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Merge branch 'endgame_1.15-updates' of https://github.com/hhunter-ms/docs into endgame_1.15-updates

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Hannah Hunter 2025-02-12 16:02:44 -05:00
parent 68b0f30e1c 99da138ee5
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96 changed files with 2566 additions and 784 deletions
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.github/workflows/link_validation.yaml vendored
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@ -13,7 +13,7 @@ jobs:
validate:
runs-on: ubuntu-latest
env:
PYTHON_VER: 3.7
PYTHON_VER: 3.12
steps:
- uses: actions/checkout@v2
- name: Check Microsoft URLs do not pin localized versions
@ -27,7 +27,7 @@ jobs:
exit 1
fi
- name: Set up Python ${{ env.PYTHON_VER }}
uses: actions/setup-python@v2
uses: actions/setup-python@v5
with:
python-version: ${{ env.PYTHON_VER }}
- name: Install dependencies

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daprdocs/config.toml
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@ -109,7 +109,7 @@ id = "G-60C6Q1ETC1"
lang = "en"
[[module.mounts]]
source = "../sdkdocs/rust/daprdocs/content/en/rust-sdk-contributing"
target = "content/contributing/sdks-contrib"
target = "content/contributing/sdk-contrib/"
lang = "en"
[[module.mounts]]
@ -143,7 +143,11 @@ id = "G-60C6Q1ETC1"
[[module.mounts]]
source = "../translations/docs-zh/translated_content/zh_CN/sdks_js"
target = "content/developing-applications/sdks/js"
lang = "zh-hans"
lang = "zh-hans"
[[module.mounts]]
source = "../translations/docs-zh/translated_content/zh_CN/sdks_rust"
target = "content/developing-applications/sdks/rust"
lang = "zh-hans"
[[module.mounts]]
source = "../translations/docs-zh/translated_content/zh_CN/pluggable-components/dotnet"
target = "content/developing-applications/develop-components/pluggable-components/pluggable-components-sdks/pluggable-components-dotnet"

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daprdocs/content/en/concepts/building-blocks-concept.md
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@ -22,7 +22,7 @@ Dapr provides the following building blocks:
|----------------|----------|-------------|
| [**Service-to-service invocation**]({{< ref "service-invocation-overview.md" >}}) | `/v1.0/invoke` | Service invocation enables applications to communicate with each other through well-known endpoints in the form of http or gRPC messages. Dapr provides an endpoint that acts as a combination of a reverse proxy with built-in service discovery, while leveraging built-in distributed tracing and error handling.
| [**Publish and subscribe**]({{< ref "pubsub-overview.md" >}}) | `/v1.0/publish` `/v1.0/subscribe`| Pub/Sub is a loosely coupled messaging pattern where senders (or publishers) publish messages to a topic, to which subscribers subscribe. Dapr supports the pub/sub pattern between applications.
| [**Workflows**]({{< ref "workflow-overview.md" >}}) | `/v1.0/workflow` | The Workflow API enables you to define long running, persistent processes or data flows that span multiple microservices using Dapr workflows or workflow components. The Workflow API can be combined with other Dapr API building blocks. For example, a workflow can call another service with service invocation or retrieve secrets, providing flexibility and portability.
| [**Workflows**]({{< ref "workflow-overview.md" >}}) | `/v1.0/workflow` | The Workflow API enables you to define long running, persistent processes or data flows that span multiple microservices using Dapr workflows. The Workflow API can be combined with other Dapr API building blocks. For example, a workflow can call another service with service invocation or retrieve secrets, providing flexibility and portability.
| [**State management**]({{< ref "state-management-overview.md" >}}) | `/v1.0/state` | Application state is anything an application wants to preserve beyond a single session. Dapr provides a key/value-based state and query APIs with pluggable state stores for persistence.
| [**Bindings**]({{< ref "bindings-overview.md" >}}) | `/v1.0/bindings` | A binding provides a bi-directional connection to an external cloud/on-premise service or system. Dapr allows you to invoke the external service through the Dapr binding API, and it allows your application to be triggered by events sent by the connected service.
| [**Actors**]({{< ref "actors-overview.md" >}}) | `/v1.0/actors` | An actor is an isolated, independent unit of compute and state with single-threaded execution. Dapr provides an actor implementation based on the virtual actor pattern which provides a single-threaded programming model and where actors are garbage collected when not in use.

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@ -78,13 +78,6 @@ Pub/sub broker components are message brokers that can pass messages to/from ser
- [List of pub/sub brokers]({{< ref supported-pubsub >}})
- [Pub/sub broker implementations](https://github.com/dapr/components-contrib/tree/master/pubsub)
### Workflows
A [workflow]({{< ref workflow-overview.md >}}) is custom application logic that defines a reliable business process or data flow. Workflow components are workflow runtimes (or engines) that run the business logic written for that workflow and store their state into a state store.
<!--- [List of supported workflows]()
- [Workflow implementations](https://github.com/dapr/components-contrib/tree/master/workflows)-->
### State stores
State store components are data stores (databases, files, memory) that store key-value pairs as part of the [state management]({{< ref "state-management-overview.md" >}}) building block.

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daprdocs/content/en/concepts/dapr-services/placement.md
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@ -13,7 +13,9 @@ The Placement service Docker container is started automatically as part of [`dap
## Kubernetes mode
The Placement service is deployed as part of `dapr init -k`, or via the Dapr Helm charts. For more information on running Dapr on Kubernetes, visit the [Kubernetes hosting page]({{< ref kubernetes >}}).
The Placement service is deployed as part of `dapr init -k`, or via the Dapr Helm charts. You can run Placement in high availability (HA) mode. [Learn more about setting HA mode in your Kubernetes service.]({{< ref "kubernetes-production.md#individual-service-ha-helm-configuration" >}})
For more information on running Dapr on Kubernetes, visit the [Kubernetes hosting page]({{< ref kubernetes >}}).
## Placement tables

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daprdocs/content/en/concepts/dapr-services/scheduler.md
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@ -5,20 +5,118 @@ linkTitle: "Scheduler"
description: "Overview of the Dapr scheduler service"
---
The Dapr Scheduler service is used to schedule jobs, running in [self-hosted mode]({{< ref self-hosted >}}) or on [Kubernetes]({{< ref kubernetes >}}).
The Dapr Scheduler service is used to schedule different types of jobs, running in [self-hosted mode]({{< ref self-hosted >}}) or on [Kubernetes]({{< ref kubernetes >}}).
- Jobs created through the Jobs API
- Actor reminder jobs (used by the actor reminders)
- Actor reminder jobs created by the Workflow API (which uses actor reminders)
The diagram below shows how the Scheduler service is used via the jobs API when called from your application. All the jobs that are tracked by the Scheduler service are stored in an embedded Etcd database.
From Dapr v1.15, the Scheduler service is used by default to schedule actor reminders as well as actor reminders for the Workflow API.
There is no concept of a leader Scheduler instance. All Scheduler service replicas are considered peers. All receive jobs to be scheduled for execution and the jobs are allocated between the available Scheduler service replicas for load balancing of the trigger events.
The diagram below shows how the Scheduler service is used via the jobs API when called from your application. All the jobs that are tracked by the Scheduler service are stored in an embedded etcd database.
<img src="/images/scheduler/scheduler-architecture.png" alt="Diagram showing the Scheduler control plane service and the jobs API">
## Actor Reminders
Prior to Dapr v1.15, [actor reminders]({{< ref "actors-timers-reminders.md#actor-reminders" >}}) were run using the Placement service. Now, by default, the [`SchedulerReminders` feature flag]({{< ref "support-preview-features.md#current-preview-features" >}}) is set to `true`, and all new actor reminders you create are run using the Scheduler service to make them more scalable.
When you deploy Dapr v1.15, any _existing_ actor reminders are automatically migrated from the Actor State Store to the Scheduler service as a one time operation for each actor type. Each replica will only migrate the reminders whose actor type and id are associated with that host. This means that only when all replicas implementing an actor type are upgraded to 1.15, will all the reminders associated with that type be migrated. There will be _no_ loss of reminder triggers during the migration. However, you can prevent this migration and keep the existing actor reminders running using the Actor State Store by setting the `SchedulerReminders` flag to `false` in the application configuration file for the actor type.
To confirm that the migration was successful, check the Dapr sidecar logs for the following:
```sh
Running actor reminder migration from state store to scheduler
```
coupled with
```sh
Migrated X reminders from state store to scheduler successfully
```
or
```sh
Skipping migration, no missing scheduler reminders found
```
## Job Locality
### Default Job Behavior
By default, when the Scheduler service triggers jobs, they are sent back to a single replica for the same app ID that scheduled the job in a randomly load balanced manner. This provides basic load balancing across your application's replicas, which is suitable for most use cases where strict locality isn't required.
### Using Actor Reminders for Perfect Locality
For users who require perfect job locality (having jobs triggered on the exact same host that created them), actor reminders provide a solution. To enforce perfect locality for a job:
1. Create an actor type with a random UUID that is unique to the specific replica
2. Use this actor type to create an actor reminder
This approach ensures that the job will always be triggered on the same host which created it, rather than being randomly distributed among replicas.
## Job Triggering
### Job Failure Policy and Staging Queue
When the Scheduler service triggers a job and it has a client side error, the job is retried by default with a 1s interval and 3 maximum retries.
For non-client side errors, for example, when a job cannot be sent to an available Dapr sidecar at trigger time, it is placed in a staging queue within the Scheduler service. Jobs remain in this queue until a suitable sidecar instance becomes available, at which point they are automatically sent to the appropriate Dapr sidecar instance.
## Self-hosted mode
The Scheduler service Docker container is started automatically as part of `dapr init`. It can also be run manually as a process if you are running in [slim-init mode]({{< ref self-hosted-no-docker.md >}}).
## Kubernetes mode
The Scheduler service is deployed as part of `dapr init -k`, or via the Dapr Helm charts. For more information on running Dapr on Kubernetes, visit the [Kubernetes hosting page]({{< ref kubernetes >}}).
The Scheduler service is deployed as part of `dapr init -k`, or via the Dapr Helm charts. When running in Kubernetes mode, the Scheduler service is configured to run with exactly 3 replicas to ensure data integrity.
You can run Scheduler in high availability (HA) mode. [Learn more about setting HA mode in your Kubernetes service.]({{< ref "kubernetes-production.md#individual-service-ha-helm-configuration" >}})
When a Kubernetes namespace is deleted, all the Job and Actor Reminders corresponding to that namespace are deleted.
## Back Up and Restore Scheduler Data
In production environments, it's recommended to perform periodic backups of this data at an interval that aligns with your recovery point objectives.
### Port Forward for Backup Operations
To perform backup and restore operations, you'll need to access the embedded etcd instance. This requires port forwarding to expose the etcd ports (port 2379).
#### Docker Compose Example
Here's how to expose the etcd ports in a Docker Compose configuration for standalone mode:
```yaml
scheduler-1:
image: "diagrid/dapr/scheduler:dev110-linux-arm64"
command: ["./scheduler",
"--etcd-data-dir", "/var/run/dapr/scheduler",
"--replica-count", "3",
"--id","scheduler-1",
"--initial-cluster", "scheduler-1=http://scheduler-1:2380,scheduler-0=http://scheduler-0:2380,scheduler-2=http://scheduler-2:2380",
"--etcd-client-ports", "scheduler-0=2379,scheduler-1=2379,scheduler-2=2379",
"--etcd-client-http-ports", "scheduler-0=2330,scheduler-1=2330,scheduler-2=2330",
"--log-level=debug"
]
ports:
- 2379:2379
volumes:
- ./dapr_scheduler/1:/var/run/dapr/scheduler
networks:
- network
```
When running in HA mode, you only need to expose the ports for one scheduler instance to perform backup operations.
### Performing Backup and Restore
Once you have access to the etcd ports, you can follow the [official etcd backup and restore documentation](https://etcd.io/docs/v3.5/op-guide/recovery/) to perform backup and restore operations. The process involves using standard etcd commands to create snapshots and restore from them.
## Disabling the Scheduler service
If you are not using any features that require the Scheduler service (Jobs API, Actor Reminders, or Workflows), you can disable it by setting `global.scheduler.enabled=false`.
For more information on running Dapr on Kubernetes, visit the [Kubernetes hosting page]({{< ref kubernetes >}}).
## Related links
[Learn more about the Jobs API.]({{< ref jobs_api.md >}})
[Learn more about the Jobs API.]({{< ref jobs_api.md >}})

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@ -27,11 +27,11 @@ Creating a new actor follows a local call like `http://localhost:3500/v1.0/actor
The Dapr runtime SDKs have language-specific actor frameworks. For example, the .NET SDK has C# actors. The goal is for all the Dapr language SDKs to have an actor framework. Currently .NET, Java, Go and Python SDK have actor frameworks.
### Does Dapr have any SDKs I can use if I want to work with a particular programming language or framework?
## Does Dapr have any SDKs I can use if I want to work with a particular programming language or framework?
To make using Dapr more natural for different languages, it includes [language specific SDKs]({{<ref sdks>}}) for Go, Java, JavaScript, .NET, Python, PHP, Rust and C++. These SDKs expose the functionality in the Dapr building blocks, such as saving state, publishing an event or creating an actor, through a typed language API rather than calling the http/gRPC API. This enables you to write a combination of stateless and stateful functions and actors all in the language of your choice. And because these SDKs share the Dapr runtime, you get cross-language actor and functions support.
### What frameworks does Dapr integrate with?
## What frameworks does Dapr integrate with?
Dapr can be integrated with any developer framework. For example, in the Dapr .NET SDK you can find ASP.NET Core integration, which brings stateful routing controllers that respond to pub/sub events from other services.
Dapr is integrated with the following frameworks;

