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hello_activity.py
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hello_activity.py
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import asyncio
from dataclasses import dataclass
from datetime import timedelta
from temporalio import activity, workflow
from temporalio.client import Client
from temporalio.worker import Worker
# While we could use multiple parameters in the activity, Temporal strongly
# encourages using a single dataclass instead which can have fields added to it
# in a backwards-compatible way.
@dataclass
class ComposeGreetingInput:
greeting: str
name: str
# Basic activity that logs and does string concatenation
@activity.defn
async def compose_greeting(input: ComposeGreetingInput) -> str:
activity.logger.info("Running activity with parameter %s" % input)
return f"{input.greeting}, {input.name}!"
# Basic workflow that logs and invokes an activity
@workflow.defn
class GreetingWorkflow:
@workflow.run
async def run(self, name: str) -> str:
workflow.logger.info("Running workflow with parameter %s" % name)
return await workflow.execute_activity(
compose_greeting,
ComposeGreetingInput("Hello", name),
start_to_close_timeout=timedelta(seconds=10),
)
async def main():
# Uncomment the lines below to see logging output
# import logging
# logging.basicConfig(level=logging.INFO)
# Start client
client = await Client.connect("localhost:7233")
# Run a worker for the workflow
async with Worker(
client,
task_queue="hello-activity-task-queue",
workflows=[GreetingWorkflow],
activities=[compose_greeting],
):
# While the worker is running, use the client to run the workflow and
# print out its result. Note, in many production setups, the client
# would be in a completely separate process from the worker.
result = await client.execute_workflow(
GreetingWorkflow.run,
"World",
id="hello-activity-workflow-id",
task_queue="hello-activity-task-queue",
)
print(f"Result: {result}")
if __name__ == "__main__":
asyncio.run(main())