com/v1/projects/ project-id 
/regions/ region 
/workflowTemplates:instantiateInline" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/project-id/regions/region/operations/2fbd0dad-...",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.dataproc.v1.WorkflowMetadata",
    "graph": {
      "nodes": [
        {
          "stepId": "teragen",
          "state": "RUNNABLE"
        },
        {
          "stepId": "terasort",
          "prerequisiteStepIds": [
            "teragen"
          ],
          "state": "BLOCKED"
        }
      ]
    },
    "state": "PENDING",
    "startTime": "2020-04-02T22:50:44.826Z"
  }
}

Console

Currently, the creation of inline workflows is not supported in the Google Cloud console. Workflow templates and instantiated workflows can be viewed from Managed Service for Apache Spark Workflows page.

Go

Install the client library
Set up application default credentials

Run the code

  import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "io" 
  
 dataproc 
  
 "cloud.google.com/go/dataproc/apiv1" 
  
 "cloud.google.com/go/dataproc/apiv1/dataprocpb" 
  
 "google.golang.org/api/option" 
 ) 
 func 
  
 instantiateInlineWorkflowTemplate 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 projectID 
 , 
  
 region 
  
 string 
 ) 
  
 error 
  
 { 
  
 // projectID := "your-project-id" 
  
 // region := "us-central1" 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 // Create the cluster client. 
  
 endpoint 
  
 := 
  
 region 
  
 + 
  
 "-dataproc.googleapis.com:443" 
  
 workflowTemplateClient 
 , 
  
 err 
  
 := 
  
 dataproc 
 . 
  NewWorkflowTemplateClient 
 
 ( 
 ctx 
 , 
  
 option 
 . 
 WithEndpoint 
 ( 
 endpoint 
 )) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "dataproc.NewWorkflowTemplateClient: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 defer 
  
 workflowTemplateClient 
 . 
 Close 
 () 
  
 // Create jobs for the workflow. 
  
 teragenJob 
  
 := 
  
& dataprocpb 
 . 
 OrderedJob 
 { 
  
 JobType 
 : 
  
& dataprocpb 
 . 
 OrderedJob_HadoopJob 
 { 
  
 HadoopJob 
 : 
  
& dataprocpb 
 . 
 HadoopJob 
 { 
  
 Driver 
 : 
  
& dataprocpb 
 . 
 HadoopJob_MainJarFileUri 
 { 
  
 MainJarFileUri 
 : 
  
 "file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar" 
 , 
  
 }, 
  
 Args 
 : 
  
 [] 
 string 
 { 
  
 "teragen" 
 , 
  
 "1000" 
 , 
  
 "hdfs:///gen/" 
 , 
  
 }, 
  
 }, 
  
 }, 
  
 StepId 
 : 
  
 "teragen" 
 , 
  
 } 
  
 terasortJob 
  
 := 
  
& dataprocpb 
 . 
 OrderedJob 
 { 
  
 JobType 
 : 
  
& dataprocpb 
 . 
 OrderedJob_HadoopJob 
 { 
  
 HadoopJob 
 : 
  
& dataprocpb 
 . 
 HadoopJob 
 { 
  
 Driver 
 : 
  
& dataprocpb 
 . 
 HadoopJob_MainJarFileUri 
 { 
  
 MainJarFileUri 
 : 
  
 "file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar" 
 , 
  
 }, 
  
 Args 
 : 
  
 [] 
 string 
 { 
  
 "terasort" 
 , 
  
 "hdfs:///gen/" 
 , 
  
 "hdfs:///sort/" 
 , 
  
 }, 
  
 }, 
  
 }, 
  
 StepId 
 : 
  
 "terasort" 
 , 
  
 PrerequisiteStepIds 
 : 
  
 [] 
 string 
 { 
  
 "teragen" 
 , 
  
 }, 
  
 } 
  
 // Create the cluster placement. 
  
 clusterPlacement 
  
 := 
  
& dataprocpb 
 . 
 WorkflowTemplatePlacement 
 { 
  
 Placement 
 : 
  
& dataprocpb 
 . 
 WorkflowTemplatePlacement_ManagedCluster 
 { 
  
 ManagedCluster 
 : 
  
