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Running First Worker

In this article we will explore how you can get your first worker task running.

We are hosting the code used in this article in the following location. You can clone and use it as a reference locally.

In the first codelab Running your First Workflow, we created a simple workflow that used a HTTP System Task to run our workflow. System tasks run on the Conductor server - making commonly used functions easy to deploy in your workflow. Now it's time to explore how to run a custom worker that you will implement yourself.

After completing the steps in this article, you will:

  1. Learn about a SIMPLE worker type which runs your custom code.
  2. Learn about how a custom worker task runs from your environment and connects to Conductor.

Worker tasks are implemented by your application(s) and run in a separate environment from Conductor. The worker tasks can be implemented in any language. These tasks talk to Conductor server via REST/gRPC to poll for tasks and update its status after execution. In our example we will be implementing a Java based worker by leveraging the official Java Client SDK. (A full list of SDKs)

Worker tasks are identified by task type SIMPLE in the workflow JSON definition.

Step 1 - Register the Worker Task

First let's create task definition for "simple_worker". Send a POST request to /metadata/taskdefs API endpoint on your conductor server to register these tasks.

"name": "simple_worker",
"retryCount": 3,
"retryLogic": "FIXED",
"retryDelaySeconds": 10,
"timeoutSeconds": 300,
"timeoutPolicy": "TIME_OUT_WF",
"responseTimeoutSeconds": 180,
"ownerEmail": ""

In the Running your First Workflow example, we used the Swagger API UI to make our API calls:

NOTE: With the Playground, you will need an authentication token. In the dashboard, click your user icon in the top right, and choose the copy token option. Back on the Swagger page, click the lock to the right of the endpoint, and past the token here.

You can also use curl to add a task:

curl 'http://localhost:8080/api/metadata/taskdefs' \
-H 'accept: */*' \
-H 'Referer: ' \
-H 'Content-Type: application/json' \
--data-raw '[{"name":"simple_worker", "retryCount":3,"retryLogic":"FIXED","retryDelaySeconds":10,"timeoutSeconds":300,"timeoutPolicy":"TIME_OUT_WF","responseTimeoutSeconds":180,"ownerEmail":""}]'

For Orkes Playground add an additional header line to your curl command: -H 'X-Authorization: {YourToken}' \ (and replace http://localhost:8080 with Alternatively, the playground also offers task creation via the dashboard.

Here is an overview of the task fields that we just created

  1. "name" : Name of your worker. This should be unique.
  2. "retryCount" : The number of times Conductor should retry your worker task in the event of an unexpected failure.
  3. "retryLogic" : FIXED - The retry logic - options are FIXED and EXPONENTIAL_BACKOFF
  4. "retryDelaySeconds" : Time to wait before retry (or time in exponential back off calculation).
  5. "timeoutSeconds" : Time in seconds, after which the task is marked as TIMED_OUT. (This is optional, and some long running tasks do not need this value.)
  6. "timeoutPolicy" : TIME_OUT_WF - Task's timeout policy. Options can be found here.
  7. "responseTimeoutSeconds" : Must be greater than 0 and less than timeoutSeconds. The task is rescheduled if not updated with a status after this time (heartbeat mechanism). Useful when the worker polls for the task but fails to complete due to errors/network failure. Defaults to 3600.
  8. "ownerEmail" : Mandatory metadata to manage who created or owns this worker definition in a shared conductor environment.

More details on the fields used and the remaining fields in the task definition can be found here.

Step 2 - Create a Workflow definition

Creating Workflow definition is similar to creating the task definition. In our workflow, we will use the task we defined earlier. Note that same Task definitions can be used in multiple workflows, or for multiple times in same Workflow. Conductor references the task via the taskReferenceName, so each invocation of the task in the same workflow a must have a unique value in this parameter.

"createTime": 1634021619147,
"updateTime": 1630694890267,
"name": "first_sample_workflow_with_worker",
"description": "First Sample Workflow With Worker",
"version": 1,
"tasks": [
"name": "simple_worker",
"taskReferenceName": "simple_worker_ref_1",
"inputParameters": {},
"type": "SIMPLE"
"inputParameters": [],
"outputParameters": {
"currentTimeOnServer": "${simple_worker_ref_1.output.currentTimeOnServer}",
"message": "${simple_worker_ref_1.output.message}"
"schemaVersion": 2,
"restartable": true,
"ownerEmail": "",
"timeoutPolicy": "ALERT_ONLY",
"timeoutSeconds": 0

Notice that in the workflow definition, we are using a single worker task using the task worker definition we created earlier. The task is of type SIMPLE.

