Workflow architecture

Dapr Workflows allow developers to define workflows using ordinary code in a variety of programming languages. The workflow engine runs inside of the Dapr sidecar and orchestrates workflow code deployed as part of your application. Dapr Workflows are built on top of Dapr Actors providing durability and scalability for workflow execution.

This article describes:

• The architecture of the Dapr Workflow engine
• How the workflow engine interacts with application code
• How the workflow engine fits into the overall Dapr architecture
• How different workflow backends can work with workflow engine

For more information on how to author Dapr Workflows in your application, see How to: Author a workflow.

The Dapr Workflow engine is internally powered by Dapr’s actor runtime. The following diagram illustrates the Dapr Workflow architecture in Kubernetes mode:

To use the Dapr Workflow building block, you write workflow code in your application using the Dapr Workflow SDK, which internally connects to the sidecar using a gRPC stream. This registers the workflow and any workflow activities, or tasks that workflows can schedule.

The engine is embedded directly into the sidecar and implemented using the durabletask-go framework library. This framework allows you to swap out different storage providers, including a storage provider created for Dapr that leverages internal actors behind the scenes. Since Dapr Workflows use actors, you can store workflow state in state stores.

Sidecar interactions

When a workflow application starts up, it uses a workflow authoring SDK to send a gRPC request to the Dapr sidecar and get back a stream of workflow work items, following the server streaming RPC pattern. These work items can be anything from “start a new X workflow” (where X is the type of a workflow) to “schedule activity Y with input Z to run on behalf of workflow X”.

The workflow app executes the appropriate workflow code and then sends a gRPC request back to the sidecar with the execution results.

All interactions happen over a single gRPC channel and are initiated by the application, which means the application doesn’t need to open any inbound ports. The details of these interactions are internally handled by the language-specific Dapr Workflow authoring SDK.

Differences between workflow and application actor interactions

If you’re familiar with Dapr actors, you may notice a few differences in terms of how sidecar interactions works for workflows compared to application defined actors.

Workflow distributed tracing

The durabletask-go core used by the workflow engine writes distributed traces using Open Telemetry SDKs. These traces are captured automatically by the Dapr sidecar and exported to the configured Open Telemetry provider, such as Zipkin.

Each workflow instance managed by the engine is represented as one or more spans. There is a single parent span representing the full workflow execution and child spans for the various tasks, including spans for activity task execution and durable timers.

Workflow activity code currently does not have access to the trace context.

Workflow actors

Upon the workflow client connecting to the sidecar, there are two types of actors that are registered in support of the workflow engine:

• dapr.internal.{namespace}.{appID}.workflow
• dapr.internal.{namespace}.{appID}.activity

The {namespace} value is the Dapr namespace and defaults to default if no namespace is configured. The {appID} value is the app’s ID. For example, if you have a workflow app named “wfapp”, then the type of the workflow actor would be dapr.internal.default.wfapp.workflow and the type of the activity actor would be dapr.internal.default.wfapp.activity.

The following diagram demonstrates how workflow actors operate in a Kubernetes scenario:

Just like user-defined actors, workflow actors are distributed across the cluster by the hashing lookup table provided by the actor placement service. They also maintain their own state and make use of reminders. However, unlike actors that live in application code, these workflow actors are embedded into the Dapr sidecar. Application code is completely unaware that these actors exist.

Workflow actors

There are 2 different types of actors used with workflows: workflow actors and activity actors. Workflow actors are responsible for managing the state and placement of all workflows running in the app. A new instance of the workflow actor is activated for every workflow instance that gets scheduled. The ID of the workflow actor is the ID of the workflow. This workflow actor stores the state of the workflow as it progresses, and determines the node on which the workflow code executes via the actor lookup table.

As workflows are based on actors, all workflow and activity work is randomly distributed across all replicas of the application implementing workflows. There is no locality or relationship between where a workflow is started and where each work item is executed.

Each workflow actor saves its state using the following keys in the configured actor state store:

The following diagram illustrates the typical lifecycle of a workflow actor.

To summarize:

1. A workflow actor is activated when it receives a new message.
2. New messages then trigger the associated workflow code (in your application) to run and return an execution result back to the workflow actor.
3. Once the result is received, the actor schedules any tasks as necessary.
4. After scheduling, the actor updates its state in the state store.
5. Finally, the actor goes idle until it receives another message. During this idle time, the sidecar may decide to unload the workflow actor from memory.

Activity actors

Activity actors are responsible for managing the state and placement of all workflow activity invocations. A new instance of the activity actor is activated for every activity task that gets scheduled by a workflow. The ID of the activity actor is the ID of the workflow combined with a sequence number (sequence numbers start with 0), as well as the “generation” (incremented during instances of rerunning from using continue as new). For example, if a workflow has an ID of 876bf371 and is the third activity to be scheduled by the workflow, it’s ID will be 876bf371::2::1 where 2 is the sequence number, and 1 is the generation. If the activity is scheduled again after a continue as new, the ID will be 876bf371::2::2.

No state is stored by activity actors, and instead all resulting data is sent back to the parent workflow actor.

The following diagram illustrates the typical lifecycle of an activity actor.

Activity actors are short-lived:

1. Activity actors are activated when a workflow actor schedules an activity task.
2. Activity actors then immediately call into the workflow application to invoke the associated activity code.
3. Once the activity code has finished running and has returned its result, the activity actor sends a message to the parent workflow actor with the execution results.
4. The activity actor then deactivates itself.
5. Once the results are sent, the workflow is triggered to move forward to its next step.