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@ -46,7 +46,7 @@ Each of these building block APIs is independent, meaning that you can use any n
|----------------|-------------|
| [**Service-to-service invocation**]({{< ref "service-invocation-overview.md" >}}) | Resilient service-to-service invocation enables method calls, including retries, on remote services, wherever they are located in the supported hosting environment.
| [**Publish and subscribe**]({{< ref "pubsub-overview.md" >}}) | Publishing events and subscribing to topics between services enables event-driven architectures to simplify horizontal scalability and make them resilient to failure. Dapr provides at-least-once message delivery guarantee, message TTL, consumer groups and other advance features.
| [**Workflows**]({{< ref "workflow-overview.md" >}}) | The workflow API can be combined with other Dapr building blocks to define long running, persistent processes or data flows that span multiple microservices using Dapr workflows or workflow components.
| [**Workflows**]({{< ref "workflow-overview.md" >}}) | The workflow API can be combined with other Dapr building blocks to define long running, persistent processes or data flows that span multiple microservices using Dapr workflows.
| [**State management**]({{< ref "state-management-overview.md" >}}) | With state management for storing and querying key/value pairs, long-running, highly available, stateful services can be easily written alongside stateless services in your application. The state store is pluggable and examples include AWS DynamoDB, Azure Cosmos DB, Azure SQL Server, GCP Firebase, PostgreSQL or Redis, among others.
| [**Resource bindings**]({{< ref "bindings-overview.md" >}}) | Resource bindings with triggers builds further on event-driven architectures for scale and resiliency by receiving and sending events to and from any external source such as databases, queues, file systems, etc.
| [**Actors**]({{< ref "actors-overview.md" >}}) | A pattern for stateful and stateless objects that makes concurrency simple, with method and state encapsulation. Dapr provides many capabilities in its actor runtime, including concurrency, state, and life-cycle management for actor activation/deactivation, and timers and reminders to wake up actors.
@ -76,7 +76,7 @@ Dapr exposes its HTTP and gRPC APIs as a sidecar architecture, either as a conta
## Hosting environments
Dapr can be hosted in multiple environments, including:
- Self-hosted on a Windows/Linux/macOS machine for local development
- Self-hosted on a Windows/Linux/macOS machine for local development and in production
- On Kubernetes or clusters of physical or virtual machines in production
### Self-hosted local development

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@ -57,7 +57,7 @@ This simplifies some choices, but also carries some consideration:
## Actor communication
You can interact with Dapr to invoke the actor method by calling HTTP/gRPC endpoint.
You can interact with Dapr to invoke the actor method by calling the HTTP endpoint.
```bash
POST/GET/PUT/DELETE http://localhost:3500/v1.0/actors/<actorType>/<actorId>/<method/state/timers/reminders>

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@ -107,6 +107,10 @@ Refer [api spec]({{< ref "actors_api.md#invoke-timer" >}}) for more details.
## Actor reminders
{{% alert title="Note" color="primary" %}}
In Dapr v1.15, actor reminders are stored by default in the [Scheduler service]({{< ref "scheduler.md#actor-reminders" >}}). When upgrading to Dapr v1.15 all existing reminders are automatically migrated to the Scheduler service with no loss of reminders as a one time operation for each actor type.
{{% /alert %}}
Reminders are a mechanism to trigger *persistent* callbacks on an actor at specified times. Their functionality is similar to timers. But unlike timers, reminders are triggered under all circumstances until the actor explicitly unregisters them or the actor is explicitly deleted or the number in invocations is exhausted. Specifically, reminders are triggered across actor deactivations and failovers because the Dapr actor runtime persists the information about the actors' reminders using Dapr actor state provider.
You can create a persistent reminder for an actor by calling the HTTP/gRPC request to Dapr as shown below, or via Dapr SDK.
@ -133,6 +137,10 @@ You can remove the actor reminder by calling
DELETE http://localhost:3500/v1.0/actors/<actorType>/<actorId>/reminders/<name>
```
If an actor reminder is triggered and the app does not return a 2** code to the runtime (for example, because of a connection issue),
actor reminders will be retried up to three times with a backoff interval of one second between each attempt. There may be
additional retries attempted in accordance with any optionally applied [actor resiliency policy]({{< ref "override-default-retries.md" >}}).
Refer [api spec]({{< ref "actors_api.md#invoke-reminder" >}}) for more details.
## Error handling
@ -148,7 +156,9 @@ If an invocation of the method fails, the timer is not removed. Timers are only
## Reminder data serialization format
Actor reminder data is serialized to JSON by default. Dapr v1.13 onwards supports a protobuf serialization format for reminders data which, depending on throughput and size of the payload, can result in significant performance improvements, giving developers a higher throughput and lower latency. Another benefit is storing smaller data in the actor underlying database, which can result in cost optimizations when using some cloud databases. A restriction with using protobuf serialization is that the reminder data can no longer be queried.
Actor reminder data is serialized to JSON by default. Dapr v1.13 onwards supports a protobuf serialization format for internal reminders data for workflow via both the Placement and Scheduler services. Depending on throughput and size of the payload, this can result in significant performance improvements, giving developers a higher throughput and lower latency.
Another benefit is storing smaller data in the actor underlying database, which can result in cost optimizations when using some cloud databases. A restriction with using protobuf serialization is that the reminder data can no longer be queried.
{{% alert title="Note" color="primary" %}}
Protobuf serialization will become the default format in Dapr 1.14

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daprdocs/content/en/developing-applications/building-blocks/conversation/conversation-overview.md
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@ -10,18 +10,33 @@ description: "Overview of the conversation API building block"
The conversation API is currently in [alpha]({{< ref "certification-lifecycle.md#certification-levels" >}}).
{{% /alert %}}
Dapr's conversation API reduces the complexity of securely and reliably interacting with Large Language Models (LLM) at scale. Whether you're a developer who doesn't have the necessary native SDKs or a polyglot shop who just wants to focus on the prompt aspects of LLM interactions, the conversation API provides one consistent API entry point to talk to underlying LLM providers.
Using the Dapr conversation API, you can reduce the complexity of interacting with Large Language Models (LLMs) and enable critical performance and security functionality with features like prompt caching and personally identifiable information (PII) data obfuscation.
<img src="/images/conversation-overview.png" width=800 alt="Diagram showing the flow of a user's app communicating with Dapr's LLM components.">
In additon to enabling critical performance and security functionality (like [prompt caching]({{< ref "#prompt-caching" >}}) and [PII scrubbing]({{< ref "#personally-identifiable-information-pii-obfuscation" >}})), you can also pair the conversation API with Dapr functionalities, like:
- Resiliency circuit breakers and retries to circumvent limit and token errors, or
- Middleware to authenticate requests coming to and from the LLM
Dapr provides observability by issuing metrics for your LLM interactions.
## Features
The following features are out-of-the-box for [all the supported conversation components]({{< ref supported-conversation >}}).
### Prompt caching
To significantly reduce latency and cost, frequent prompts are stored in a cache to be reused, instead of reprocessing the information for every new request. Prompt caching optimizes performance by storing and reusing prompts that are often repeated across multiple API calls.
Prompt caching optimizes performance by storing and reusing prompts that are often repeated across multiple API calls. To significantly reduce latency and cost, Dapr stores frequent prompts in a local cache to be reused by your cluster, pod, or other, instead of reprocessing the information for every new request.
### Personally identifiable information (PII) obfuscation
The PII obfuscation feature identifies and removes any PII from a conversation response. This feature protects your privacy by eliminating sensitive details like names, addresses, phone numbers, or other details that could be used to identify an individual.
The PII obfuscation feature identifies and removes any form of sensitve user information from a conversation response. Simply enable PII obfuscation on input and output data to protect your privacy and scrub sensitive details that could be used to identify an individual.
## Demo
Watch the demo presented during [Diagrid's Dapr v1.15 celebration](https://www.diagrid.io/videos/dapr-1-15-deep-dive) to see how the conversation API works using the .NET SDK.
<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/NTnwoDhHIcQ?si=37SDcOHtEpgCIwkG&amp;start=5444" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
## Try out conversation
@ -31,7 +46,7 @@ Want to put the Dapr conversation API to the test? Walk through the following qu
| Quickstart/tutorial | Description |
| ------------------- | ----------- |
| [Conversation quickstart](todo) | . |
| [Conversation quickstart](todo) | TODO |
### Start using the conversation API directly in your app
@ -40,4 +55,4 @@ Want to skip the quickstarts? Not a problem. You can try out the conversation bu
## Next steps
- [How-To: Converse with an LLM using the conversation API]({{< ref howto-conversation-layer.md >}})
- [Conversation API components]({{< ref supported-conversation >}})
- [Conversation API components]({{< ref supported-conversation >}})

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daprdocs/content/en/developing-applications/building-blocks/conversation/howto-conversation-layer.md
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@ -14,6 +14,7 @@ Let's get started using the [conversation API]({{< ref conversation-overview.md
- Set up one of the available Dapr components (echo) that work with the conversation API.
- Add the conversation client to your application.
- Run the connection using `dapr run`.
## Set up the conversation component
@ -33,8 +34,31 @@ spec:
version: v1
```
### Use the OpenAI component
To interface with a real LLM, use one of the other [supported conversation components]({{< ref "supported-conversation" >}}), including OpenAI, Hugging Face, Anthropic, DeepSeek, and more.
For example, to swap out the `echo` mock component with an `OpenAI` component, replace the `conversation.yaml` file with the following. You'll need to copy your API key into the component file.
```
apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
name: openai
spec:
type: conversation.openai
metadata:
- name: key
value: <REPLACE_WITH_YOUR_KEY>
- name: model
value: gpt-4-turbo
- name: cacheTTL
value: 10m
```
## Connect the conversation client
The following examples use an HTTP client to send a POST request to Dapr's sidecar HTTP endpoint. You can also use [the Dapr SDK client instead]({{< ref "#related-links" >}}).
{{< tabs ".NET" "Go" "Rust" >}}
@ -42,8 +66,30 @@ spec:
<!-- .NET -->
{{% codetab %}}
```dotnet
todo
```csharp
using Dapr.AI.Conversation;
using Dapr.AI.Conversation.Extensions;
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddDaprConversationClient();
var app = builder.Build();
var conversationClient = app.Services.GetRequiredService<DaprConversationClient>();
var response = await conversationClient.ConverseAsync("conversation",
new List<DaprConversationInput>
{
new DaprConversationInput(
"Please write a witty haiku about the Dapr distributed programming framework at dapr.io",
DaprConversationRole.Generic)
});
Console.WriteLine("Received the following from the LLM:");
foreach (var resp in response.Outputs)
{
Console.WriteLine($"\t{resp.Result}");
}
```
{{% /codetab %}}
@ -68,7 +114,7 @@ func main() {
}
input := dapr.ConversationInput{
Message: "hello world",
Message: "Please write a witty haiku about the Dapr distributed programming framework at dapr.io",
// Role: nil, // Optional
// ScrubPII: nil, // Optional
}
@ -110,7 +156,7 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut client = DaprClient::connect(address).await?;
let input = ConversationInputBuilder::new("hello world").build();
let input = ConversationInputBuilder::new("Please write a witty haiku about the Dapr distributed programming framework at dapr.io").build();
let conversation_component = "echo";
@ -130,6 +176,84 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
{{< /tabs >}}
## Run the conversation connection
Start the connection using the `dapr run` command. For example, for this scenario, we're running `dapr run` on an application with the app ID `conversation` and pointing to our conversation YAML file in the `./config` directory.
{{< tabs ".NET" "Go" "Rust" >}}
<!-- .NET -->
{{% codetab %}}
```bash
dapr run --app-id conversation --dapr-grpc-port 50001 --log-level debug --resources-path ./config -- dotnet run
```
{{% /codetab %}}
<!-- Go -->
{{% codetab %}}
```bash
dapr run --app-id conversation --dapr-grpc-port 50001 --log-level debug --resources-path ./config -- go run ./main.go
```
**Expected output**
```
- '== APP == conversation output: Please write a witty haiku about the Dapr distributed programming framework at dapr.io'
```
{{% /codetab %}}
<!-- Rust -->
{{% codetab %}}
```bash
dapr run --app-id=conversation --resources-path ./config --dapr-grpc-port 3500 -- cargo run --example conversation
```
**Expected output**
```
- 'conversation input: hello world'
- 'conversation output: hello world'
```
{{% /codetab %}}
{{< /tabs >}}
## Related links
Try out the conversation API using the full examples provided in the supported SDK repos.
{{< tabs ".NET" "Go" "Rust" >}}
<!-- .NET -->
{{% codetab %}}
[Dapr conversation example with the .NET SDK](https://github.com/dapr/dotnet-sdk/tree/master/examples/AI/ConversationalAI)
{{% /codetab %}}
<!-- Go -->
{{% codetab %}}
[Dapr conversation example with the Go SDK](https://github.com/dapr/go-sdk/tree/main/examples/conversation)
{{% /codetab %}}
<!-- Rust -->
{{% codetab %}}
[Dapr conversation example with the Rust SDK](https://github.com/dapr/rust-sdk/tree/main/examples/src/conversation)
{{% /codetab %}}
{{< /tabs >}}
## Next steps