We can use Swagger to add the workflow:

Here is the curl command to add a workflow to a a local version of Conductor (for Orkes Playground, change the domain, and add the X-Authenication header as we did in step 1 when adding a task):

curl 'http://localhost:8080/api/metadata/workflow' \
-H 'accept: */*' \
-H 'Referer: ' \
-H 'Content-Type: application/json' \
--data-raw '{"createTime":1634021619147,"updateTime":1630694890267,"name":"first_sample_workflow_with_worker","description":"First Sample Workflow With Worker","version":1,"tasks":[{"name":"simple_worker","taskReferenceName":"simple_worker_ref_1","inputParameters":{},"type":"SIMPLE"}],"inputParameters":[],"outputParameters":{"currentTimeOnServer":"${simple_worker_ref_1.output.currentTimeOnServer}","message":"${simple_worker_ref_1.output.message}"},"schemaVersion":2,"restartable":true,"ownerEmail":"","timeoutPolicy":"ALERT_ONLY","timeoutSeconds":0}'

Step 3 - Starting the Workflow

We've created a task that polls an external workflow, and added the task to a workflow. When we run the workflow, Conductor adds a job to the task's queue. Let's do that now:

  • Playground: click run workflow select the workflow by name, and click run workflow
  • In Swagger, the endpoint to start a workflow is POST /api/workflow/{workflowName}.
  • Using curl (same modifications for playground as above):
curl 'http://localhost:8080/api/workflow/first_sample_workflow_with_worker' \
-H 'accept: text/plain' \
-H 'Referer: ' \
-H 'Content-Type: application/json' \
--data-raw '{}'

The API path contains the workflow name first_sample_workflow_with_worker and our workflow doesn't need any inputs, so the body of the call is an empty JSON string: {}.

A successful POST request will return a workflowId, a unique identifier for the invocation of the workflow. Now, we can visualize the execution of the workflow by navigating to http://localhost:5000/execution/<workflowId>.

Step 4 - Poll for Worker Task

Notice that the workflow is running. If you click on simple_worker_ref_1 in the diagram you can see tha that the task is marked as SCHEDULED. Conductor is ready to send this task off for processing, but we have not yet connected our worker to Conductor to do that work.

In the next step, we will create the worker that is used to complete our Conductor task. We'll also connect it to our Conductor instance so that the simple_worker tasks can be completed, and allow our workflows complete.

Step 5 Building the worker

To simplify this tutorial, we have placed the simple_worker worker in a GitHub repository.

Clone this repository to your local machine, and open it in your IDE of choice. Our worker will poll the conductor server every second to see if there is a task in it's queue. For that to happen, we need to tell the worker where our Conductor server is. To do this, we must modify the file /src/main/resources/

  • For a local Conductor installation, change conductor.server.url to http://localhost:5000.

  • For Playground, you'll need to create an application to create a key/secret that authenticates your worker with the playground, and then add the values of the key and secret to lines 2 and 3 of as indicated.

How Workers and Conductor interact

The is the runnable Java application. * taskClient creates an Orkes taskClient, and uses the url (and uses the Java SDK to create an access token from the Key and secret for Orkes Playground).

  • TaskRunnerConfigurer grabs a list of workers (there are many in the repository), and creates a thread for each one. These worker threads will poll the Conductor server to find tasks hat require to be run.

Step 6: Running your worker

Run your Java application to start running your worker.

After running your application, it will be able to poll and run your worker. Let's go back and load up the previously created workflow in your UI.

Conductor UI - Workflow Run

In your worker you had this implementation:

     result.addOutputData("currentTimeOnServer", currentTimeOnServer);
result.addOutputData("message", "Hello World!");

As you can see in the screenshot above the worker has added these outputs to the workflow run. Play around with this, change the outputs and re-run and see how it works.


In this blog post — we learned how to run a sample workflow in our Conductor installation with a custom worker. Concepts we touched on:

  1. Adding Task (worker) definition
  2. Adding Workflow definition with a custom SIMPLE task
  3. Running Conductor client using the Java SDK

Try out more complex workflows in our other codelabs, or build your own. We've created a number of workflows that you can edit in our use cases section.