mirror of https://github.com/dapr/docs.git
Replat the workflows docs (#1497)
* Replat the workflows docs * Add more docs
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Aaron Crawfis
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@ -6,4 +6,224 @@ description: "Learn how to build workflows using Dapr Workflows and Logic Apps"
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weight: 4000
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---
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To enable developers to easily build workflow applications that use Dapr’s capabilities including diagnostics and multi-language support, you can use Dapr workflows. Dapr integrates with workflow engines such as Logic Apps runtime. For more information read [cloud-native workflows using Dapr and Logic Apps](https://cloudblogs.microsoft.com/opensource/2020/05/26/announcing-cloud-native-workflows-dapr-logic-apps/) and visit the [Dapr workflow](https://github.com/dapr/workflows) repo to try out the samples.
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Dapr Workflows is a lightweight host that allows developers to run cloud-native workflows locally, on-premises or any cloud environment using the [Azure Logic Apps](https://docs.microsoft.com/en-us/azure/logic-apps/logic-apps-overview) workflow engine and Dapr.
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## Benefits
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By using a workflow engine, business logic can be defined in a declarative, no-code fashion so application code doesn't need to change when a workflow changes. Dapr Workflows allows you to use workflows in a distributed application along with these added benefits:
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- **Run workflows anywhere**: on your local machine, on-premises, on Kubernetes or in the cloud
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- **Built-in observability**: tracing, metrics and mTLS through Dapr
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- **gRPC and HTTP endpoints** for your workflows
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- Kick off workflows based on **Dapr bindings** events
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- Orchestrate complex workflows by **calling back to Dapr** to save state, publish a message and more
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<img src="/images/workflows-diagram.png" width=500 alt="Diagram of Dapr Workflows">
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## How it works
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Dapr Workflows hosts a gRPC server that implements the Dapr Client API.
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This allows users to start workflows using gRPC and HTTP endpoints through Dapr, or start a workflow asynchronously using Dapr bindings.
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Once a workflow request comes in, Dapr Workflows uses the Logic Apps SDK to execute the workflow.
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## Supported workflow features
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### Supported actions and triggers
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- [HTTP](https://docs.microsoft.com/en-us/azure/connectors/connectors-native-http)
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- [Schedule](https://docs.microsoft.com/en-us/azure/logic-apps/concepts-schedule-automated-recurring-tasks-workflows)
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- [Request / Response](https://docs.microsoft.com/en-us/azure/connectors/connectors-native-reqres)
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### Supported control workflows
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- [All control workflows](https://docs.microsoft.com/en-us/azure/connectors/apis-list#control-workflow)
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### Supported data manipulation
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- [All data operations](https://docs.microsoft.com/en-us/azure/connectors/apis-list#manage-or-manipulate-data)
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### Not supported
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- [Managed connectors](https://docs.microsoft.com/en-us/azure/connectors/apis-list#managed-connectors)
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## Example
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Dapr Workflows can be used as the orchestrator for many otherwise complex activities. For example, invoking an external endpoint, saving the data to a state store, publishing the result to a different app or invoking a binding can all be done by calling back into Dapr from the workflow itself.
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This is due to the fact Dapr runs as a sidecar next to the workflow host just as if it was any other app.
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Examine [workflow2.json](/code/workflow.json) as an example of a workflow that does the following:
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1. Calls into Azure Functions to get a JSON response
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2. Saves the result to a Dapr state store
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3. Sends the result to a Dapr binding
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4. Returns the result to the caller
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Since Dapr supports many pluggable state stores and bindings, the workflow becomes portable between different environments (cloud, edge or on-premises) without the user changing the code - *because there is no code involved*.
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## Get started
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Prerequisites:
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1. Install the [Dapr CLI]({{< ref install-dapr-cli.md >}})
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2. [Azure blob storage account](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blob-create-account-block-blob?tabs=azure-portal)
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### Self-hosted
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1. Make sure you have the Dapr runtime initialized:
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```bash
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dapr init
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```
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1. Set up the environment variables containing the Azure Storage Account credentials:
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{{< tabs Windows "macOS/Linux" >}}
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{{% codetab %}}
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```bash
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export STORAGE_ACCOUNT_KEY=<YOUR-STORAGE-ACCOUNT-KEY>
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export STORAGE_ACCOUNT_NAME=<YOUR-STORAGE-ACCOUNT-NAME>
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```
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{{% /codetab %}}
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{{% codetab %}}
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```bash
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set STORAGE_ACCOUNT_KEY=<YOUR-STORAGE-ACCOUNT-KEY>
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set STORAGE_ACCOUNT_NAME=<YOUR-STORAGE-ACCOUNT-NAME>
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```
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{{% /codetab %}}
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{{< /tabs >}}
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1. Move to the workflows directory and run the sample runtime:
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```bash
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cd src/Dapr.Workflows
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dapr run --app-id workflows --protocol grpc --port 3500 --app-port 50003 -- dotnet run --workflows-path ../../samples
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```
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1. Invoke a workflow:
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```bash
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curl http://localhost:3500/v1.0/invoke/workflows/method/workflow1
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{"value":"Hello from Logic App workflow running with Dapr!"}
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```
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### Kubernetes
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1. Make sure you have a running Kubernetes cluster and `kubectl` in your path.