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@ -2,7 +2,7 @@
type: docs
title: "How-To: Schedule and handle triggered jobs"
linkTitle: "How-To: Schedule and handle triggered jobs"
weight: 2000
weight: 5000
description: "Learn how to use the jobs API to schedule and handle triggered jobs"
---
@ -20,7 +20,103 @@ When you [run `dapr init` in either self-hosted mode or on Kubernetes]({{< ref i
In your code, set up and schedule jobs within your application.
{{< tabs "Go" >}}
{{< tabs ".NET" "Go" >}}
{{% codetab %}}
<!-- .NET -->
The following .NET SDK code sample schedules the job named `prod-db-backup`. The job data contains information
about the database that you'll be seeking to backup regularly. Over the course of this example, you'll:
- Define types used in the rest of the example
- Register an endpoint during application startup that handles all job trigger invocations on the service
- Register the job with Dapr
In the following example, you'll create records that you'll serialize and register alongside the job so the information
is available when the job is triggered in the future:
- The name of the backup task (`db-backup`)
- The backup task's `Metadata`, including:
- The database name (`DBName`)
- The database location (`BackupLocation`)
Create an ASP.NET Core project and add the latest version of `Dapr.Jobs` from NuGet.
> **Note:** While it's not strictly necessary
for your project to use the `Microsoft.NET.Sdk.Web` SDK to create jobs, as of the time this documentation is authored,
only the service that schedules a job receives trigger invocations for it. As those invocations expect an endpoint
that can handle the job trigger and requires the `Microsoft.NET.Sdk.Web` SDK, it's recommended that you
use an ASP.NET Core project for this purpose.
Start by defining types to persist our backup job data and apply our own JSON property name attributes to the properties
so they're consistent with other language examples.
```cs
//Define the types that we'll represent the job data with
internal sealed record BackupJobData([property: JsonPropertyName("task")] string Task, [property: JsonPropertyName("metadata")] BackupMetadata Metadata);
internal sealed record BackupMetadata([property: JsonPropertyName("DBName")]string DatabaseName, [property: JsonPropertyName("BackupLocation")] string BackupLocation);
```
Next, set up a handler as part of your application setup that will be called anytime a job is triggered on your
application. It's the responsibility of this handler to identify how jobs should be processed based on the job name provided.
This works by registering a handler with ASP.NET Core at `/job/<job-name>`, where `<job-name>` is parameterized and
passed into this handler delegate, meeting Dapr's expectation that an endpoint is available to handle triggered named jobs.
Populate your `Program.cs` file with the following:
```cs
using System.Text;
using System.Text.Json;
using Dapr.Jobs;
using Dapr.Jobs.Extensions;
using Dapr.Jobs.Models;
using Dapr.Jobs.Models.Responses;
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddDaprJobsClient();
var app = builder.Build();
//Registers an endpoint to receive and process triggered jobs
var cancellationTokenSource = new CancellationTokenSource(TimeSpan.FromSeconds(5));
app.MapDaprScheduledJobHandler((string jobName, ReadOnlyMemory<byte> jobPayload, ILogger logger, CancellationToken cancellationToken) => {
logger?.LogInformation("Received trigger invocation for job '{jobName}'", jobName);
switch (jobName)
{
case "prod-db-backup":
// Deserialize the job payload metadata
var jobData = JsonSerializer.Deserialize<BackupJobData>(jobPayload);
// Process the backup operation - we assume this is implemented elsewhere in your code
await BackupDatabaseAsync(jobData, cancellationToken);
break;
}
}, cancellationTokenSource.Token);
await app.RunAsync();
```
Finally, the job itself needs to be registered with Dapr so it can be triggered at a later point in time. You can do this
by injecting a `DaprJobsClient` into a class and executing as part of an inbound operation to your application, but for
this example's purposes, it'll go at the bottom of the `Program.cs` file you started above. Because you'll be using the
`DaprJobsClient` you registered with dependency injection, start by creating a scope so you can access it.
```cs
//Create a scope so we can access the registered DaprJobsClient
await using scope = app.Services.CreateAsyncScope();
var daprJobsClient = scope.ServiceProvider.GetRequiredService<DaprJobsClient>();
//Create the payload we wish to present alongside our future job triggers
var jobData = new BackupJobData("db-backup", new BackupMetadata("my-prod-db", "/backup-dir"));
//Serialize our payload to UTF-8 bytes
var serializedJobData = JsonSerializer.SerializeToUtf8Bytes(jobData);
//Schedule our backup job to run every minute, but only repeat 10 times
await daprJobsClient.ScheduleJobAsync("prod-db-backup", DaprJobSchedule.FromDuration(TimeSpan.FromMinutes(1)),
serializedJobData, repeats: 10);
```
{{% /codetab %}}
{{% codetab %}}
@ -92,66 +188,8 @@ In this example, at trigger time, which is `@every 1s` according to the `Schedul
}
```
At the trigger time, the `prodDBBackupHandler` function is called, executing the desired business logic for this job at trigger time. For example:
#### HTTP
When you create a job using Dapr's Jobs API, Dapr will automatically assume there is an endpoint available at
`/job/<job-name>`. For instance, if you schedule a job named `test`, Dapr expects your application to listen for job
events at `/job/test`. Ensure your application has a handler set up for this endpoint to process the job when it is
triggered. For example:
*Note: The following example is in Go but applies to any programming language.*
```go
func main() {
...
http.HandleFunc("/job/", handleJob)
http.HandleFunc("/job/<job-name>", specificJob)
...
}
func specificJob(w http.ResponseWriter, r *http.Request) {
// Handle specific triggered job
}
func handleJob(w http.ResponseWriter, r *http.Request) {
// Handle the triggered jobs
}
```
#### gRPC
When a job reaches its scheduled trigger time, the triggered job is sent back to the application via the following
callback function:
*Note: The following example is in Go but applies to any programming language with gRPC support.*
```go
import rtv1 "github.com/dapr/dapr/pkg/proto/runtime/v1"
...
func (s *JobService) OnJobEventAlpha1(ctx context.Context, in *rtv1.JobEventRequest) (*rtv1.JobEventResponse, error) {
// Handle the triggered job
}
```
This function processes the triggered jobs within the context of your gRPC server. When you set up the server, ensure that
you register the callback server, which will invoke this function when a job is triggered:
```go
...
js := &JobService{}
rtv1.RegisterAppCallbackAlphaServer(server, js)
```
In this setup, you have full control over how triggered jobs are received and processed, as they are routed directly
through this gRPC method.
#### SDKs
For SDK users, handling triggered jobs is simpler. When a job is triggered, Dapr will automatically route the job to the
event handler you set up during the server initialization. For example, in Go, you'd register the event handler like this:
When a job is triggered, Dapr will automatically route the job to the event handler you set up during the server
initialization. For example, in Go, you'd register the event handler like this:
```go
...

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@ -0,0 +1,121 @@
---
type: docs
title: "Features and concepts"
linkTitle: "Features and concepts"
weight: 2000
description: "Learn more about the Dapr Jobs features and concepts"
---
Now that you've learned about the [jobs building block]({{< ref jobs-overview.md >}}) at a high level, let's deep dive
into the features and concepts included with Dapr Jobs and the various SDKs. Dapr Jobs:
- Provides a robust and scalable API for scheduling operations to be triggered in the future.
- Exposes several capabilities which are common across all supported languages.
## Job identity
All jobs are registered with a case-sensitive job name. These names are intended to be unique across all services
interfacing with the Dapr runtime. The name is used as an identifier when creating and modifying the job as well as
to indicate which job a triggered invocation is associated with.
Only one job can be associated with a name at any given time. Any attempt to create a new job using the same name
as an existing job will result in an overwrite of this existing job.
## Scheduling Jobs
A job can be scheduled using any of the following mechanisms:
- Intervals using Cron expressions, duration values, or period expressions
- Specific dates and times
For all time-based schedules, if a timestamp is provided with a time zone via the RFC3339 specification, that
time zone is used. When not provided, the time zone used by the server running Dapr is used.
In other words, do **not** assume that times run in UTC time zone, unless otherwise specified when scheduling
the job.
### Schedule using a Cron expression
When scheduling a job to execute on a specific interval using a Cron expression, the expression is written using 6
fields spanning the values specified in the table below:
| seconds | minutes | hours | day of month | month | day of week |
| -- | -- | -- | -- | -- | -- |
| 0-59 | 0-59 | 0-23 | 1-31 | 1-12/jan-dec | 0-6/sun-sat |
#### Example 1
`"0 30 * * * *"` triggers every hour on the half-hour mark.
#### Example 2
`"0 15 3 * * *"` triggers every day at 03:15.
### Schedule using a duration value
You can schedule jobs using [a Go duration string](https://pkg.go.dev/time#ParseDuration), in which
a string consists of a (possibly) signed sequence of decimal numbers, each with an optional fraction and a unit suffix.
Valid time units are `"ns"`, `"us"`, `"ms"`, `"s"`, `"m"`, or `"h"`.
#### Example 1
`"2h45m"` triggers every 2 hours and 45 minutes.
#### Example 2
`"37m25s"` triggers every 37 minutes and 25 seconds.
### Schedule using a period expression
The following period expressions are supported. The "@every" expression also accepts a [Go duration string](https://pkg.go.dev/time#ParseDuration).
| Entry | Description | Equivalent Cron expression |
| -- | -- | -- |
| @every | Run every (e.g. "@every 1h30m") | N/A |
| @yearly (or @annually) | Run once a year, midnight, January 1st | 0 0 0 1 1 * |
| @monthly | Run once a month, midnight, first of month | 0 0 0 1 * * |
| @weekly | Run once a week, midnight on Sunday | 0 0 0 * * 0 |
| @daily or @midnight | Run once a day at midnight | 0 0 0 * * * |
| @hourly | Run once an hour at the beginning of the hour | 0 0 * * * * |
### Schedule using a specific date/time
A job can also be scheduled to run at a particular point in time by providing a date using the
[RFC3339 specification](https://www.rfc-editor.org/rfc/rfc3339).
#### Example 1
`"2025-12-09T16:09:53+00:00"` Indicates that the job should be run on December 9, 2025 at 4:09:53 PM UTC.
## Scheduled triggers
When a scheduled Dapr job is triggered, the runtime sends a message back to the service that scheduled the job using
either the HTTP or gRPC approach, depending on which is registered with Dapr when the service starts.
### gRPC
When a job reaches its scheduled trigger time, the triggered job is sent back to the application via the following
callback function:
> **Note:** The following example is in Go, but applies to any programming language with gRPC support.
```go
import rtv1 "github.com/dapr/dapr/pkg/proto/runtime/v1"
...
func (s *JobService) OnJobEventAlpha1(ctx context.Context, in *rtv1.JobEventRequest) (*rtv1.JobEventResponse, error) {
// Handle the triggered job
}
```
This function processes the triggered jobs within the context of your gRPC server. When you set up the server, ensure that
you register the callback server, which invokes this function when a job is triggered:
```go
...
js := &JobService{}
rtv1.RegisterAppCallbackAlphaServer(server, js)
```
In this setup, you have full control over how triggered jobs are received and processed, as they are routed directly
through this gRPC method.
### HTTP
If a gRPC server isn't registered with Dapr when the application starts up, Dapr instead triggers jobs by making a
POST request to the endpoint `/job/<job-name>`. The body includes the following information about the job:
- `Schedule`: When the job triggers occur
- `RepeatCount`: An optional value indicating how often the job should repeat
- `DueTime`: An optional point in time representing either the one time when the job should execute (if not recurring)
or the not-before time from which the schedule should take effect
- `Ttl`: An optional value indicating when the job should expire
- `Payload`: A collection of bytes containing data originally stored when the job was scheduled
The `DueTime` and `Ttl` fields will reflect an RC3339 timestamp value reflective of the time zone provided when the job was
originally scheduled. If no time zone was provided, these values indicate the time zone used by the server running
Dapr.