& dataprocpb 
 . 
 ManagedCluster 
 { 
  
 ClusterName 
 : 
  
 "my-managed-cluster" 
 , 
  
 Config 
 : 
  
& dataprocpb 
 . 
 ClusterConfig 
 { 
  
 GceClusterConfig 
 : 
  
& dataprocpb 
 . 
 GceClusterConfig 
 { 
  
 // Leave "ZoneUri" empty for "Auto Zone Placement" 
  
 // ZoneUri: "" 
  
 ZoneUri 
 : 
  
 "us-central1-a" 
 , 
  
 }, 
  
 }, 
  
 }, 
  
 }, 
  
 } 
  
 // Create the Instantiate Inline Workflow Template Request. 
  
 req 
  
 := 
  
& dataprocpb 
 . 
 InstantiateInlineWorkflowTemplateRequest 
 { 
  
 Parent 
 : 
  
 fmt 
 . 
 Sprintf 
 ( 
 "projects/%s/regions/%s" 
 , 
  
 projectID 
 , 
  
 region 
 ), 
  
 Template 
 : 
  
& dataprocpb 
 . 
 WorkflowTemplate 
 { 
  
 Jobs 
 : 
  
 [] 
 * 
 dataprocpb 
 . 
 OrderedJob 
 { 
  
 teragenJob 
 , 
  
 terasortJob 
 , 
  
 }, 
  
 Placement 
 : 
  
 clusterPlacement 
 , 
  
 }, 
  
 } 
  
 // Create the cluster. 
  
 op 
 , 
  
 err 
  
 := 
  
 workflowTemplateClient 
 . 
 InstantiateInlineWorkflowTemplate 
 ( 
 ctx 
 , 
  
 req 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "InstantiateInlineWorkflowTemplate: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 if 
  
 err 
  
 := 
  
 op 
 . 
 Wait 
 ( 
 ctx 
 ); 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "InstantiateInlineWorkflowTemplate.Wait: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 // Output a success message. 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "Workflow created successfully." 
 ) 
  
 return 
  
 nil 
 }

Java

Install the client library
Set up application default credentials

Run the code

  import 
  
 com.google.api.gax.longrunning. OperationFuture 
 
 ; 
 import 
  
 com.google.cloud.dataproc.v1. ClusterConfig 
 
 ; 
 import 
  
 com.google.cloud.dataproc.v1. GceClusterConfig 
 
 ; 
 import 
  
 com.google.cloud.dataproc.v1. HadoopJob 
 
 ; 
 import 
  
 com.google.cloud.dataproc.v1. ManagedCluster 
 
 ; 
 import 
  
 com.google.cloud.dataproc.v1. OrderedJob 
 
 ; 
 import 
  
 com.google.cloud.dataproc.v1. RegionName 
 
 ; 
 import 
  
 com.google.cloud.dataproc.v1. WorkflowMetadata 
 
 ; 
 import 
  
 com.google.cloud.dataproc.v1. WorkflowTemplate 
 
 ; 
 import 
  
 com.google.cloud.dataproc.v1. WorkflowTemplatePlacement 
 
 ; 
 import 
  
 com.google.cloud.dataproc.v1. WorkflowTemplateServiceClient 
 
 ; 
 import 
  
 com.google.cloud.dataproc.v1. WorkflowTemplateServiceSettings 
 
 ; 
 import 
  
 com.google.protobuf. Empty 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.concurrent.ExecutionException 
 ; 
 public 
  
 class 
 InstantiateInlineWorkflowTemplate 
  
 { 
  
 public 
  
 static 
  
 void 
  
 instantiateInlineWorkflowTemplate 
 () 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 projectId 
  
 = 
  
 "your-project-id" 
 ; 
  
 String 
  
 region 
  
 = 
  
 "your-project-region" 
 ; 
  
 instantiateInlineWorkflowTemplate 
 ( 
 projectId 
 , 
  
 region 
 ); 
  
 } 
  
 public 
  
 static 
  
 void 
  
 instantiateInlineWorkflowTemplate 
 ( 
 String 
  
 projectId 
 , 
  
 String 
  
 region 
 ) 
  
 throws 
  
 IOException 
 , 
  
 InterruptedException 
  
 { 
  
 String 
  
 myEndpoint 
  
 = 
  
 String 
 . 
 format 
 ( 
 "%s-dataproc.googleapis.com:443" 
 , 
  
 region 
 ); 
  
 // Configure the settings for the workflow template service client. 
  
