[Workflow] Add Python samples to patterns doc

Signed-off-by: Chris Gillum <cgillum@microsoft.com>
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Chris Gillum 2023-06-08 10:44:05 -07:00
parent 3d8b8a6103
commit b4f1e85d4b
1 changed files with 97 additions and 10 deletions

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@ -25,9 +25,10 @@ While the pattern is simple, there are many complexities hidden in the implement
Dapr Workflow solves these complexities by allowing you to implement the task chaining pattern concisely as a simple function in the programming language of your choice, as shown in the following example.
{{< tabs ".NET" >}}
{{< tabs ".NET" Python >}}
{{% codetab %}}
<!--dotnet-->
```csharp
// Expotential backoff retry policy that survives long outages
@ -45,7 +46,6 @@ try
var result1 = await context.CallActivityAsync<string>("Step1", wfInput, retryOptions);
var result2 = await context.CallActivityAsync<byte[]>("Step2", result1, retryOptions);
var result3 = await context.CallActivityAsync<long[]>("Step3", result2, retryOptions);
var result4 = await context.CallActivityAsync<Guid[]>("Step4", result3, retryOptions);
return string.Join(", ", result4);
}
catch (TaskFailedException) // Task failures are surfaced as TaskFailedException
@ -56,14 +56,61 @@ catch (TaskFailedException) // Task failures are surfaced as TaskFailedException
}
```
{{% alert title="Note" color="primary" %}}
In the example above, `"Step1"`, `"Step2"`, `"Step3"`, and `"MyCompensation"` represent workflow activities, which are functions in your code that actually implement the steps of the workflow. For brevity, these activity implementations are left out of this example.
{{% /alert %}}
{{% /codetab %}}
{{% codetab %}}
<!--python-->
```python
import dapr.ext.workflow as wf
def task_chain_workflow(ctx: wf.DaprWorkflowContext, wf_input: int):
try:
result1 = yield ctx.call_activity(step1, input=wf_input)
result2 = yield ctx.call_activity(step2, input=result1)
result3 = yield ctx.call_activity(step3, input=result2)
except Exception as e:
yield ctx.call_activity(error_handler, input=str(e))
raise
return [result1, result2, result3]
def step1(ctx, activity_input):
print(f'Step 1: Received input: {activity_input}.')
# Do some work
return activity_input + 1
def step2(ctx, activity_input):
print(f'Step 2: Received input: {activity_input}.')
# Do some work
return activity_input * 2
def step3(ctx, activity_input):
print(f'Step 3: Received input: {activity_input}.')
# Do some work
return activity_input ^ 2
def error_handler(ctx, error):
print(f'Executing error handler: {error}.')
# Do some compensating work
```
{{% alert title="Note" color="primary" %}}
Workflow retry policies will be available in a future version of the Python SDK.
{{% /alert %}}
{{% /codetab %}}
{{< /tabs >}}
{{% alert title="Note" color="primary" %}}
In the example above, `"Step1"`, `"Step2"`, `"MyCompensation"`, etc. represent workflow activities, which are functions in your code that actually implement the steps of the workflow. For brevity, these activity implementations are left out of this example.
{{% /alert %}}
As you can see, the workflow is expressed as a simple series of statements in the programming language of your choice. This allows any engineer in the organization to quickly understand the end-to-end flow without necessarily needing to understand the end-to-end system architecture.
Behind the scenes, the Dapr Workflow runtime:
@ -88,9 +135,10 @@ In addition to the challenges mentioned in [the previous pattern]({{< ref "workf
Dapr Workflows provides a way to express the fan-out/fan-in pattern as a simple function, as shown in the following example:
{{< tabs ".NET" >}}
{{< tabs ".NET" Python >}}
{{% codetab %}}
<!--dotnet-->
```csharp
// Get a list of N work items to process in parallel.
@ -114,6 +162,15 @@ await context.CallActivityAsync("PostResults", sum);
{{% /codetab %}}
{{% codetab %}}
<!--python-->
```python
# TODO
```
{{% /codetab %}}
{{< /tabs >}}
The key takeaways from this example are:
@ -214,9 +271,10 @@ Depending on the business needs, there may be a single monitor or there may be m
Dapr Workflow supports this pattern natively by allowing you to implement _eternal workflows_. Rather than writing infinite while-loops ([which is an anti-pattern]({{< ref "workflow-features-concepts.md#infinite-loops-and-eternal-workflows" >}})), Dapr Workflow exposes a _continue-as-new_ API that workflow authors can use to restart a workflow function from the beginning with a new input.
{{< tabs ".NET" >}}
{{< tabs ".NET" Python >}}
{{% codetab %}}
<!--dotnet-->
```csharp
public override async Task<object> RunAsync(WorkflowContext context, MyEntityState myEntityState)
@ -256,6 +314,15 @@ public override async Task<object> RunAsync(WorkflowContext context, MyEntitySta
{{% /codetab %}}
{{% codetab %}}
<!--python-->
```python
# TODO
```
{{% /codetab %}}
{{< /tabs >}}
A workflow implementing the monitor pattern can loop forever or it can terminate itself gracefully by not calling _continue-as-new_.
@ -284,9 +351,10 @@ The following diagram illustrates this flow.
The following example code shows how this pattern can be implemented using Dapr Workflow.
{{< tabs ".NET" >}}
{{< tabs ".NET" Python >}}
{{% codetab %}}
<!--dotnet-->
```csharp
public override async Task<OrderResult> RunAsync(WorkflowContext context, OrderPayload order)
@ -331,13 +399,23 @@ In the example above, `RequestApprovalActivity` is the name of a workflow activi
{{% /codetab %}}
{{% codetab %}}
<!--python-->
```python
# TODO
```
{{% /codetab %}}
{{< /tabs >}}
The code that delivers the event to resume the workflow execution is external to the workflow. Workflow events can be delivered to a waiting workflow instance using the [raise event]({{< ref "howto-manage-workflow.md#raise-an-event" >}}) workflow management API, as shown in the following example:
{{< tabs ".NET" >}}
{{< tabs ".NET" Python >}}
{{% codetab %}}
<!--dotnet-->
```csharp
// Raise the workflow event to the waiting workflow
@ -350,6 +428,15 @@ await daprClient.RaiseWorkflowEventAsync(
{{% /codetab %}}
{{% codetab %}}
<!--python-->
```python
# TODO
```
{{% /codetab %}}
{{< /tabs >}}
External events don't have to be directly triggered by humans. They can also be triggered by other systems. For example, a workflow may need to pause and wait for a payment to be received. In this case, a payment system might publish an event to a pub/sub topic on receipt of a payment, and a listener on that topic can raise an event to the workflow using the raise event workflow API.