8
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@ -8,7 +8,7 @@ description: "Overview of the jobs API building block"
Many applications require job scheduling, or the need to take an action in the future. The jobs API is an orchestrator for scheduling these future jobs, either at a specific time or for a specific interval.
Not only does the jobs API help you with scheduling jobs, but internally, Dapr uses the scheduler service to schedule actor reminders.
Not only does the jobs API help you with scheduling jobs, but internally, Dapr uses the Scheduler service to schedule actor reminders.
Jobs in Dapr consist of:
- [The jobs API building block]({{< ref jobs_api.md >}})
@ -57,11 +57,9 @@ The jobs API provides several features to make it easy for you to schedule jobs.
### Schedule jobs across multiple replicas
The Scheduler service enables the scheduling of jobs to scale across multiple replicas, while guaranteeing that a job is only triggered by 1 scheduler service instance.
When you create a job, it replaces any existing job with the same name. This means that every time a job is created, it resets the count and only keeps 1 record in the embedded etcd for that job. Therefore, you don't need to worry about multiple jobs being created and firing off — only the most recent job is recorded and executed, even if all your apps schedule the same job on startup.
### Actor reminders
Actors have actor reminders, but present some limitations involving scalability using the Placement service implementation. You can make reminders more scalable by using [`SchedulerReminders`]({{< ref support-preview-features.md >}}). This is set in the configuration for your actor application.
The Scheduler service enables the scheduling of jobs to scale across multiple replicas, while guaranteeing that a job is only triggered by 1 Scheduler service instance.
## Try out the jobs API

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@ -70,7 +70,7 @@ app.get('/dapr/subscribe', (_req, res) => {
## Retries and dead letter topics
By default, when a dead letter topic is set, any failing message immediately goes to the dead letter topic. As a result it is recommend to always have a retry policy set when using dead letter topics in a subscription.
To enable the retry of a message before sending it to the dead letter topic, apply a [retry resiliency policy]({{< ref "policies.md#retries" >}}) to the pub/sub component.
To enable the retry of a message before sending it to the dead letter topic, apply a [retry resiliency policy]({{< ref "retries-overview.md" >}}) to the pub/sub component.
This example shows how to set a constant retry policy named `pubsubRetry`, with 10 maximum delivery attempts applied every 5 seconds for the `pubsub` pub/sub component.

91
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@ -20,7 +20,7 @@ Not using CloudEvents disables support for tracing, event deduplication per mess
To disable CloudEvent wrapping, set the `rawPayload` metadata to `true` as part of the publishing request. This allows subscribers to receive these messages without having to parse the CloudEvent schema.
{{< tabs curl "Python SDK" "PHP SDK">}}
{{< tabs curl ".NET" "Python" "PHP">}}
{{% codetab %}}
```bash
@ -28,6 +28,43 @@ curl -X "POST" http://localhost:3500/v1.0/publish/pubsub/TOPIC_A?metadata.rawPay
```
{{% /codetab %}}
{{% codetab %}}
```csharp
using Dapr.Client;
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddControllers().AddDapr();
var app = builder.Build();
app.MapPost("/publish", async (DaprClient daprClient) =>
{
var message = new Message(
Guid.NewGuid().ToString(),
$"Hello at {DateTime.UtcNow}",
DateTime.UtcNow
);
await daprClient.PublishEventAsync(
"pubsub", // pubsub name
"messages", // topic name
message, // message data
new Dictionary<string, string>
{
{ "rawPayload", "true" },
{ "content-type", "application/json" }
}
);
return Results.Ok(message);
});
app.Run();
```
{{% /codetab %}}
{{% codetab %}}
```python
from dapr.clients import DaprClient
@ -74,9 +111,52 @@ Dapr apps are also able to subscribe to raw events coming from existing pub/sub
### Programmatically subscribe to raw events
When subscribing programmatically, add the additional metadata entry for `rawPayload` so the Dapr sidecar automatically wraps the payloads into a CloudEvent that is compatible with current Dapr SDKs.
When subscribing programmatically, add the additional metadata entry for `rawPayload` to allow the subscriber to receive a message that is not wrapped by a CloudEvent. For .NET, this metadata entry is called `isRawPayload`.
{{< tabs "Python" "PHP SDK" >}}
{{< tabs ".NET" "Python" "PHP" >}}
{{% codetab %}}
```csharp
using System.Text.Json;
using System.Text.Json.Serialization;
var builder = WebApplication.CreateBuilder(args);
var app = builder.Build();
app.MapGet("/dapr/subscribe", () =>
{
var subscriptions = new[]
{
new
{
pubsubname = "pubsub",
topic = "messages",
route = "/messages",
metadata = new Dictionary<string, string>
{
{ "isRawPayload", "true" },
{ "content-type", "application/json" }
}
}
};
return Results.Ok(subscriptions);
});
app.MapPost("/messages", async (HttpContext context) =>
{
using var reader = new StreamReader(context.Request.Body);
var json = await reader.ReadToEndAsync();
Console.WriteLine($"Raw message received: {json}");
return Results.Ok();
});
app.Run();
```
{{% /codetab %}}
{{% codetab %}}
@ -151,7 +231,7 @@ spec:
default: /dsstatus
pubsubname: pubsub
metadata:
rawPayload: "true"
isRawPayload: "true"
scopes:
- app1
- app2
@ -161,4 +241,5 @@ scopes:
- Learn more about [publishing and subscribing messages]({{< ref pubsub-overview.md >}})
- List of [pub/sub components]({{< ref supported-pubsub >}})
- Read the [API reference]({{< ref pubsub_api.md >}})
- Read the [API reference]({{< ref pubsub_api.md >}})
- Read the .NET sample on how to [consume Kafka messages without CloudEvents](https://github.com/dapr/samples/pubsub-raw-payload)

107
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@ -203,7 +203,112 @@ As messages are sent to the given message handler code, there is no concept of r
The example below shows the different ways to stream subscribe to a topic.
{{< tabs Go>}}
{{< tabs Python Go >}}
{{% codetab %}}
You can use the `subscribe` method, which returns a `Subscription` object and allows you to pull messages from the stream by calling the `next_message` method. This runs in and may block the main thread while waiting for messages.
```python
import time
from dapr.clients import DaprClient
from dapr.clients.grpc.subscription import StreamInactiveError
counter = 0
def process_message(message):
global counter
counter += 1
# Process the message here
print(f'Processing message: {message.data()} from {message.topic()}...')
return 'success'
def main():
with DaprClient() as client:
global counter
subscription = client.subscribe(
pubsub_name='pubsub', topic='orders', dead_letter_topic='orders_dead'
)
try:
while counter < 5:
try:
message = subscription.next_message()
except StreamInactiveError as e:
print('Stream is inactive. Retrying...')
time.sleep(1)
continue
if message is None:
print('No message received within timeout period.')
continue
# Process the message
response_status = process_message(message)
if response_status == 'success':
subscription.respond_success(message)
elif response_status == 'retry':
subscription.respond_retry(message)
elif response_status == 'drop':
subscription.respond_drop(message)
finally:
print("Closing subscription...")
subscription.close()
if __name__ == '__main__':
main()
```
You can also use the `subscribe_with_handler` method, which accepts a callback function executed for each message received from the stream. This runs in a separate thread, so it doesn't block the main thread.
```python
import time
from dapr.clients import DaprClient
from dapr.clients.grpc._response import TopicEventResponse
counter = 0
def process_message(message):
# Process the message here
global counter
counter += 1
print(f'Processing message: {message.data()} from {message.topic()}...')
return TopicEventResponse('success')
def main():
with (DaprClient() as client):
# This will start a new thread that will listen for messages
# and process them in the `process_message` function
close_fn = client.subscribe_with_handler(
pubsub_name='pubsub', topic='orders', handler_fn=process_message,
dead_letter_topic='orders_dead'
)
while counter < 5:
time.sleep(1)
print("Closing subscription...")
close_fn()
if __name__ == '__main__':
main()
```
[Learn more about streaming subscriptions using the Python SDK client.]({{< ref "python-client.md#streaming-message-subscription" >}})
{{% /codetab %}}
{{% codetab %}}

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@ -309,6 +309,8 @@ context.AddMetadata("dapr-stream", "true");
### Streaming gRPCs and Resiliency
> Currently, resiliency policies are not supported for service invocation via gRPC.
When proxying streaming gRPCs, due to their long-lived nature, [resiliency]({{< ref "resiliency-overview.md" >}}) policies are applied on the "initial handshake" only. As a consequence:
- If the stream is interrupted after the initial handshake, it will not be automatically re-established by Dapr. Your application will be notified that the stream has ended, and will need to recreate it.

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@ -6,7 +6,9 @@ weight: 4000
description: "The Dapr Workflow engine architecture"
---
[Dapr Workflows]({{< ref "workflow-overview.md" >}}) 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. This article describes:
[Dapr Workflows]({{< ref "workflow-overview.md" >}}) 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
@ -72,7 +74,7 @@ The internal workflow actor types are only registered after an app has registere
### Workflow 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 created. The ID of the workflow actor is the ID of the workflow. This internal actor stores the state of the workflow as it progresses and determines the node on which the workflow code executes via the actor placement service.
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 created. The ID of the workflow actor is the ID of the workflow. This internal actor stores the state of the workflow as it progresses and determines the node on which the workflow code executes via the actor placement service.
Each workflow actor saves its state using the following keys in the configured state store:
@ -84,7 +86,7 @@ Each workflow actor saves its state using the following keys in the configured s
| `metadata` | Contains meta information about the workflow as a JSON blob and includes details such as the length of the inbox, the length of the history, and a 64-bit integer representing the workflow generation (for cases where the instance ID gets reused). The length information is used to determine which keys need to be read or written to when loading or saving workflow state updates. |
{{% alert title="Warning" color="warning" %}}
In the [Alpha release of the Dapr Workflow engine]({{< ref support-preview-features.md >}}), workflow actor state will remain in the state store even after a workflow has completed. Creating a large number of workflows could result in unbounded storage usage. In a future release, data retention policies will be introduced that can automatically purge the state store of old workflow state.
Workflow actor state remains in the state store even after a workflow has completed. Creating a large number of workflows could result in unbounded storage usage. To address this either purge workflows using their ID or directly delete entries in the workflow DB store.
{{% /alert %}}
The following diagram illustrates the typical lifecycle of a workflow actor.
@ -122,7 +124,7 @@ Activity actors are short-lived:
### Reminder usage and execution guarantees
The Dapr Workflow ensures workflow fault-tolerance by using [actor reminders]({{< ref "howto-actors.md#actor-timers-and-reminders" >}}) to recover from transient system failures. Prior to invoking application workflow code, the workflow or activity actor will create a new reminder. If the application code executes without interruption, the reminder is deleted. 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.
The Dapr Workflow ensures workflow fault-tolerance by using [actor reminders]({{< ref "../actors/actors-timers-reminders.md##actor-reminders" >}}) to recover from transient system failures. Prior to invoking application workflow code, the workflow or activity actor will create a new reminder. If the application code executes without interruption, the reminder is deleted. 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.
<img src="/images/workflow-overview/workflow-actor-reminder-flow.png" width=600 alt="Diagram showing the process of invoking workflow actors"/>

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@ -195,7 +195,7 @@ string randomString = GetRandomString();
// DON'T DO THIS!
Instant currentTime = Instant.now();
UUID newIdentifier = UUID.randomUUID();
string randomString = GetRandomString();
String randomString = getRandomString();
```
{{% /codetab %}}
@ -242,7 +242,7 @@ string randomString = await context.CallActivityAsync<string>("GetRandomString")
```java
// Do this!!
Instant currentTime = context.getCurrentInstant();
Guid newIdentifier = context.NewGuid();
Guid newIdentifier = context.newGuid();
String randomString = context.callActivity(GetRandomString.class.getName(), String.class).await();
```
@ -338,7 +338,7 @@ Do this:
```csharp
// Do this!!
string configuation = workflowInput.Configuration; // imaginary workflow input argument
string configuration = workflowInput.Configuration; // imaginary workflow input argument
string data = await context.CallActivityAsync<string>("MakeHttpCall", "https://example.com/api/data");
```
@ -348,7 +348,7 @@ string data = await context.CallActivityAsync<string>("MakeHttpCall", "https://e
```java
// Do this!!
String configuation = ctx.getInput(InputType.class).getConfiguration(); // imaginary workflow input argument
String configuration = ctx.getInput(InputType.class).getConfiguration(); // imaginary workflow input argument
String data = ctx.callActivity(MakeHttpCall.class, "https://example.com/api/data", String.class).await();
```
@ -358,7 +358,7 @@ String data = ctx.callActivity(MakeHttpCall.class, "https://example.com/api/data
```javascript
// Do this!!
const configuation = workflowInput.getConfiguration(); // imaginary workflow input argument
const configuration = workflowInput.getConfiguration(); // imaginary workflow input argument
const data = yield ctx.callActivity(makeHttpCall, "https://example.com/api/data");
```

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@ -2,6 +2,6 @@
type: docs
title: "Debugging Dapr applications and the Dapr control plane"
linkTitle: "Debugging"
weight: 50
weight: 60
description: "Guides on how to debug Dapr applications and the Dapr control plane"
---

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@ -2,6 +2,6 @@
type: docs
title: "Components"
linkTitle: "Components"
weight: 30
weight: 40
description: "Learn more about developing Dapr's pluggable and middleware components"
---

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@ -0,0 +1,8 @@
---
type: docs
title: "Error codes"
linkTitle: "Error codes"
weight: 20
description: "Error codes and messages you may encounter while using Dapr"
---