  WorkflowTemplateServiceSettings 
 
  
 workflowTemplateServiceSettings 
  
 = 
  
  WorkflowTemplateServiceSettings 
 
 . 
 newBuilder 
 (). 
 setEndpoint 
 ( 
 myEndpoint 
 ). 
 build 
 (); 
  
 // Create a workflow template service client with the configured settings. The client only 
  
 // needs to be created once and can be reused for multiple requests. Using a try-with-resources 
  
 // closes the client, but this can also be done manually with the .close() method. 
  
 try 
  
 ( 
  WorkflowTemplateServiceClient 
 
  
 workflowTemplateServiceClient 
  
 = 
  
  WorkflowTemplateServiceClient 
 
 . 
 create 
 ( 
 workflowTemplateServiceSettings 
 )) 
  
 { 
  
 // Configure the jobs within the workflow. 
  
  HadoopJob 
 
  
 teragenHadoopJob 
  
 = 
  
  HadoopJob 
 
 . 
 newBuilder 
 () 
  
 . 
 setMainJarFileUri 
 ( 
 "file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar" 
 ) 
  
 . 
 addArgs 
 ( 
 "teragen" 
 ) 
  
 . 
 addArgs 
 ( 
 "1000" 
 ) 
  
 . 
 addArgs 
 ( 
 "hdfs:///gen/" 
 ) 
  
 . 
 build 
 (); 
  
  OrderedJob 
 
  
 teragen 
  
 = 
  
  OrderedJob 
 
 . 
 newBuilder 
 (). 
 setHadoopJob 
 ( 
 teragenHadoopJob 
 ). 
 setStepId 
 ( 
 "teragen" 
 ). 
 build 
 (); 
  
  HadoopJob 
 
  
 terasortHadoopJob 
  
 = 
  
  HadoopJob 
 
 . 
 newBuilder 
 () 
  
 . 
 setMainJarFileUri 
 ( 
 "file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar" 
 ) 
  
 . 
 addArgs 
 ( 
 "terasort" 
 ) 
  
 . 
 addArgs 
 ( 
 "hdfs:///gen/" 
 ) 
  
 . 
 addArgs 
 ( 
 "hdfs:///sort/" 
 ) 
  
 . 
 build 
 (); 
  
  OrderedJob 
 
  
 terasort 
  
 = 
  
  OrderedJob 
 
 . 
 newBuilder 
 () 
  
 . 
 setHadoopJob 
 ( 
 terasortHadoopJob 
 ) 
  
 . 
 addPrerequisiteStepIds 
 ( 
 "teragen" 
 ) 
  
 . 
 setStepId 
 ( 
 "terasort" 
 ) 
  
 . 
 build 
 (); 
  
 // Configure the cluster placement for the workflow. 
  
 // Leave "ZoneUri" empty for "Auto Zone Placement". 
  
 // GceClusterConfig gceClusterConfig = 
  
 //     GceClusterConfig.newBuilder().setZoneUri("").build(); 
  
  GceClusterConfig 
 
  
 gceClusterConfig 
  
 = 
  
  GceClusterConfig 
 
 . 
 newBuilder 
 (). 
  setZoneUri 
 
 ( 
 "us-central1-a" 
 ). 
 build 
 (); 
  
  ClusterConfig 
 
  
 clusterConfig 
  
 = 
  
  ClusterConfig 
 
 . 
 newBuilder 
 (). 
  setGceClusterConfig 
 
 ( 
 gceClusterConfig 
 ). 
 build 
 (); 
  
  ManagedCluster 
 
  
 managedCluster 
  
 = 
  
  ManagedCluster 
 
 . 
 newBuilder 
 () 
  
 . 
 setClusterName 
 ( 
 "my-managed-cluster" 
 ) 
  
 . 
 setConfig 
 ( 
 clusterConfig 
 ) 
  
 . 
 build 
 (); 
  
  WorkflowTemplatePlacement 
 
  
 workflowTemplatePlacement 
  
 = 
  
  WorkflowTemplatePlacement 
 
 . 
 newBuilder 
 (). 
  setManagedCluster 
 
 ( 
 managedCluster 
 ). 
 build 
 (); 
  
 // Create the inline workflow template. 
  
