forked from temporalio/samples-python
-
Notifications
You must be signed in to change notification settings - Fork 0
/
hello_child_workflow.py
57 lines (44 loc) · 1.51 KB
/
hello_child_workflow.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
import asyncio
from dataclasses import dataclass
from temporalio import workflow
from temporalio.client import Client
from temporalio.worker import Worker
@dataclass
class ComposeGreetingInput:
greeting: str
name: str
@workflow.defn
class ComposeGreetingWorkflow:
@workflow.run
async def run(self, input: ComposeGreetingInput) -> str:
return f"{input.greeting}, {input.name}!"
@workflow.defn
class GreetingWorkflow:
@workflow.run
async def run(self, name: str) -> str:
return await workflow.execute_child_workflow(
ComposeGreetingWorkflow.run,
ComposeGreetingInput("Hello", name),
id="hello-child-workflow-workflow-child-id",
)
async def main():
# Start client
client = await Client.connect("localhost:7233")
# Run a worker for the workflow
async with Worker(
client,
task_queue="hello-child-workflow-task-queue",
workflows=[GreetingWorkflow, ComposeGreetingWorkflow],
):
# 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-child-workflow-workflow-id",
task_queue="hello-child-workflow-task-queue",
)
print(f"Result: {result}")
if __name__ == "__main__":
asyncio.run(main())