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@ -0,0 +1,206 @@
---
type: docs
title: "Error codes reference guide"
linkTitle: "Reference"
description: "List of gRPC and HTTP error codes in Dapr and their descriptions"
weight: 20
---
The following tables list the error codes returned by Dapr runtime.
The error codes are returned in the response body of an HTTP request or in the `ErrorInfo` section of a gRPC status response, if one is present.
An effort is underway to enrich all gRPC error responses according to the [Richer Error Model]({{< ref "grpc-error-codes.md#richer-grpc-error-model" >}}). Error codes without a corresponding gRPC code indicate those errors have not yet been updated to this model.
### Actors API
| HTTP Code | gRPC Code | Description |
| ---------------------------------- | --------- | ----------------------------------------------------------------------- |
| `ERR_ACTOR_INSTANCE_MISSING` | | Missing actor instance |
| `ERR_ACTOR_INVOKE_METHOD` | | Error invoking actor method |
| `ERR_ACTOR_RUNTIME_NOT_FOUND` | | Actor runtime not found |
| `ERR_ACTOR_STATE_GET` | | Error getting actor state |
| `ERR_ACTOR_STATE_TRANSACTION_SAVE` | | Error saving actor transaction |
| `ERR_ACTOR_REMINDER_CREATE` | | Error creating actor reminder |
| `ERR_ACTOR_REMINDER_DELETE` | | Error deleting actor reminder |
| `ERR_ACTOR_REMINDER_GET` | | Error getting actor reminder |
| `ERR_ACTOR_REMINDER_NON_HOSTED` | | Reminder operation on non-hosted actor type |
| `ERR_ACTOR_TIMER_CREATE` | | Error creating actor timer |
| `ERR_ACTOR_NO_APP_CHANNEL` | | App channel not initialized |
| `ERR_ACTOR_STACK_DEPTH` | | Maximum actor call stack depth exceeded |
| `ERR_ACTOR_NO_PLACEMENT` | | Placement service not configured |
| `ERR_ACTOR_RUNTIME_CLOSED` | | Actor runtime is closed |
| `ERR_ACTOR_NAMESPACE_REQUIRED` | | Actors must have a namespace configured when running in Kubernetes mode |
| `ERR_ACTOR_NO_ADDRESS` | | No address found for actor |
### Workflows API
| HTTP Code | gRPC Code | Description |
| ---------------------------------- | --------- | --------------------------------------------------------------------------------------- |
| `ERR_GET_WORKFLOW` | | Error getting workflow |
| `ERR_START_WORKFLOW` | | Error starting workflow |
| `ERR_PAUSE_WORKFLOW` | | Error pausing workflow |
| `ERR_RESUME_WORKFLOW` | | Error resuming workflow |
| `ERR_TERMINATE_WORKFLOW` | | Error terminating workflow |
| `ERR_PURGE_WORKFLOW` | | Error purging workflow |
| `ERR_RAISE_EVENT_WORKFLOW` | | Error raising event in workflow |
| `ERR_WORKFLOW_COMPONENT_MISSING` | | Missing workflow component |
| `ERR_WORKFLOW_COMPONENT_NOT_FOUND` | | Workflow component not found |
| `ERR_WORKFLOW_EVENT_NAME_MISSING` | | Missing workflow event name |
| `ERR_WORKFLOW_NAME_MISSING` | | Workflow name not configured |
| `ERR_INSTANCE_ID_INVALID` | | Invalid workflow instance ID. (Only alphanumeric and underscore characters are allowed) |
| `ERR_INSTANCE_ID_NOT_FOUND` | | Workflow instance ID not found |
| `ERR_INSTANCE_ID_PROVIDED_MISSING` | | Missing workflow instance ID |
| `ERR_INSTANCE_ID_TOO_LONG` | | Workflow instance ID too long |
### State management API
| HTTP Code | gRPC Code | Description |
| --------------------------------------- | --------------------------------------- | ----------------------------------------- |
| `ERR_STATE_TRANSACTION` | | Error in state transaction |
| `ERR_STATE_SAVE` | | Error saving state |
| `ERR_STATE_GET` | | Error getting state |
| `ERR_STATE_DELETE` | | Error deleting state |
| `ERR_STATE_BULK_DELETE` | | Error deleting state in bulk |
| `ERR_STATE_BULK_GET` | | Error getting state in bulk |
| `ERR_NOT_SUPPORTED_STATE_OPERATION` | | Operation not supported in transaction |
| `ERR_STATE_QUERY` | `DAPR_STATE_QUERY_FAILED` | Error querying state |
| `ERR_STATE_STORE_NOT_FOUND` | `DAPR_STATE_NOT_FOUND` | State store not found |
| `ERR_STATE_STORE_NOT_CONFIGURED` | `DAPR_STATE_NOT_CONFIGURED` | State store not configured |
| `ERR_STATE_STORE_NOT_SUPPORTED` | `DAPR_STATE_TRANSACTIONS_NOT_SUPPORTED` | State store does not support transactions |
| `ERR_STATE_STORE_NOT_SUPPORTED` | `DAPR_STATE_QUERYING_NOT_SUPPORTED` | State store does not support querying |
| `ERR_STATE_STORE_TOO_MANY_TRANSACTIONS` | `DAPR_STATE_TOO_MANY_TRANSACTIONS` | Too many operations per transaction |
| `ERR_MALFORMED_REQUEST` | `DAPR_STATE_ILLEGAL_KEY` | Invalid key |
### Configuration API
| HTTP Code | gRPC Code | Description |
| ---------------------------------------- | --------- | -------------------------------------- |
| `ERR_CONFIGURATION_GET` | | Error getting configuration |
| `ERR_CONFIGURATION_STORE_NOT_CONFIGURED` | | Configuration store not configured |
| `ERR_CONFIGURATION_STORE_NOT_FOUND` | | Configuration store not found |
| `ERR_CONFIGURATION_SUBSCRIBE` | | Error subscribing to configuration |
| `ERR_CONFIGURATION_UNSUBSCRIBE` | | Error unsubscribing from configuration |
### Crypto API
| HTTP Code | gRPC Code | Description |
| ------------------------------------- | --------- | ------------------------------- |
| `ERR_CRYPTO` | | Error in crypto operation |
| `ERR_CRYPTO_KEY` | | Error retrieving crypto key |
| `ERR_CRYPTO_PROVIDER_NOT_FOUND` | | Crypto provider not found |
| `ERR_CRYPTO_PROVIDERS_NOT_CONFIGURED` | | Crypto providers not configured |
### Secrets API
| HTTP Code | gRPC Code | Description |
| ---------------------------------- | --------- | --------------------------- |
| `ERR_SECRET_GET` | | Error getting secret |
| `ERR_SECRET_STORE_NOT_FOUND` | | Secret store not found |
| `ERR_SECRET_STORES_NOT_CONFIGURED` | | Secret store not configured |
| `ERR_PERMISSION_DENIED` | | Permission denied by policy |
### Pub/Sub and messaging errors
| HTTP Code | gRPC Code | Description |
| ----------------------------- | -------------------------------------- | -------------------------------------- |
| `ERR_PUBSUB_EMPTY` | `DAPR_PUBSUB_NAME_EMPTY` | Pubsub name is empty |
| `ERR_PUBSUB_NOT_FOUND` | `DAPR_PUBSUB_NOT_FOUND` | Pubsub not found |
| `ERR_PUBSUB_NOT_FOUND` | `DAPR_PUBSUB_TEST_NOT_FOUND` | Pubsub not found |
| `ERR_PUBSUB_NOT_CONFIGURED` | `DAPR_PUBSUB_NOT_CONFIGURED` | Pubsub not configured |
| `ERR_TOPIC_NAME_EMPTY` | `DAPR_PUBSUB_TOPIC_NAME_EMPTY` | Topic name is empty |
| `ERR_PUBSUB_FORBIDDEN` | `DAPR_PUBSUB_FORBIDDEN` | Access to topic forbidden for APP ID |
| `ERR_PUBSUB_PUBLISH_MESSAGE` | `DAPR_PUBSUB_PUBLISH_MESSAGE` | Error publishing message |
| `ERR_PUBSUB_REQUEST_METADATA` | `DAPR_PUBSUB_METADATA_DESERIALIZATION` | Error deserializing metadata |
| `ERR_PUBSUB_CLOUD_EVENTS_SER` | `DAPR_PUBSUB_CLOUD_EVENT_CREATION` | Error creating CloudEvent |
| `ERR_PUBSUB_EVENTS_SER` | `DAPR_PUBSUB_MARSHAL_ENVELOPE` | Error marshalling Cloud Event envelope |
| `ERR_PUBSUB_EVENTS_SER` | `DAPR_PUBSUB_MARSHAL_EVENTS` | Error marshalling events to bytes |
| `ERR_PUBSUB_EVENTS_SER` | `DAPR_PUBSUB_UNMARSHAL_EVENTS` | Error unmarshalling events |
| `ERR_PUBLISH_OUTBOX` | | Error publishing message to outbox |
### Conversation API
| HTTP Code | gRPC Code | Description |
| --------------------------------- | --------- | --------------------------------------------- |
| `ERR_CONVERSATION_INVALID_PARMS` | | Invalid parameters for conversation component |
| `ERR_CONVERSATION_INVOKE` | | Error invoking conversation |
| `ERR_CONVERSATION_MISSING_INPUTS` | | Missing inputs for conversation |
| `ERR_CONVERSATION_NOT_FOUND` | | Conversation not found |
### Service Invocation / Direct Messaging API
| HTTP Code | gRPC Code | Description |
| ------------------- | --------- | ---------------------- |
| `ERR_DIRECT_INVOKE` | | Error invoking service |
### Bindings API
| HTTP Code | gRPC Code | Description |
| --------------------------- | --------- | ----------------------------- |
| `ERR_INVOKE_OUTPUT_BINDING` | | Error invoking output binding |
### Distributed Lock API
| HTTP Code | gRPC Code | Description |
| ------------------------------- | --------- | ------------------------- |
| `ERR_LOCK_STORE_NOT_CONFIGURED` | | Lock store not configured |
| `ERR_LOCK_STORE_NOT_FOUND` | | Lock store not found |
| `ERR_TRY_LOCK` | | Error acquiring lock |
| `ERR_UNLOCK` | | Error releasing lock |
### Healthz
| HTTP Code | gRPC Code | Description |
| ------------------------------- | --------- | --------------------------- |
| `ERR_HEALTH_NOT_READY` | | Dapr not ready |
| `ERR_HEALTH_APPID_NOT_MATCH` | | Dapr App ID does not match |
| `ERR_OUTBOUND_HEALTH_NOT_READY` | | Dapr outbound not ready |
### Common
| HTTP Code | gRPC Code | Description |
| ---------------------------- | --------- | -------------------------- |
| `ERR_API_UNIMPLEMENTED` | | API not implemented |
| `ERR_APP_CHANNEL_NIL` | | App channel is nil |
| `ERR_BAD_REQUEST` | | Bad request |
| `ERR_BODY_READ` | | Error reading request body |
| `ERR_INTERNAL` | | Internal error |
| `ERR_MALFORMED_REQUEST` | | Malformed request |
| `ERR_MALFORMED_REQUEST_DATA` | | Malformed request data |
| `ERR_MALFORMED_RESPONSE` | | Malformed response |
### Scheduler/Jobs API
| HTTP Code | gRPC Code | Description |
| ------------------------------- | ------------------------------- | -------------------------------------- |
| `DAPR_SCHEDULER_SCHEDULE_JOB` | `DAPR_SCHEDULER_SCHEDULE_JOB` | Error scheduling job |
| `DAPR_SCHEDULER_JOB_NAME` | `DAPR_SCHEDULER_JOB_NAME` | Job name should only be set in the url |
| `DAPR_SCHEDULER_JOB_NAME_EMPTY` | `DAPR_SCHEDULER_JOB_NAME_EMPTY` | Job name is empty |
| `DAPR_SCHEDULER_GET_JOB` | `DAPR_SCHEDULER_GET_JOB` | Error getting job |
| `DAPR_SCHEDULER_LIST_JOBS` | `DAPR_SCHEDULER_LIST_JOBS` | Error listing jobs |
| `DAPR_SCHEDULER_DELETE_JOB` | `DAPR_SCHEDULER_DELETE_JOB` | Error deleting job |
| `DAPR_SCHEDULER_EMPTY` | `DAPR_SCHEDULER_EMPTY` | Required argument is empty |
| `DAPR_SCHEDULER_SCHEDULE_EMPTY` | `DAPR_SCHEDULER_SCHEDULE_EMPTY` | No schedule provided for job |
### Generic
| HTTP Code | gRPC Code | Description |
| --------- | --------- | ------------- |
| `ERROR` | `ERROR` | Generic error |
## Next steps
- [Handling HTTP error codes]({{< ref http-error-codes.md >}})
- [Handling gRPC error codes]({{< ref grpc-error-codes.md >}})

