Workflow can be defined as the collection of tasks and operators that specify the order and execution of the defined tasks. This orchestration occurs in a hybrid ecosystem that encircles serverless functions, microservices, and monolithic applications.
This section will dive deeper into creating and executing Conductor workflows using Python SDK.
- Creating Workflows
- Executing Workflows
- Managing Workflow Executions
- Searching for Workflows
- Handling Failures, Retries and Rate Limits
Conductor lets you create the workflows using either Python or JSON as the configuration.
Using Python as code to define and execute workflows lets you build extremely powerful, dynamic workflows and run them on Conductor.
When the workflows are relatively static, they can be designed using the Orkes UI (available when using Orkes Conductor) and APIs or SDKs to register and run the workflows.
Both the code and configuration approaches are equally powerful and similar in nature to how you treat Infrastructure as Code.
For cases where the workflows cannot be created statically ahead of time, Conductor is a powerful dynamic workflow execution platform that lets you create very complex workflows in code and execute them. It is useful when the workflow is unique for each execution.
from conductor.client.automator.task_handler import TaskHandler
from conductor.client.configuration.configuration import Configuration
from conductor.client.orkes_clients import OrkesClients
from conductor.client.worker.worker_task import worker_task
from conductor.client.workflow.conductor_workflow import ConductorWorkflow
#@worker_task annotation denotes that this is a worker
@worker_task(task_definition_name='get_user_email')
def get_user_email(userid: str) -> str:
return f'{userid}@example.com'
#@worker_task annotation denotes that this is a worker
@worker_task(task_definition_name='send_email')
def send_email(email: str, subject: str, body: str):
print(f'sending email to {email} with subject {subject} and body {body}')
def main():
# defaults to reading the configuration using following env variables
# CONDUCTOR_SERVER_URL : conductor server e.g. https://play.orkes.io/api
# CONDUCTOR_AUTH_KEY : API Authentication Key
# CONDUCTOR_AUTH_SECRET: API Auth Secret
api_config = Configuration()
task_handler = TaskHandler(configuration=api_config)
#Start Polling
task_handler.start_processes()
clients = OrkesClients(configuration=api_config)
workflow_executor = clients.get_workflow_executor()
workflow = ConductorWorkflow(name='dynamic_workflow', version=1, executor=workflow_executor)
get_email = get_user_email(task_ref_name='get_user_email_ref', userid=workflow.input('userid'))
sendmail = send_email(task_ref_name='send_email_ref', email=get_email.output('result'), subject='Hello from Orkes',
body='Test Email')
#Order of task execution
workflow >> get_email >> sendmail
# Configure the output of the workflow
workflow.output_parameters(output_parameters={
'email': get_email.output('result')
})
#Run the workflow
result = workflow.execute(workflow_input={'userid': 'user_a'})
print(f'\nworkflow output: {result.output}\n')
#Stop Polling
task_handler.stop_processes()
if __name__ == '__main__':
main()>> python3 dynamic_workflow.py
2024-02-03 19:54:35,700 [32853] conductor.client.automator.task_handler INFO created worker with name=get_user_email and domain=None
2024-02-03 19:54:35,781 [32853] conductor.client.automator.task_handler INFO created worker with name=send_email and domain=None
2024-02-03 19:54:35,859 [32853] conductor.client.automator.task_handler INFO TaskHandler initialized
2024-02-03 19:54:35,859 [32853] conductor.client.automator.task_handler INFO Starting worker processes...
2024-02-03 19:54:35,861 [32853] conductor.client.automator.task_runner INFO Polling task get_user_email with domain None with polling interval 0.1
2024-02-03 19:54:35,861 [32853] conductor.client.automator.task_handler INFO Started 2 TaskRunner process
2024-02-03 19:54:35,862 [32853] conductor.client.automator.task_handler INFO Started all processes
2024-02-03 19:54:35,862 [32853] conductor.client.automator.task_runner INFO Polling task send_email with domain None with polling interval 0.1
sending email to user_a@example.com with subject Hello from Orkes and body Test Email
workflow output: {'email': 'user_a@example.com'}
2024-02-03 19:54:36,309 [32853] conductor.client.automator.task_handler INFO Stopped worker processes...See dynamic_workflow.py for a fully functional example.
For a more complex workflow example with all the supported features, see kitchensink.py.
The WorkflowClient interface provides all the APIs required to work with workflow executions.
from conductor.client.configuration.configuration import Configuration
from conductor.client.orkes_clients import OrkesClients
api_config = Configuration()
clients = OrkesClients(configuration=api_config)
workflow_client = clients.get_workflow_client() Useful when workflows are long-running.
from conductor.client.http.models import StartWorkflowRequest
request = StartWorkflowRequest()
request.name = 'hello'
request.version = 1
request.input = {'name': 'Orkes'}
# workflow id is the unique execution id associated with this execution
workflow_id = workflow_client.start_workflow(request)Applicable when workflows complete very quickly - usually under 20-30 seconds.
from conductor.client.http.models import StartWorkflowRequest
request = StartWorkflowRequest()
request.name = 'hello'
request.version = 1
request.input = {'name': 'Orkes'}
workflow_run = workflow_client.execute_workflow(
start_workflow_request=request,
wait_for_seconds=12)Note
See workflow_ops.py for a fully working application that demonstrates working with the workflow executions and sending signals to the workflow to manage its state.
Workflows represent the application state. With Conductor, you can query the workflow execution state anytime during its lifecycle. You can also send signals to the workflow that determines the outcome of the workflow state.