Reminder usage and execution guarantees

The Dapr Workflow ensures workflow fault-tolerance by using actor reminders to recover from transient system failures. Prior to invoking application workflow code, the workflow or activity actor will create a new reminder. These reminders are made “one shot”, meaning that they will expire after successful triggering. If the application code executes without interruption, the reminder is triggered and expired. However, if the node or the sidecar hosting the associated workflow or activity crashes, the reminder will reactivate the corresponding actor and the execution will be retried, forever.

State store usage

Dapr Workflows use actors internally to drive the execution of workflows. Like any actors, these workflow actors store their state in the configured actor state store. Any state store that supports actors implicitly supports Dapr Workflow.

As discussed in the workflow actors section, workflows save their state incrementally by appending to a history log. The history log for a workflow is distributed across multiple state store keys so that each “checkpoint” only needs to append the newest entries.

The size of each checkpoint is determined by the number of concurrent actions scheduled by the workflow before it goes into an idle state. Sequential workflows will therefore make smaller batch updates to the state store, while fan-out/fan-in workflows will require larger batches. The size of the batch is also impacted by the size of inputs and outputs when workflows invoke activities or child workflows.

Different state store implementations may implicitly put restrictions on the types of workflows you can author. For example, the Azure Cosmos DB state store limits item sizes to 2 MB of UTF-8 encoded JSON (source). The input or output payload of an activity or child workflow is stored as a single record in the state store, so a item limit of 2 MB means that workflow and activity inputs and outputs can’t exceed 2 MB of JSON-serialized data.

Similarly, if a state store imposes restrictions on the size of a batch transaction, that may limit the number of parallel actions that can be scheduled by a workflow.

Workflow state can be purged from a state store, including all its history. Each Dapr SDK exposes APIs for purging all metadata related to specific workflow instances.

State store record count

The number of records which are saved as history in the state store per workflow run is determined by its complexity or “shape”. In other words, the number of activities, timers, sub-workflows etc. The following table shows a general guide to the number of records that are saved by different workflow tasks. This number may be larger or smaller depending on retries or concurrency.

Workflow scalability

Because Dapr Workflows are internally implemented using actors, Dapr Workflows have the same scalability characteristics as actors. The placement service:

• Doesn’t distinguish between workflow actors and actors you define in your application
• Will load balance workflows using the same algorithms that it uses for actors

The expected scalability of a workflow is determined by the following factors:

• The number of machines used to host your workflow application
• The CPU and memory resources available on the machines running workflows
• The scalability of the state store configured for actors
• The scalability of the actor placement service and the reminder subsystem

The implementation details of the workflow code in the target application also plays a role in the scalability of individual workflow instances. Each workflow instance executes on a single node at a time, but a workflow can schedule activities and child workflows which run on other nodes.

Workflows can also schedule these activities and child workflows to run in parallel, allowing a single workflow to potentially distribute compute tasks across all available nodes in the cluster.

You can configure the maximum concurrent workflows and activities that can be executed at any one time with the following configuration. These limits are imposed on a per sidecar basis, meaning that if you have 10 replicas of your workflow app, the effective limit is 10 times the configured value. These limits do not distinguish between different workflow or activity definitions.

apiVersion: dapr.io/v1alpha1
kind: Configuration
metadata:
  name: appconfig
spec:
  workflow:
    maxConcurrentWorkflowInvocations: 100 # Default is infinite
    maxConcurrentActivityInvocations: 1000 # Default is infinite

Workflows don’t control the specifics of how load is distributed across the cluster. For example, if a workflow schedules 10 activity tasks to run in parallel, all 10 tasks may run on as many as 10 different compute nodes or as few as a single compute node. The actual scale behavior is determined by the actor placement service, which manages the distribution of the actors that represent each of the workflow’s tasks.

Workflow latency

In order to provide guarantees around durability and resiliency, Dapr Workflows frequently write to the state store and rely on reminders to drive execution. Dapr Workflows therefore may not be appropriate for latency-sensitive workloads. Expected sources of high latency include:

• Latency from the state store when persisting workflow state.
• Latency from the state store when rehydrating workflows with large histories.
• Latency caused by too many active reminders in the cluster.
• Latency caused by high CPU usage in the cluster.

See the Reminder usage and execution guarantees section for more details on how the design of workflow actors may impact execution latency.

Increasing scheduling throughput

By default, when a client schedules a workflow, the workflow engine waits for the workflow to be fully started before returning a response to the client. Waiting for the workflow to start before returning can decrease the scheduling throughput of workflows. When scheduling a workflow with a start time, the workflow engine does not wait for the workflow to start before returning a response to the client. To increase scheduling throughput, consider adding a start time of “now” when scheduling a workflow. An example of scheduling a workflow with a start time of “now” in the Go SDK is shown below:

client.ScheduleNewWorkflow(ctx, "MyCoolWorkflow", workflow.WithStartTime(time.Now()))

Workflows cluster deployment when using Dapr Shared with workflow

When using Dapr Shared, it can be the case that there are multiple daprd sidecars running behind a single load balancer or service. As such, the instance to which a worker receiving work, may not be the same instance that receives the work result. Dapr creates a third actor type to handle this scenario: dapr.internal.{namespace}.{appID}.executor to handle routing of the worker results back to the correct workflow actor to ensure correct operation.

Next steps

Related links

• Workflow overview
• Workflow API reference
• Try out the Workflow quickstart
• Try out the following examples:
  • Python
  • JavaScript example
  • .NET
  • Java
  • Go example