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@ -0,0 +1,62 @@
---
type: docs
title: "Errors overview"
linkTitle: "Overview"
weight: 10
description: "Overview of Dapr errors"
---
An error code is a numeric or alphamueric code that indicates the nature of an error and, when possible, why it occured.
Dapr error codes are standardized strings for over 80+ common errors across HTTP and gRPC requests when using the Dapr APIs. These codes are both:
- Returned in the JSON response body of the request.
- When enabled, logged in debug-level logs in the runtime.
- If you're running in Kubernetes, error codes are logged in the sidecar.
- If you're running in self-hosted, you can enable and run debug logs.
## Error format
Dapr error codes consist of a prefix, a category, and shorthand of the error itself. For example:
| Prefix | Category | Error shorthand |
| ------ | -------- | --------------- |
| ERR_ | PUBSUB_ | NOT_FOUND |
Some of the most common errors returned include:
- ERR_ACTOR_TIMER_CREATE
- ERR_PURGE_WORKFLOW
- ERR_STATE_STORE_NOT_FOUND
- ERR_HEALTH_NOT_READY
> **Note:** [See a full list of error codes in Dapr.]({{< ref error-codes-reference.md >}})
An error returned for a state store not found might look like the following:
```json
{
"error": "Bad Request",
"error_msg": "{\"errorCode\":\"ERR_STATE_STORE_NOT_FOUND\",\"message\":\"state store <name> is not found\",\"details\":[{\"@type\":\"type.googleapis.com/google.rpc.ErrorInfo\",\"domain\":\"dapr.io\",\"metadata\":{\"appID\":\"nodeapp\"},\"reason\":\"DAPR_STATE_NOT_FOUND\"}]}",
"status": 400
}
```
The returned error includes:
- The error code: `ERR_STATE_STORE_NOT_FOUND`
- The error message describing the issue: `state store <name> is not found`
- The app ID in which the error is occuring: `nodeapp`
- The reason for the error: `DAPR_STATE_NOT_FOUND`
## Dapr error code metrics
Metrics help you see when exactly errors are occuring from within the runtime. Error code metrics are collected using the `error_code_total` endpoint. This endpoint is disabled by default. You can [enable it using the `recordErrorCodes` field in your configuration file]({{< ref "metrics-overview.md#configuring-metrics-for-error-codes" >}}).
## Demo
Watch a demo presented during [Diagrid's Dapr v1.15 celebration](https://www.diagrid.io/videos/dapr-1-15-deep-dive) to see how to enable error code metrics and handle error codes returned in the runtime.
<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/NTnwoDhHIcQ?si=I2uCB_TINGxlu-9v&amp;start=2812" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
## Next step
{{< button text="See a list of all Dapr error codes" page="error-codes-reference" >}}