WorkflowClient is the client interface used to manage workflow executions.
from conductor.client.configuration.configuration import Configuration
from conductor.client.orkes_clients import OrkesClients
api_config = Configuration()
clients = OrkesClients(configuration=api_config)
workflow_client = clients.get_workflow_client()The following method lets you query the status of the workflow execution given the id. When the include_tasks is set, the response also includes all the completed and in-progress tasks.
get_workflow(workflow_id: str, include_tasks: Optional[bool] = True) -> WorkflowVariables inside a workflow are the equivalent of global variables in a program.
update_variables(self, workflow_id: str, variables: dict[str, object] = {})Used to terminate a running workflow. Any pending tasks are canceled, and no further work is scheduled for this workflow upon termination. A failure workflow will be triggered but can be avoided if trigger_failure_workflow is set to False.
terminate_workflow(self, workflow_id: str, reason: Optional[str] = None, trigger_failure_workflow: bool = False)If the workflow has failed due to one of the task failures after exhausting the retries for the task, the workflow can still be resumed by calling the retry.
retry_workflow(self, workflow_id: str, resume_subworkflow_tasks: Optional[bool] = False)When a sub-workflow inside a workflow has failed, there are two options:
- Re-trigger the sub-workflow from the start (Default behavior).
- Resume the sub-workflow from the failed task (set
resume_subworkflow_tasksto True).
A workflow in the terminal state (COMPLETED, TERMINATED, FAILED) can be restarted from the beginning. Useful when retrying from the last failed task is insufficient, and the whole workflow must be started again.
restart_workflow(self, workflow_id: str, use_latest_def: Optional[bool] = False)In the cases where a workflow needs to be restarted from a specific task rather than from the beginning, rerun provides that option. When issuing the rerun command to the workflow, you can specify the task ID from where the workflow should be restarted (as opposed to from the beginning), and optionally, the workflow's input can also be changed.
rerun_workflow(self, workflow_id: str, rerun_workflow_request: RerunWorkflowRequest)Tip
Rerun is one of the most powerful features Conductor has, giving you unparalleled control over the workflow restart.
A running workflow can be put to a PAUSED status. A paused workflow lets the currently running tasks complete but does not schedule any new tasks until resumed.
pause_workflow(self, workflow_id: str)Resume operation resumes the currently paused workflow, immediately evaluating its state and scheduling the next set of tasks.
resume_workflow(self, workflow_id: str)Workflow executions are retained until removed from the Conductor. This gives complete visibility into all the executions an application has - regardless of the number of executions. Conductor has a powerful search API that allows you to search for workflow executions.
search(self, start, size, free_text: str = '*', query: str = None) -> ScrollableSearchResultWorkflowSummary- free_text: Free text search to look for specific words in the workflow and task input/output.
- query SQL-like query to search against specific fields in the workflow.
Here are the supported fields for query:
| Field | Description |
|---|---|
| status | The status of the workflow. |
| correlationId | The ID to correlate the workflow execution to other executions. |
| workflowType | The name of the workflow. |
| version | The version of the workflow. |
| startTime | The start time of the workflow is in milliseconds. |
Conductor lets you embrace failures rather than worry about the complexities introduced in the system to handle failures.
All the aspects of handling failures, retries, rate limits, etc., are driven by the configuration that can be updated in real time without re-deploying your application.
Each task in the Conductor workflow can be configured to handle failures with retries, along with the retry policy (linear, fixed, exponential backoff) and maximum number of retry attempts allowed.
See Error Handling for more details.
What happens when a task is operating on a critical resource that can only handle a few requests at a time? Tasks can be configured to have a fixed concurrency (X request at a time) or a rate (Y tasks/time window).
from conductor.client.configuration.configuration import Configuration
from conductor.client.http.models import TaskDef
from conductor.client.orkes_clients import OrkesClients
def main():
api_config = Configuration()
clients = OrkesClients(configuration=api_config)
metadata_client = clients.get_metadata_client()
task_def = TaskDef()
task_def.name = 'task_with_retries'
task_def.retry_count = 3
task_def.retry_logic = 'LINEAR_BACKOFF'
task_def.retry_delay_seconds = 1
# only allow 3 tasks at a time to be in the IN_PROGRESS status
task_def.concurrent_exec_limit = 3
# timeout the task if not polled within 60 seconds of scheduling
task_def.poll_timeout_seconds = 60
# timeout the task if the task does not COMPLETE in 2 minutes
task_def.timeout_seconds = 120
# for the long running tasks, timeout if the task does not get updated in COMPLETED or IN_PROGRESS status in
# 60 seconds after the last update
task_def.response_timeout_seconds = 60
# only allow 100 executions in a 10-second window! -- Note, this is complementary to concurrent_exec_limit
task_def.rate_limit_per_frequency = 100
task_def.rate_limit_frequency_in_seconds = 10
metadata_client.register_task_def(task_def=task_def){
"name": "task_with_retries",
"retryCount": 3,
"retryLogic": "LINEAR_BACKOFF",
"retryDelaySeconds": 1,
"backoffScaleFactor": 1,
"timeoutSeconds": 120,
"responseTimeoutSeconds": 60,
"pollTimeoutSeconds": 60,
"timeoutPolicy": "TIME_OUT_WF",
"concurrentExecLimit": 3,
"rateLimitPerFrequency": 0,
"rateLimitFrequencyInSeconds": 1
}POST /api/metadata/taskdef -d @task_def.jsonSee task_configure.py for a detailed working app.