Step 3: Adding Custom Code Worker
Continuing the use case from the previous step, we now have a requirement to add a fraud check for all deposit transactions >= $10,000.
- UI
- In your current definition, add a Switch task before the deposit task.
- Add a switch case for checking amounts >= 10000, and add a Worker task for the case with the name
fraud-check. - Run workflow.
We can see that when we run this workflow for amounts >= $10,000, it runs a fraud check. If we named the task fraud-check, we'd notice that it is actually executed (in Developer Playground env), but how?
That's because there is a pre-defined task that is polling and running all the tasks named fraud-check. We also have the required permissions in the Developer Playground for this task.
So how can we implement this task for ourselves? First, let's rename the task to a new unique name for ourselves - for ex: fraud-check-<replace-with-a-unique-value>. And now, let’s see how this custom fraud check can be implemented:
View our documentation on Conductor Clients & SDKs list and how to import the required dependencies in our applications. Refer to the linked repositories in the code samples below to see how to implement the worker.
- Java
- Python
- Go
- Clojure
- C#
- JavaScript
- Typescript
/**
* Note: Using this setting, up to 5 tasks will run in parallel, with tasks being polled every 200ms
*/
@WorkerTask(value = "fraud-check", threadCount = 5, pollingInterval = 200)
public FraudCheckResult checkForFraudTask(DepositDetail depositDetail) {
return fraudCheckService.checkForFraud(depositDetail);
}
from conductor.client.http.models import Task, TaskResult
from conductor.client.http.models.task_result_status import TaskResultStatus
from conductor.client.worker.worker_interface import WorkerInterface
from conductor.client.configuration.configuration import Configuration
from conductor.client.configuration.settings.authentication_settings import AuthenticationSettings
from conductor.client.automator.task_handler import TaskHandler
from conductor.client.worker.worker import Worker
from conductor.client.worker.worker_task import WorkerTask
### Add these lines if not running on unix###
from multiprocessing import set_start_method
set_start_method("fork")
#############################################
SERVER_API_URL = 'https://developer.orkescloud.com/api'
KEY_ID = '<KEY_ID>'
KEY_SECRET = '<KEY_SECRET>'
# Create worker using a class implemented by WorkerInterface
class SimplePythonWorker(WorkerInterface):
def execute(self, task: Task) -> TaskResult:
task_result = self.get_task_result_from_task(task)
task_result.add_output_data('worker_style', 'class')
task_result.add_output_data('secret_number', 1234)
task_result.add_output_data('is_it_true', False)
task_result.status = TaskResultStatus.COMPLETED
return task_result
def get_polling_interval_in_seconds(self) -> float:
# poll every 500ms
return 0.5
# Create worker using a function
def execute(task: Task) -> TaskResult:
task_result = TaskResult(
task_id=task.task_id,
workflow_instance_id=task.workflow_instance_id,
worker_id='your_custom_id'
)
task_result.add_output_data('worker_style', 'function')
task_result.status = TaskResultStatus.COMPLETED
return task_result
# Create worker using a WorkerTask decorator
@WorkerTask(task_definition_name='python_annotated_task', worker_id='decorated')
def python_annotated_task(input) -> object:
return {'message': 'python is so great :)'}
# Create worker with domain using a WorkerTask decorator
@WorkerTask(task_definition_name='python_annotated_task', worker_id='decorated_domain', domain='cool')
def python_annotated_task_with_domain(input) -> object:
return {'message': 'python is so great and cool :)'}
configuration = Configuration(
server_api_url=SERVER_API_URL,
debug=True,
authentication_settings=AuthenticationSettings(
key_id=KEY_ID,
key_secret=KEY_SECRET
),
)
workers = [
SimplePythonWorker(
task_definition_name='python_task_example'
),
Worker(
task_definition_name='python_execute_example',
execute_function=execute,
poll_interval=0.25,
)
]
# If there are decorated workers in your application, scan_for_annotated_workers should be set
# default value of scan_for_annotated_workers is False
with TaskHandler(workers, configuration, scan_for_annotated_workers=True) as task_handler:
task_handler.start_processes()
task_handler.join_processes()
// @TODO:Gustavo
@WorkerTask("fraud-check")
public String checkForFraud(@InputParam("amount") BigDecimal amount, @InputParam("accountId") String accountId) {
boolean isFraud = fraudService.isFraudulentTxn(accountId, amount);
if(isFraud) {
return "This transaction cannot be processed as its flagged for review.";
}
return "Deposit of " + amount + " has processed successfully";
}
(def fraud-check-worker
{:name "fraud-check",
:execute (fn [d]
(let [amount (get-in d [:inputData "amount"])
accountId (get-in d [:inputData "accountId"] )]
{:status "COMPLETED"
:outputData {"message" (if (account-service amount accountId)
"This transaction cannot be processed as its flagged for review."
(str "Deposit of " amount " has processed successfully"))}}))})
// Note: Using this setting, up to 5 tasks will run in parallel, with tasks being polled every 200ms
[WorkerTask(taskType: "fraud-check", batchSize: 5, domain: null, pollIntervalMs: 200, workerId: "workerId")]
public TaskResult FraudWorker(Task task)
{
var depositDetail = (DepositDetail)task.InputData["depositDetail"];
var fraudCheckResult = _fraudCheckService.CheckForFraud(depositDetail);
var result = task.Completed();
result.OutputData = Examples.Util.TypeUtil.GetDictionaryFromObject(fraudCheckResult);
return result;
}
const fraudCheckWorker = {
taskDefName: "fraud-check",
execute: async ({ inputData }) => {
const { amount, accountId } = inputData;
const fraudResult = fraudService.isFraudulentTxn(accountId, amount);
return {
outputData: fraudResult,
status: "COMPLETED",
};
},
domain: "fraud", // Optional
pollInterval: 100, // Optional, can be specified in the TaskManager
concurrency: 1, // Optional, can be specified in the TaskManager
};
export const fraudCheckWorker: ConductorWorker = {
taskDefName: "fraud-check",
execute: async ({ inputData }) => {
const amount = inputData?.amount;
const accountId = inputData?.accountId;
const fraudResult = fraudService.isFraudulentTxn(accountId, amount);
return {
outputData: fraudResult,
status: "COMPLETED",
};
},
domain: "fraud", // Optional
pollInterval: 100, // Optional, can be specified in the TaskManager
concurrency: 2, // Optional, can be specified in the TaskManager
};
Once we have cloned the repo or copied the required elements to our local machines, we can run this locally by connecting to the Conductor server.
To do this, we must give the required permissions to our application.
Refer to this video to add permission to execute the custom worker we just created above (fraud-check-<replace-with-a-unique-value>).
After providing the permissions, we can change the definition to run our worker (fraud-check-<replace-with-a-unique-value>) and start the application.
We can see that now our worker is picking up the task.
This is the first example of how a distributed worker is executed in Conductor; without exposing an endpoint or creating any sort of inbound connectivity, we were able to execute the task directly from our local machine pointing to the Developer Playground server.
We can run similar workflows in production, too, workers could live in any applications or even third-party services and we can connect them all together using Conductor. All of this without worrying about creating inbound connections or exposing unwanted API endpoints.