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1. Once you have the Dapr CLI installed, run:
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```bash
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dapr init --kubernetes
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```
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1. Wait until the Dapr pods have the status `Running`.
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1. Create a Config Map for the workflow:
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```bash
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kubectl create configmap workflows --from-file ./samples/workflow1. json
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```
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1. Create a secret containing the Azure Storage Account credentials. Replace the account name and key values below with the actual credentials:
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```bash
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kubectl create secret generic dapr-workflows --from-literal=accountName=<YOUR-STORAGE-ACCOUNT-NAME> --from-literal=accountKey=<YOUR-STORAGE-ACCOUNT-KEY>
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```
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1. Deploy Dapr Worfklows:
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```bash
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kubectl apply -f deploy/deploy.yaml
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```
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1. Create a port-forward to the dapr workflows container:
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```bash
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kubectl port-forward deploy/dapr-workflows-host 3500:3500
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```
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1. Invoke logic apps through Dapr:
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```bash
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curl http://localhost:3500/v1.0/invoke/workflows/method/workflow1
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{"value":"Hello from Logic App workflow running with Dapr!"}
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```
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## Invoking workflows using Dapr bindings
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1. First, create any [Dapr binding]({{< ref components-reference >}}) of your choice. See [this]({{< ref howto-triggers >}}) How-To tutorial.
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In order for Dapr Workflows to be able to start a workflow from a Dapr binding event, simply name the binding with the name of the workflow you want it to trigger.
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Here's an example of a Kafka binding that will trigger a workflow named `workflow1`:
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```yaml
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apiVersion: dapr.io/v1alpha1
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kind: Component
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metadata:
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name: workflow1
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spec:
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type: bindings.kafka
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metadata:
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- name: topics
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value: topic1
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- name: brokers
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value: localhost:9092
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- name: consumerGroup
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value: group1
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- name: authRequired
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value: "false"
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```
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1. Next, apply the Dapr component:
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{{< tabs Self-hosted Kubernetes >}}
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{{% codetab %}}
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Place the binding yaml file above in a `components` directory at the root of your application.
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{{% /codetab %}}
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{{% codetab %}}
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```bash
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kubectl apply -f my_binding.yaml
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```
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{{% /codetab %}}
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{{< /tabs >}}
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1. Once an event is sent to the bindings component, check the logs Dapr Workflows to see the output.
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{{< tabs Self-hosted Kubernetes >}}
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{{% codetab %}}
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In standalone mode, the output will be printed to the local terminal.
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{{% /codetab %}}
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{{% codetab %}}
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On Kubernetes, run the following command:
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```bash
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kubectl logs -l app=dapr-workflows-host -c host
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```
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{{% /codetab %}}
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{{< /tabs >}}
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## Example
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Watch an example from the Dapr community call:
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<iframe width="560" height="315" src="https://www.youtube.com/embed/7fP-0Ixmi-w?start=116" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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## Additional resources
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- [Blog announcement](https://cloudblogs.microsoft.com/opensource/2020/05/26/announcing-cloud-native-workflows-dapr-logic-apps/)
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- [Repo](https://github.com/dapr/workflows)
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@ -0,0 +1,82 @@
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{
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"definition": {
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"$schema": "https://schema.management.azure.com/providers/Microsoft.Logic/schemas/2016-06-01/workflowdefinition.json#",
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"actions": {
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"Function": {
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"type": "Http",
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"inputs": {
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"method": "GET",
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"uri": "https://daprfunc.azurewebsites.net/api/HttpTrigger1"
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},
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"runAfter": {}
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},
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"SaveState": {
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"type": "Http",
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"inputs": {
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"method": "POST",
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"uri": "http://localhost:3500/v1.0/state/statestore",
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"body": [
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{
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"key": "mystate",
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"value": "@body('Function')"
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}
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],
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"headers": {
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"Content-Type": "application/json"
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}
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},
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"runAfter": {
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"Function": [
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"Succeeded"
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]
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}
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},
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"SendToServiceBus": {
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"type": "Http",
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"inputs": {
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"method": "POST",
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"uri": "http://localhost:3500/v1.0/bindings/servicebus",
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"body": {
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"data": {
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"message": "@body('Function')"
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}
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},
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"headers": {
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"Content-Type": "application/json"
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}
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},
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"runAfter": {
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"SaveState": [
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"Succeeded"
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]
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}
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},
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"Response": {
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"inputs": {
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"body": {
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"value": "@body('Function')"
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},
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"statusCode": 200
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},
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"runAfter": {
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"SendToServiceBus": [
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"Succeeded"
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]
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},
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"type": "Response"
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}
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},
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"contentVersion": "1.0.0.0",
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"outputs": {},
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"parameters": {},
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"triggers": {
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"manual": {
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"inputs": {
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"schema": {}
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},
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"kind": "Http",
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"type": "Request"
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}
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}
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}
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}
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