  WorkflowTemplate 
 
  
 workflowTemplate 
  
 = 
  
  WorkflowTemplate 
 
 . 
 newBuilder 
 () 
  
 . 
 addJobs 
 ( 
 teragen 
 ) 
  
 . 
 addJobs 
 ( 
 terasort 
 ) 
  
 . 
 setPlacement 
 ( 
 workflowTemplatePlacement 
 ) 
  
 . 
 build 
 (); 
  
 // Submit the instantiated inline workflow template request. 
  
 String 
  
 parent 
  
 = 
  
  RegionName 
 
 . 
 format 
 ( 
 projectId 
 , 
  
 region 
 ); 
  
 OperationFuture<Empty 
 , 
  
 WorkflowMetadata 
>  
 instantiateInlineWorkflowTemplateAsync 
  
 = 
  
 workflowTemplateServiceClient 
 . 
  instantiateInlineWorkflowTemplateAsync 
 
 ( 
  
 parent 
 , 
  
 workflowTemplate 
 ); 
  
  instantiateInlineWorkflowTemplateAsync 
 
 . 
 get 
 (); 
  
 // Print out a success message. 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
 "Workflow ran successfully." 
 ); 
  
 } 
  
 catch 
  
 ( 
 ExecutionException 
  
 e 
 ) 
  
 { 
  
 System 
 . 
 err 
 . 
 println 
 ( 
 String 
 . 
 format 
 ( 
 "Error running workflow: %s " 
 , 
  
 e 
 . 
 getMessage 
 ())); 
  
 } 
  
 } 
 }

Node.js

  const 
  
 dataproc 
  
 = 
  
 require 
 ( 
 ' @google-cloud/dataproc 
' 
 ); 
 // TODO(developer): Uncomment and set the following variables 
 // projectId = 'YOUR_PROJECT_ID' 
 // region = 'YOUR_REGION' 
 // Create a client with the endpoint set to the desired region 
 const 
  
 client 
  
 = 
  
 new 
  
 dataproc 
 . 
 v1 
 . 
  WorkflowTemplateServiceClient 
 
 ({ 
  
 apiEndpoint 
 : 
  
 ` 
 ${ 
 region 
 } 
 -dataproc.googleapis.com` 
 , 
  
 projectId 
 : 
  
 projectId 
 , 
 }); 
 async 
  
 function 
  
 instantiateInlineWorkflowTemplate 
 () 
  
 { 
  
 // Create the formatted parent. 
  
 const 
  
 parent 
  
 = 
  
 client 
 . 
 regionPath 
 ( 
 projectId 
 , 
  
 region 
 ); 
  
 // Create the template 
  
 const 
  
 template 
  
 = 
  
 { 
  
 jobs 
 : 
  
 [ 
  
 { 
  
 hadoopJob 
 : 
  
 { 
  
 mainJarFileUri 
 : 
  
 'file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar' 
 , 
  
 args 
 : 
  
 [ 
 'teragen' 
 , 
  
 '1000' 
 , 
  
 'hdfs:///gen/' 
 ], 
  
 }, 
  
 stepId 
 : 
  
 'teragen' 
 , 
  
 }, 
  
 { 
  
 hadoopJob 
 : 
  
 { 
  
 mainJarFileUri 
 : 
  
 'file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar' 
 , 
  
 args 
 : 
  
 [ 
 'terasort' 
 , 
  
 'hdfs:///gen/' 
 , 
  
 'hdfs:///sort/' 
 ], 
  
 }, 
  
 stepId 
 : 
  
 'terasort' 
 , 
  
 prerequisiteStepIds 
 : 
  
 [ 
 'teragen' 
 ], 
  
 }, 
  
 ], 
  
 placement 
 : 
  
 { 
  
 managedCluster 
 : 
  
 { 
  
 clusterName 
 : 
  
 'my-managed-cluster' 
 , 
  
 config 
 : 
  
 { 
  
 gceClusterConfig 
 : 
  
