mirror of https://github.com/dapr/docs.git
update python tabs, remove sdk from tabs
Signed-off-by: Hannah Hunter <hannahhunter@microsoft.com>
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Hannah Hunter
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29ce06fc1d
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@ -87,7 +87,55 @@ It also includes a `RunAsync` method that does the heavy lifting of the workflow
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- HTTP API calls to start, pause, resume, terminate, and purge the workflow.
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```python
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todo
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from dapr.clients import DaprClient
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from dapr.clients.grpc._helpers import to_bytes
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# ...
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# Dapr Workflows are registered as part of the service configuration
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with DaprClient() as d:
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instanceId = "RRLINSTANCEID35"
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workflowComponent = "dapr"
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workflowName = "PlaceOrder"
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workflowOptions = dict()
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workflowOptions["task_queue"] = "testQueue"
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inventoryItem = ("Computers", 5, 10)
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item2 = "paperclips"
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encoded_data = b''.join(bytes(str(element), 'UTF-8') for element in item2)
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encoded_data2 = json.dumps(item2).encode("UTF-8")
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# ...
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# Start the workflow
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start_resp = d.start_workflow(instance_id=instanceId, workflow_component=workflowComponent,
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workflow_name=workflowName, input=encoded_data2, workflow_options=workflowOptions)
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print(f"Attempting to start {workflowName}")
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print(f"start_resp {start_resp.instance_id}")
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# Get workflow status
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after start call: {getResponse.runtime_status}")
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# Pause Test
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d.pause_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after pause call: {getResponse.runtime_status}")
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# Resume Test
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d.resume_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after resume call: {getResponse.runtime_status}")
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# Terminate Test
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d.terminate_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after terminate call: {getResponse.runtime_status}")
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# Purge Test
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d.purge_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after purge call: {getResponse}")
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```
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@ -8,7 +8,7 @@ description: Manage and run workflows
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Now that you've [authored the workflow and its activities in your application]({{< ref howto-author-workflow.md >}}), you can start, terminate, and get information about the workflow using HTTP API calls. For more information, read the [workflow API reference]({{< ref workflow_api.md >}}).
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{{< tabs ".NET SDK" "Python SDK" HTTP >}}
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{{< tabs ".NET" Python HTTP >}}
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<!--NET-->
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{{% codetab %}}
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@ -48,54 +48,54 @@ Manage your workflow within your code. In the `OrderProcessingWorkflow` example
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- **purge_workflow**: Removes all metadata related to a specific workflow instance
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```python
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from dapr.clients import DaprClient
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dapr = DaprGrpcClient(f'localhost:{self.server_port}')
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from dapr.clients.grpc._helpers import to_bytes
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# ...
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# Sane parameters
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workflow_name = 'testWorkflow'
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instance_id = str(uuid.uuid4())
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workflow_component = "dapr"
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input = "paperclips"
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# Dapr Workflows are registered as part of the service configuration
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with DaprClient() as d:
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instanceId = "RRLINSTANCEID35"
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workflowComponent = "dapr"
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workflowName = "PlaceOrder"
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workflowOptions = dict()
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workflowOptions["task_queue"] = "testQueue"
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inventoryItem = ("Computers", 5, 10)
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item2 = "paperclips"
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# Start the workflow
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start_response = dapr.start_workflow(instance_id, workflow_name, workflow_component,
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input.encode('utf-8'), None)
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self.assertEqual(instance_id, start_response.instance_id)
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encoded_data = b''.join(bytes(str(element), 'UTF-8') for element in item2)
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encoded_data2 = json.dumps(item2).encode("UTF-8")
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# ...
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# Start the workflow
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start_resp = d.start_workflow(instance_id=instanceId, workflow_component=workflowComponent,
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workflow_name=workflowName, input=encoded_data2, workflow_options=workflowOptions)
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print(f"Attempting to start {workflowName}")
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print(f"start_resp {start_resp.instance_id}")
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# Get workflow status
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after start call: {getResponse.runtime_status}")
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# Get info on the workflow to check that it is running
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get_response = dapr.get_workflow(instance_id, workflow_component)
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self.assertEqual("RUNNING", get_response.runtime_status)
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# Pause Test
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d.pause_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after pause call: {getResponse.runtime_status}")
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# Pause the workflow
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dapr.pause_workflow(instance_id, workflow_component)
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# Resume Test
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d.resume_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after resume call: {getResponse.runtime_status}")
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# Get info on the workflow to check that it is paused
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get_response = dapr.get_workflow(instance_id, workflow_component)
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self.assertEqual("SUSPENDED", get_response.runtime_status)
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# Terminate Test
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d.terminate_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after terminate call: {getResponse.runtime_status}")
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# Resume the workflow
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dapr.resume_workflow(instance_id, workflow_component)
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# Purge Test
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d.purge_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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getResponse = d.get_workflow(instance_id=instanceId, workflow_component=workflowComponent)
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print(f"Get response from {workflowName} after purge call: {getResponse}")
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# Get info on the workflow to check that it is resumed
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get_response = dapr.get_workflow(instance_id, workflow_component)
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self.assertEqual("RUNNING", get_response.runtime_status)
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# Raise an event on the workflow.
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TODO: Figure out how to check/verify this
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# Terminate the workflow
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dapr.terminate_workflow(instance_id, workflow_component)
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# Get info on the workflow to check that it is terminated
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get_response = dapr.get_workflow(instance_id, workflow_component)
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self.assertEqual("TERMINATED", get_response.runtime_status)
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# Purge the workflow
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dapr.purge_workflow(instance_id, workflow_component)
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# Get information on the workflow to ensure that it has been purged
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TODO
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```
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{{% /codetab %}}
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