 { 
  
 // Leave 'zoneUri' empty for 'Auto Zone Placement' 
  
 // zoneUri: '' 
  
 zoneUri 
 : 
  
 'us-central1-a' 
 , 
  
 }, 
  
 }, 
  
 }, 
  
 }, 
  
 }; 
  
 const 
  
 request 
  
 = 
  
 { 
  
 parent 
 : 
  
 parent 
 , 
  
 template 
 : 
  
 template 
 , 
  
 }; 
  
 // Submit the request to instantiate the workflow from an inline template. 
  
 const 
  
 [ 
 operation 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 instantiateInlineWorkflowTemplate 
 ( 
 request 
 ); 
  
 await 
  
 operation 
 . 
 promise 
 (); 
  
 // Output a success message 
  
 console 
 . 
 log 
 ( 
 'Workflow ran successfully.' 
 );

Python

Install the client library
Set up application default credentials

Run the code

  from 
  
 google.cloud 
  
 import 
 dataproc_v1 
 as 
 dataproc 
 def 
  
 instantiate_inline_workflow_template 
 ( 
 project_id 
 , 
 region 
 ): 
  
 """This sample walks a user through submitting a workflow 
 for a Cloud Dataproc using the Python client library. 
 Args: 
 project_id (string): Project to use for running the workflow. 
 region (string): Region where the workflow resources should live. 
 """ 
 # Create a client with the endpoint set to the desired region. 
 workflow_template_client 
 = 
 dataproc 
 . 
 WorkflowTemplateServiceClient 
 ( 
 client_options 
 = 
 { 
 "api_endpoint" 
 : 
 f 
 " 
 { 
 region 
 } 
 -dataproc.googleapis.com:443" 
 } 
 ) 
 parent 
 = 
 f 
 "projects/ 
 { 
 project_id 
 } 
 /regions/ 
 { 
 region 
 } 
 " 
 template 
 = 
 { 
 "jobs" 
 : 
 [ 
 { 
 "hadoop_job" 
 : 
 { 
 "main_jar_file_uri" 
 : 
 "file:///usr/lib/hadoop-mapreduce/" 
 "hadoop-mapreduce-examples.jar" 
 , 
 "args" 
 : 
 [ 
 "teragen" 
 , 
 "1000" 
 , 
 "hdfs:///gen/" 
 ], 
 }, 
 "step_id" 
 : 
 "teragen" 
 , 
 }, 
 { 
 "hadoop_job" 
 : 
 { 
 "main_jar_file_uri" 
 : 
 "file:///usr/lib/hadoop-mapreduce/" 
 "hadoop-mapreduce-examples.jar" 
 , 
 "args" 
 : 
 [ 
 "terasort" 
 , 
 "hdfs:///gen/" 
 , 
 "hdfs:///sort/" 
 ], 
 }, 
 "step_id" 
 : 
 "terasort" 
 , 
 "prerequisite_step_ids" 
 : 
 [ 
 "teragen" 
 ], 
 }, 
 ], 
 "placement" 
 : 
 { 
 "managed_cluster" 
 : 
 { 
 "cluster_name" 
 : 
 "my-managed-cluster" 
 , 
 "config" 
 : 
 { 
 "gce_cluster_config" 
 : 
 { 
 # Leave 'zone_uri' empty for 'Auto Zone Placement' 
 # 'zone_uri': '' 
 "zone_uri" 
 : 
 "us-central1-a" 
 } 
 }, 
 } 
 }, 
 } 
 # Submit the request to instantiate the workflow from an inline template. 
 operation 
 = 
 workflow_template_client 
 . 
 instantiate_inline_workflow_template 
 ( 
 request 
 = 
 { 
 "parent" 
 : 
 parent 
 , 
 "template" 
 : 
 template 
 } 
 ) 
 operation 
 . 
 result 
 () 
 # Output a success message. 
 print 
 ( 
 "Workflow ran successfully." 
 )

Use inline workflows Stay organized with collections Save and categorize content based on your preferences.

Create and run an inline workflow

gcloud

REST

curl (Linux, macOS, or Cloud Shell)

PowerShell (Windows)

Console

Go

Java

Node.js

Python

Use inline workflows