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@ -1,20 +1,18 @@
---
type: docs
title: Dapr errors
linkTitle: "Dapr errors"
weight: 700
description: "Information on Dapr errors and how to handle them"
title: Handling gRPC error codes
linkTitle: "gRPC"
weight: 40
description: "Information on Dapr gRPC errors and how to handle them"
---
## Error handling: Understanding errors model and reporting
Initially, errors followed the [Standard gRPC error model](https://grpc.io/docs/guides/error/#standard-error-model). However, to provide more detailed and informative error messages, an enhanced error model has been defined which aligns with the gRPC [Richer error model](https://grpc.io/docs/guides/error/#richer-error-model).
{{% alert title="Note" color="primary" %}}
Not all Dapr errors have been converted to the richer gRPC error model.
{{% /alert %}}
### Standard gRPC Error Model
## Standard gRPC Error Model
The [Standard gRPC error model](https://grpc.io/docs/guides/error/#standard-error-model) is an approach to error reporting in gRPC. Each error response includes an error code and an error message. The error codes are standardized and reflect common error conditions.
@ -25,7 +23,7 @@ ERROR:
Message: input key/keyPrefix 'bad||keyname' can't contain '||'
```
### Richer gRPC Error Model
## Richer gRPC Error Model
The [Richer gRPC error model](https://grpc.io/docs/guides/error/#richer-error-model) extends the standard error model by providing additional context and details about the error. This model includes the standard error `code` and `message`, along with a `details` section that can contain various types of information, such as `ErrorInfo`, `ResourceInfo`, and `BadRequest` details.

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@ -0,0 +1,21 @@
---
type: docs
title: "Handling HTTP error codes"
linkTitle: "HTTP"
description: "Detailed reference of the Dapr HTTP error codes and how to handle them"
weight: 30
---
For HTTP calls made to Dapr runtime, when an error is encountered, an error JSON is returned in response body. The JSON contains an error code and an descriptive error message.
```
{
"errorCode": "ERR_STATE_GET",
"message": "Requested state key does not exist in state store."
}
```
## Related
- [Error code reference list]({{< ref error-codes-reference.md >}})
- [Handling gRPC error codes]({{< ref grpc-error-codes.md >}})

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@ -80,10 +80,16 @@ In production scenarios, it is recommended to use a solution such as:
If running on AWS EKS, you can [link an IAM role to a Kubernetes service account](https://docs.aws.amazon.com/eks/latest/userguide/create-service-account-iam-policy-and-role.html), which your pod can use.
All of these solutions solve the same problem: They allow the Dapr runtime process (or sidecar) to retrive credentials dynamically, so that explicit credentials aren't needed. This provides several benefits, such as automated key rotation, and avoiding having to manage secrets.
All of these solutions solve the same problem: They allow the Dapr runtime process (or sidecar) to retrieve credentials dynamically, so that explicit credentials aren't needed. This provides several benefits, such as automated key rotation, and avoiding having to manage secrets.
Both Kiam and Kube2IAM work by intercepting calls to the [instance metadata service](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/configuring-instance-metadata-service.html).
### Setting Up Dapr with AWS EKS Pod Identity
EKS Pod Identities provide the ability to manage credentials for your applications, similar to the way that Amazon EC2 instance profiles provide credentials to Amazon EC2 instances. Instead of creating and distributing your AWS credentials to the containers or using the Amazon EC2 instance’s role, you associate an IAM role with a Kubernetes service account and configure your Pods to use the service account.
To see a comprehensive example on how to authorize pod access to AWS Secrets Manager from EKS using AWS EKS Pod Identity, [follow the sample in this repository](https://github.com/dapr/samples/tree/master/dapr-eks-podidentity).
### Use an instance profile when running in stand-alone mode on AWS EC2
If running Dapr directly on an AWS EC2 instance in stand-alone mode, you can use instance profiles.
@ -130,7 +136,6 @@ On Windows, the environment variable needs to be set before starting the `dapr`
{{< /tabs >}}
### Authenticate to AWS if using AWS SSO based profiles
If you authenticate to AWS using [AWS SSO](https://aws.amazon.com/single-sign-on/), some AWS SDKs (including the Go SDK) don't yet support this natively. There are several utilities you can use to "bridge the gap" between AWS SSO-based credentials and "legacy" credentials, such as:
@ -157,7 +162,7 @@ AWS_PROFILE=myprofile awshelper daprd...
<!-- windows -->
{{% codetab %}}
On Windows, the environment variable needs to be set before starting the `awshelper` command, doing it inline (like in Linxu/MacOS) is not supported.
On Windows, the environment variable needs to be set before starting the `awshelper` command; doing it inline (like in Linux/MacOS) is not supported.
{{% /codetab %}}
@ -169,4 +174,7 @@ On Windows, the environment variable needs to be set before starting the `awshel
## Related links
For more information, see [how the AWS SDK (which Dapr uses) handles credentials](https://docs.aws.amazon.com/sdk-for-go/v1/developer-guide/configuring-sdk.html#specifying-credentials).
- For more information, see [how the AWS SDK (which Dapr uses) handles credentials](https://docs.aws.amazon.com/sdk-for-go/v1/developer-guide/configuring-sdk.html#specifying-credentials).
- [EKS Pod Identity Documentation](https://docs.aws.amazon.com/eks/latest/userguide/pod-identities.html)
- [AWS SDK Credentials Configuration](https://docs.aws.amazon.com/sdk-for-go/v1/developer-guide/configuring-sdk.html#specifying-credentials)
- [Set up an Elastic Kubernetes Service (EKS) cluster](https://docs.dapr.io/operations/hosting/kubernetes/cluster/setup-eks/)

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@ -1,21 +0,0 @@
---
type: docs
title: "How to: Integrate using Testcontainers Dapr Module"
linkTitle: "Dapr Testcontainers"
weight: 3000
description: "Use the Dapr Testcontainer module from your Java application"
---
You can use the Testcontainers Dapr Module provided by Diagrid to set up Dapr locally for your Java applications. Simply add the following dependency to your Maven project:
```xml
<dependency>
<groupId>io.diagrid.dapr</groupId>
<artifactId>testcontainers-dapr</artifactId>
<version>0.10.x</version>
</dependency>
```
[If you're using Spring Boot, you can also use the Spring Boot Starter.](https://github.com/diagridio/spring-boot-starter-dapr)
{{< button text="Use the Testcontainers Dapr Module" link="https://github.com/diagridio/testcontainers-dapr" >}}

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@ -2,6 +2,6 @@
type: docs
title: "Integrations"
linkTitle: "Integrations"
weight: 60
weight: 70
description: "Dapr integrations with other technologies"
---

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@ -2,6 +2,6 @@
type: docs
title: "Local development"
linkTitle: "Local development"
weight: 40
weight: 50
description: "Capabilities for developing Dapr applications locally"
---

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@ -2,7 +2,7 @@
type: docs
title: "Dapr Software Development Kits (SDKs)"
linkTitle: "SDKs"
weight: 20
weight: 30
description: "Use your favorite languages with Dapr"
no_list: true
---

4
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@ -10,7 +10,7 @@ no_list: true
Hit the ground running with our Dapr quickstarts, complete with code samples aimed to get you started quickly with Dapr.
{{% alert title="Note" color="primary" %}}
We are actively working on adding to our quickstart library. In the meantime, you can explore Dapr through our [tutorials]({{< ref "getting-started/tutorials/_index.md" >}}).
Each release, the quickstart library has new examples added for the APIs and SDKs. You can also explore Dapr through the [tutorials]({{< ref "getting-started/tutorials/_index.md" >}}).
{{% /alert %}}
@ -33,4 +33,4 @@ Hit the ground running with our Dapr quickstarts, complete with code samples aim
| [Resiliency]({{< ref resiliency >}}) | Define and apply fault-tolerance policies to your Dapr API requests. |
| [Cryptography]({{< ref cryptography-quickstart.md >}}) | Encrypt and decrypt data using Dapr's cryptographic APIs. |
| [Jobs]({{< ref jobs-quickstart.md >}}) | Schedule, retrieve, and delete jobs using Dapr's jobs APIs. |
| [Conversation]({{< ref conversation-quickstart.md >}}) | Securely and reliably interact with Large Language Models (LLMs). |

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@ -20,8 +20,8 @@ As a quick overview of the .NET actors quickstart:
1. Using a `SmartDevice.Service` microservice, you host:
- Two `SmokeDetectorActor` smoke alarm objects
- A `ControllerActor` object that commands and controls the smart devices
1. Using a `SmartDevice.Client` console app, the client app interacts with each actor, or the controller, to perform actions in aggregate.
1. The `SmartDevice.Interfaces` contains the shared interfaces and data types used by both the service and client apps.
2. Using a `SmartDevice.Client` console app, the client app interacts with each actor, or the controller, to perform actions in aggregate.
3. The `SmartDevice.Interfaces` contains the shared interfaces and data types used by both the service and client apps.
<img src="/images/actors-quickstart/actors-quickstart.png" width=800 style="padding-bottom:15px;">
@ -30,10 +30,13 @@ As a quick overview of the .NET actors quickstart:
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [.NET SDK or .NET 6 SDK installed](https://dotnet.microsoft.com/download).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
- [.NET 6](https://dotnet.microsoft.com/download/dotnet/6.0), [.NET 8](https://dotnet.microsoft.com/download/dotnet/8.0) or [.NET 9](https://dotnet.microsoft.com/download/dotnet/9.0) installed
**NOTE:** .NET 6 is the minimally supported version of .NET for the Dapr .NET SDK packages in this release. Only .NET 8 and .NET 9
will be supported in Dapr v1.16 and later releases.
### Step 1: Set up the environment

5
daprdocs/content/en/getting-started/quickstarts/bindings-quickstart.md
View File

@ -443,10 +443,13 @@ In the YAML file:
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [.NET SDK or .NET 6 SDK installed](https://dotnet.microsoft.com/download).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
- [.NET 6](https://dotnet.microsoft.com/download/dotnet/6.0), [.NET 8](https://dotnet.microsoft.com/download/dotnet/8.0) or [.NET 9](https://dotnet.microsoft.com/download/dotnet/9.0) installed
**NOTE:** .NET 6 is the minimally supported version of .NET for the Dapr .NET SDK packages in this release. Only .NET 8 and .NET 9
will be supported in Dapr v1.16 and later releases.
### Step 1: Set up the environment

5
daprdocs/content/en/getting-started/quickstarts/configuration-quickstart.md
View File

@ -272,10 +272,13 @@ setTimeout(() => {
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [.NET SDK or .NET 6 SDK installed](https://dotnet.microsoft.com/download).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
- [.NET 6](https://dotnet.microsoft.com/download/dotnet/6.0), [.NET 8](https://dotnet.microsoft.com/download/dotnet/8.0) or [.NET 9](https://dotnet.microsoft.com/download/dotnet/9.0) installed
**NOTE:** .NET 6 is the minimally supported version of .NET for the Dapr .NET SDK packages in this release. Only .NET 8 and .NET 9
will be supported in Dapr v1.16 and later releases.
### Step 1: Set up the environment

782
daprdocs/content/en/getting-started/quickstarts/conversation-quickstart.md Normal file
View File

@ -0,0 +1,782 @@
---
type: docs
title: "Quickstart: Conversation"
linkTitle: Conversation
weight: 90
description: Get started with the Dapr conversation building block
---
{{% alert title="Alpha" color="warning" %}}
The conversation building block is currently in **alpha**.
{{% /alert %}}
Let's take a look at how the [Dapr conversation building block]({{< ref conversation-overview.md >}}) makes interacting with Large Language Models (LLMs) easier. In this quickstart, you use the echo component to communicate with the mock LLM and ask it for a poem about Dapr.
You can try out this conversation quickstart by either:
- [Running the application in this sample with the Multi-App Run template file]({{< ref "#run-the-app-with-the-template-file" >}}), or
- [Running the application without the template]({{< ref "#run-the-app-without-the-template" >}})
{{% alert title="Note" color="primary" %}}
Currently, only the HTTP quickstart sample is available in Python and JavaScript.
{{% /alert %}}
## Run the app with the template file
{{< tabs Python JavaScript ".NET" Go >}}
<!-- Python -->
{{% codetab %}}
### Step 1: Pre-requisites
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [Python 3.7+ installed](https://www.python.org/downloads/).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
### Step 2: Set up the environment
Clone the [sample provided in the Quickstarts repo](https://github.com/dapr/quickstarts/tree/master/conversation).
```bash
git clone https://github.com/dapr/quickstarts.git
```
From the root of the Quickstarts directory, navigate into the conversation directory:
```bash
cd conversation/python/http/conversation
```
Install the dependencies:
```bash
pip3 install -r requirements.txt
```
### Step 3: Launch the conversation service
Navigate back to the `http` directory and start the conversation service with the following command:
```bash
dapr run -f .
```
**Expected output**
```
== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?
```
### What happened?
When you ran `dapr init` during Dapr install, the [`dapr.yaml` Multi-App Run template file]({{< ref "#dapryaml-multi-app-run-template-file" >}}) was generated in the `.dapr/components` directory.
Running `dapr run -f .` in this Quickstart started [conversation.go]({{< ref "#programcs-conversation-app" >}}).
#### `dapr.yaml` Multi-App Run template file
Running the [Multi-App Run template file]({{< ref multi-app-dapr-run >}}) with `dapr run -f .` starts all applications in your project. This Quickstart has only one application, so the `dapr.yaml` file contains the following:
```yml
version: 1
common:
resourcesPath: ../../components/
apps:
- appID: conversation
appDirPath: ./conversation/
command: ["python3", "app.py"]
```
#### Echo mock LLM component
In [`conversation/components`](https://github.com/dapr/quickstarts/tree/master/conversation/components) directly of the quickstart, the [`conversation.yaml` file](https://github.com/dapr/quickstarts/tree/master/conversation/components/conversation.yml) configures the echo LLM component.
```yml
apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
name: echo
spec:
type: conversation.echo
version: v1
```
To interface with a real LLM, swap out the mock component with one of [the supported conversation components]({{< ref "supported-conversation" >}}). For example, to use an OpenAI component, see the [example in the conversation how-to guide]({{< ref "howto-conversation-layer.md#use-the-openai-component" >}})
#### `app.py` conversation app
In the application code:
- The app sends an input "What is dapr?" to the echo mock LLM component.
- The mock LLM echoes "What is dapr?".
```python
import logging
import requests
import os
logging.basicConfig(level=logging.INFO)
base_url = os.getenv('BASE_URL', 'http://localhost') + ':' + os.getenv(
'DAPR_HTTP_PORT', '3500')
CONVERSATION_COMPONENT_NAME = 'echo'
input = {
'name': 'echo',
'inputs': [{'message':'What is dapr?'}],
'parameters': {},
'metadata': {}
}
# Send input to conversation endpoint
result = requests.post(
url='%s/v1.0-alpha1/conversation/%s/converse' % (base_url, CONVERSATION_COMPONENT_NAME),
json=input
)
logging.info('Input sent: What is dapr?')
# Parse conversation output
data = result.json()
output = data["outputs"][0]["result"]
logging.info('Output response: ' + output)
```
{{% /codetab %}}
<!-- JavaScript -->
{{% codetab %}}
### Step 1: Pre-requisites
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [Latest Node.js installed](https://nodejs.org/).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
### Step 2: Set up the environment
Clone the [sample provided in the Quickstarts repo](https://github.com/dapr/quickstarts/tree/master/conversation).
```bash
git clone https://github.com/dapr/quickstarts.git
```
From the root of the Quickstarts directory, navigate into the conversation directory:
```bash
cd conversation/javascript/http/conversation
```
Install the dependencies:
```bash
npm install
```
### Step 3: Launch the conversation service
Navigate back to the `http` directory and start the conversation service with the following command:
```bash
dapr run -f .
```
**Expected output**
```
== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?
```
### What happened?
When you ran `dapr init` during Dapr install, the [`dapr.yaml` Multi-App Run template file]({{< ref "#dapryaml-multi-app-run-template-file" >}}) was generated in the `.dapr/components` directory.
Running `dapr run -f .` in this Quickstart started [conversation.go]({{< ref "#programcs-conversation-app" >}}).
#### `dapr.yaml` Multi-App Run template file
Running the [Multi-App Run template file]({{< ref multi-app-dapr-run >}}) with `dapr run -f .` starts all applications in your project. This Quickstart has only one application, so the `dapr.yaml` file contains the following:
```yml
version: 1
common:
resourcesPath: ../../components/
apps:
- appID: conversation
appDirPath: ./conversation/
daprHTTPPort: 3502
command: ["npm", "run", "start"]
```
#### Echo mock LLM component
In [`conversation/components`](https://github.com/dapr/quickstarts/tree/master/conversation/components) directly of the quickstart, the [`conversation.yaml` file](https://github.com/dapr/quickstarts/tree/master/conversation/components/conversation.yml) configures the echo LLM component.
```yml
apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
name: echo
spec:
type: conversation.echo
version: v1
```
To interface with a real LLM, swap out the mock component with one of [the supported conversation components]({{< ref "supported-conversation" >}}). For example, to use an OpenAI component, see the [example in the conversation how-to guide]({{< ref "howto-conversation-layer.md#use-the-openai-component" >}})
#### `index.js` conversation app
In the application code:
- The app sends an input "What is dapr?" to the echo mock LLM component.
- The mock LLM echoes "What is dapr?".
```javascript
const conversationComponentName = "echo";
async function main() {
const daprHost = process.env.DAPR_HOST || "http://localhost";
const daprHttpPort = process.env.DAPR_HTTP_PORT || "3500";
const inputBody = {
name: "echo",
inputs: [{ message: "What is dapr?" }],
parameters: {},
metadata: {},
};
const reqURL = `${daprHost}:${daprHttpPort}/v1.0-alpha1/conversation/${conversationComponentName}/converse`;
try {
const response = await fetch(reqURL, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify(inputBody),
});
console.log("Input sent: What is dapr?");
const data = await response.json();
const result = data.outputs[0].result;
console.log("Output response:", result);
} catch (error) {
console.error("Error:", error.message);
process.exit(1);
}
}
main().catch((error) => {
console.error("Unhandled error:", error);
process.exit(1);
});
```
{{% /codetab %}}
<!-- .NET -->
{{% codetab %}}
### Step 1: Pre-requisites
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [.NET 8 SDK+ installed](https://dotnet.microsoft.com/download).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
### Step 2: Set up the environment
Clone the [sample provided in the Quickstarts repo](https://github.com/dapr/quickstarts/tree/master/conversation).
```bash
git clone https://github.com/dapr/quickstarts.git
```
From the root of the Quickstarts directory, navigate into the conversation directory:
```bash
cd conversation/csharp/sdk
```
### Step 3: Launch the conversation service
Start the conversation service with the following command:
```bash
dapr run -f .
```
**Expected output**
```
== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?
```
### What happened?
When you ran `dapr init` during Dapr install, the [`dapr.yaml` Multi-App Run template file]({{< ref "#dapryaml-multi-app-run-template-file" >}}) was generated in the `.dapr/components` directory.
Running `dapr run -f .` in this Quickstart started the [conversation Program.cs]({{< ref "#programcs-conversation-app" >}}).
#### `dapr.yaml` Multi-App Run template file
Running the [Multi-App Run template file]({{< ref multi-app-dapr-run >}}) with `dapr run -f .` starts all applications in your project. This Quickstart has only one application, so the `dapr.yaml` file contains the following:
```yml
version: 1
common:
resourcesPath: ../../components/
apps:
- appDirPath: ./conversation/
appID: conversation
daprHTTPPort: 3500
command: ["dotnet", "run"]
```
#### Echo mock LLM component
In [`conversation/components`](https://github.com/dapr/quickstarts/tree/master/conversation/components), the [`conversation.yaml` file](https://github.com/dapr/quickstarts/tree/master/conversation/components/conversation.yml) configures the echo mock LLM component.
```yml
apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
name: echo
spec:
type: conversation.echo
version: v1
```
To interface with a real LLM, swap out the mock component with one of [the supported conversation components]({{< ref "supported-conversation" >}}). For example, to use an OpenAI component, see the [example in the conversation how-to guide]({{< ref "howto-conversation-layer.md#use-the-openai-component" >}})
#### `Program.cs` conversation app
In the application code:
- The app sends an input "What is dapr?" to the echo mock LLM component.
- The mock LLM echoes "What is dapr?".
```csharp
using Dapr.AI.Conversation;
using Dapr.AI.Conversation.Extensions;
class Program
{
private const string ConversationComponentName = "echo";
static async Task Main(string[] args)
{
const string prompt = "What is dapr?";
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddDaprConversationClient();
var app = builder.Build();
//Instantiate Dapr Conversation Client
var conversationClient = app.Services.GetRequiredService<DaprConversationClient>();
try
{
// Send a request to the echo mock LLM component
var response = await conversationClient.ConverseAsync(ConversationComponentName, [new(prompt, DaprConversationRole.Generic)]);
Console.WriteLine("Input sent: " + prompt);
if (response != null)
{
Console.Write("Output response:");
foreach (var resp in response.Outputs)
{
Console.WriteLine($" {resp.Result}");
}
}
}
catch (Exception ex)
{
Console.WriteLine("Error: " + ex.Message);
}
}
}
```
{{% /codetab %}}
<!-- Go -->
{{% codetab %}}
### Step 1: Pre-requisites
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [Latest version of Go](https://go.dev/dl/).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
### Step 2: Set up the environment
Clone the [sample provided in the Quickstarts repo](https://github.com/dapr/quickstarts/tree/master/conversation).
```bash
git clone https://github.com/dapr/quickstarts.git
```
From the root of the Quickstarts directory, navigate into the conversation directory:
```bash
cd conversation/go/sdk
```
### Step 3: Launch the conversation service
Start the conversation service with the following command:
```bash
dapr run -f .
```
**Expected output**
```
== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?
```
### What happened?
When you ran `dapr init` during Dapr install, the [`dapr.yaml` Multi-App Run template file]({{< ref "#dapryaml-multi-app-run-template-file" >}}) was generated in the `.dapr/components` directory.
Running `dapr run -f .` in this Quickstart started [conversation.go]({{< ref "#programcs-conversation-app" >}}).
#### `dapr.yaml` Multi-App Run template file
Running the [Multi-App Run template file]({{< ref multi-app-dapr-run >}}) with `dapr run -f .` starts all applications in your project. This Quickstart has only one application, so the `dapr.yaml` file contains the following:
```yml
version: 1
common:
resourcesPath: ../../components/
apps:
- appDirPath: ./conversation/
appID: conversation
daprHTTPPort: 3501
command: ["go", "run", "."]
```
#### Echo mock LLM component
In [`conversation/components`](https://github.com/dapr/quickstarts/tree/master/conversation/components) directly of the quickstart, the [`conversation.yaml` file](https://github.com/dapr/quickstarts/tree/master/conversation/components/conversation.yml) configures the echo LLM component.
```yml
apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
name: echo
spec:
type: conversation.echo
version: v1
```
To interface with a real LLM, swap out the mock component with one of [the supported conversation components]({{< ref "supported-conversation" >}}). For example, to use an OpenAI component, see the [example in the conversation how-to guide]({{< ref "howto-conversation-layer.md#use-the-openai-component" >}})
#### `conversation.go` conversation app
In the application code:
- The app sends an input "What is dapr?" to the echo mock LLM component.
- The mock LLM echoes "What is dapr?".
```go
package main
import (
"context"
"fmt"
"log"
dapr "github.com/dapr/go-sdk/client"
)
func main() {
client, err := dapr.NewClient()
if err != nil {
panic(err)
}
input := dapr.ConversationInput{
Message: "What is dapr?",
// Role: nil, // Optional
// ScrubPII: nil, // Optional
}
fmt.Println("Input sent:", input.Message)
var conversationComponent = "echo"
request := dapr.NewConversationRequest(conversationComponent, []dapr.ConversationInput{input})
resp, err := client.ConverseAlpha1(context.Background(), request)
if err != nil {
log.Fatalf("err: %v", err)
}
fmt.Println("Output response:", resp.Outputs[0].Result)
}
```
{{% /codetab %}}
{{< /tabs >}}
## Run the app without the template
{{< tabs Python JavaScript ".NET" Go >}}
<!-- Python -->
{{% codetab %}}
### Step 1: Pre-requisites
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [Python 3.7+ installed](https://www.python.org/downloads/).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
### Step 2: Set up the environment
Clone the [sample provided in the Quickstarts repo](https://github.com/dapr/quickstarts/tree/master/conversation).
```bash
git clone https://github.com/dapr/quickstarts.git
```
From the root of the Quickstarts directory, navigate into the conversation directory:
```bash
cd conversation/python/http/conversation
```
Install the dependencies:
```bash
pip3 install -r requirements.txt
```
### Step 3: Launch the conversation service
Navigate back to the `http` directory and start the conversation service with the following command:
```bash
dapr run --app-id conversation --resources-path ../../../components -- python3 app.py
```
> **Note**: Since Python3.exe is not defined in Windows, you may need to use `python app.py` instead of `python3 app.py`.
**Expected output**
```
== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?
```
{{% /codetab %}}
<!-- JavaScript -->
{{% codetab %}}
### Step 1: Pre-requisites
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [Latest Node.js installed](https://nodejs.org/).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
### Step 2: Set up the environment
Clone the [sample provided in the Quickstarts repo](https://github.com/dapr/quickstarts/tree/master/conversation).
```bash
git clone https://github.com/dapr/quickstarts.git
```
From the root of the Quickstarts directory, navigate into the conversation directory:
```bash
cd conversation/javascript/http/conversation
```
Install the dependencies:
```bash
npm install
```
### Step 3: Launch the conversation service
Navigate back to the `http` directory and start the conversation service with the following command:
```bash
dapr run --app-id conversation --resources-path ../../../components/ -- npm run start
```
**Expected output**
```
== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?
```
{{% /codetab %}}
<!-- .NET -->
{{% codetab %}}
### Step 1: Pre-requisites
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [.NET 8+ SDK installed](https://dotnet.microsoft.com/download).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
### Step 2: Set up the environment
Clone the [sample provided in the Quickstarts repo](https://github.com/dapr/quickstarts/tree/master/conversation).
```bash
git clone https://github.com/dapr/quickstarts.git
```
From the root of the Quickstarts directory, navigate into the conversation directory:
```bash
cd conversation/csharp/sdk/conversation
```
Install the dependencies:
```bash
dotnet build
```
### Step 3: Launch the conversation service
Start the conversation service with the following command:
```bash
dapr run --app-id conversation --resources-path ../../../components/ -- dotnet run
```
**Expected output**
```
== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?
```
{{% /codetab %}}
<!-- Go -->
{{% codetab %}}
### Step 1: Pre-requisites
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [Latest version of Go](https://go.dev/dl/).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
### Step 2: Set up the environment
Clone the [sample provided in the Quickstarts repo](https://github.com/dapr/quickstarts/tree/master/conversation).
```bash
git clone https://github.com/dapr/quickstarts.git
```
From the root of the Quickstarts directory, navigate into the conversation directory:
```bash
cd conversation/go/sdk/conversation
```
Install the dependencies:
```bash
go build .
```
### Step 3: Launch the conversation service
Start the conversation service with the following command:
```bash
dapr run --app-id conversation --resources-path ../../../components/ -- go run .
```
**Expected output**
```
== APP - conversation == Input sent: What is dapr?
== APP - conversation == Output response: What is dapr?
```
{{% /codetab %}}
{{< /tabs >}}
## Demo
Watch the demo presented during [Diagrid's Dapr v1.15 celebration](https://www.diagrid.io/videos/dapr-1-15-deep-dive) to see how the conversation API works using the .NET SDK.
<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/NTnwoDhHIcQ?si=37SDcOHtEpgCIwkG&amp;start=5444" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
## Tell us what you think!
We're continuously working to improve our Quickstart examples and value your feedback. Did you find this Quickstart helpful? Do you have suggestions for improvement?
Join the discussion in our [discord channel](https://discord.com/channels/778680217417809931/953427615916638238).
## Next steps
- HTTP samples of this quickstart:
- [Python](https://github.com/dapr/quickstarts/tree/master/conversation/python/http)
- [JavaScript](https://github.com/dapr/quickstarts/tree/master/conversation/javascript/http)
- [.NET](https://github.com/dapr/quickstarts/tree/master/conversation/csharp/http)
- [Go](https://github.com/dapr/quickstarts/tree/master/conversation/go/http)
- Learn more about [the conversation building block]({{< ref conversation-overview.md >}})
{{< button text="Explore Dapr tutorials >>" page="getting-started/tutorials/_index.md" >}}

5
daprdocs/content/en/getting-started/quickstarts/pubsub-quickstart.md
View File

@ -358,10 +358,13 @@ console.log("Published data: " + JSON.stringify(order));
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [.NET SDK or .NET 6 SDK installed](https://dotnet.microsoft.com/download).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
- [.NET 6](https://dotnet.microsoft.com/download/dotnet/6.0), [.NET 8](https://dotnet.microsoft.com/download/dotnet/8.0) or [.NET 9](https://dotnet.microsoft.com/download/dotnet/9.0) installed
**NOTE:** .NET 6 is the minimally supported version of .NET for the Dapr .NET SDK packages in this release. Only .NET 8 and .NET 9
will be supported in Dapr v1.16 and later releases.
### Step 2: Set up the environment

5
daprdocs/content/en/getting-started/quickstarts/secrets-quickstart.md
View File

@ -247,10 +247,13 @@ Order-processor output:
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [.NET SDK or .NET 6 SDK installed](https://dotnet.microsoft.com/download).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
- [.NET 6](https://dotnet.microsoft.com/download/dotnet/6.0), [.NET 8](https://dotnet.microsoft.com/download/dotnet/8.0) or [.NET 9](https://dotnet.microsoft.com/download/dotnet/9.0) installed
**NOTE:** .NET 6 is the minimally supported version of .NET for the Dapr .NET SDK packages in this release. Only .NET 8 and .NET 9
will be supported in Dapr v1.16 and later releases.
### Step 1: Set up the environment

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@ -315,10 +315,13 @@ console.log("Order passed: " + res.config.data);
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [.NET SDK or .NET 7 SDK installed](https://dotnet.microsoft.com/download).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
- [.NET 6](https://dotnet.microsoft.com/download/dotnet/6.0), [.NET 8](https://dotnet.microsoft.com/download/dotnet/8.0) or [.NET 9](https://dotnet.microsoft.com/download/dotnet/9.0) installed
**NOTE:** .NET 6 is the minimally supported version of .NET for the Dapr .NET SDK packages in this release. Only .NET 8 and .NET 9
will be supported in Dapr v1.16 and later releases.
### Step 2: Set up the environment
@ -439,13 +442,11 @@ app.MapPost("/orders", (Order order) =>
In the Program.cs file for the `checkout` service, you'll notice there's no need to rewrite your app code to use Dapr's service invocation. You can enable service invocation by simply adding the `dapr-app-id` header, which specifies the ID of the target service.
```csharp
var client = new HttpClient();
client.DefaultRequestHeaders.Accept.Add(new System.Net.Http.Headers.MediaTypeWithQualityHeaderValue("application/json"));
var client = DaprClient.CreateInvokeHttpClient(appId: "order-processor");
var cts = new CancellationTokenSource();
client.DefaultRequestHeaders.Add("dapr-app-id", "order-processor");
var response = await client.PostAsync($"{baseURL}/orders", content);
Console.WriteLine("Order passed: " + order);
var response = await client.PostAsJsonAsync("/orders", order, cts.Token);
Console.WriteLine("Order passed: " + order);
```
{{% /codetab %}}
@ -1089,13 +1090,11 @@ dapr run --app-id checkout --app-protocol http --dapr-http-port 3500 -- dotnet r
In the Program.cs file for the `checkout` service, you'll notice there's no need to rewrite your app code to use Dapr's service invocation. You can enable service invocation by simply adding the `dapr-app-id` header, which specifies the ID of the target service.
```csharp
var client = new HttpClient();
client.DefaultRequestHeaders.Accept.Add(new System.Net.Http.Headers.MediaTypeWithQualityHeaderValue("application/json"));
var client = DaprClient.CreateInvokeHttpClient(appId: "order-processor");
var cts = new CancellationTokenSource();
client.DefaultRequestHeaders.Add("dapr-app-id", "order-processor");
var response = await client.PostAsync($"{baseURL}/orders", content);
Console.WriteLine("Order passed: " + order);
var response = await client.PostAsJsonAsync("/orders", order, cts.Token);
Console.WriteLine("Order passed: " + order);
```
### Step 5: Use with Multi-App Run

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@ -288,10 +288,13 @@ In the YAML file:
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [.NET SDK or .NET 6 SDK installed](https://dotnet.microsoft.com/download).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
- [.NET 6](https://dotnet.microsoft.com/download/dotnet/6.0), [.NET 8](https://dotnet.microsoft.com/download/dotnet/8.0) or [.NET 9](https://dotnet.microsoft.com/download/dotnet/9.0) installed
**NOTE:** .NET 6 is the minimally supported version of .NET for the Dapr .NET SDK packages in this release. Only .NET 8 and .NET 9
will be supported in Dapr v1.16 and later releases.
### Step 1: Set up the environment

5
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@ -507,10 +507,13 @@ The `order-processor` console app starts and manages the lifecycle of an order p
For this example, you will need:
- [Dapr CLI and initialized environment](https://docs.dapr.io/getting-started).
- [.NET SDK or .NET 6 SDK installed](https://dotnet.microsoft.com/download).
<!-- IGNORE_LINKS -->
- [Docker Desktop](https://www.docker.com/products/docker-desktop)
<!-- END_IGNORE -->
- [.NET 7](https://dotnet.microsoft.com/download/dotnet/7.0), [.NET 8](https://dotnet.microsoft.com/download/dotnet/8.0) or [.NET 9](https://dotnet.microsoft.com/download/dotnet/9.0) installed
**NOTE:** .NET 7 is the minimally supported version of .NET by Dapr.Workflows in Dapr v1.15. Only .NET 8 and .NET 9
will be supported in Dapr v1.16 and later releases.
### Step 2: Set up the environment

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@ -145,9 +145,12 @@ metrics:
- /payments/{paymentID}/refund
- /payments/{paymentID}/details
excludeVerbs: false
recordErrorCodes: true
```
In the examples above, the path filter `/orders/{orderID}/items/{itemID}` would return _a single metric count_ matching all the `orderID`s and all the `itemID`s, rather than multiple metrics for each `itemID`. For more information, see [HTTP metrics path matching]({{< ref "metrics-overview.md#http-metrics-path-matching" >}})
In the examples above, the path filter `/orders/{orderID}/items/{itemID}` would return _a single metric count_ matching all the `orderID`s and all the `itemID`s, rather than multiple metrics for each `itemID`. For more information, see [HTTP metrics path matching]({{< ref "metrics-overview.md#http-metrics-path-matching" >}}).
The above example also enables [recording error code metrics]({{< ref "metrics-overview.md#configuring-metrics-for-error-codes" >}}), which is disabled by default.
The following table lists the properties for metrics:

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@ -8,14 +8,14 @@ description: "Learn how to control how many requests and events can invoke your
Typically, in distributed computing, you may only want to allow for a given number of requests to execute concurrently. Using Dapr's `app-max-concurrency`, you can control how many requests and events can invoke your application simultaneously.
Default `app-max-concurreny` is set to `-1`, meaning no concurrency.
Default `app-max-concurreny` is set to `-1`, meaning no concurrency limit is enforced.
## Different approaches
While this guide focuses on `app-max-concurrency`, you can also limit request rate per second using the **`middleware.http.ratelimit`** middleware. However, it's important to understand the difference between the two approaches:
- `middleware.http.ratelimit`: Time bound and limits the number of requests per second
- `app-max-concurrency`: Specifies the number of concurrent requests (and events) at any point of time.
- `app-max-concurrency`: Specifies the max number of concurrent requests (and events) at any point of time.
See [Rate limit middleware]({{< ref middleware-rate-limit.md >}}) for more information about that approach.
@ -46,7 +46,7 @@ To set concurrency limits with the Dapr CLI for running on your local dev machin
dapr run --app-max-concurrency 1 --app-port 5000 python ./app.py
```
The above example effectively turns your app into a single concurrent service.
The above example effectively turns your app into a sequential processing service.
{{% /codetab %}}

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@ -66,7 +66,7 @@ This guide walks you through installing an Elastic Kubernetes Service (EKS) clus
1. Create the cluster by running the following command:
```bash
eksctl create cluster -f cluster.yaml
eksctl create cluster -f cluster-config.yaml
```
1. Verify the kubectl context:

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@ -231,6 +231,19 @@ You can install Dapr on Kubernetes using a Helm v3 chart.
--wait
```
To install in **high availability** mode and scale select services independently of global:
```bash
helm upgrade --install dapr dapr/dapr \
--version={{% dapr-latest-version short="true" %}} \
--namespace dapr-system \
--create-namespace \
--set global.ha.enabled=false \
--set dapr_scheduler.ha=true \
--set dapr_placement.ha=true \
--wait
```
See [Guidelines for production ready deployments on Kubernetes]({{< ref kubernetes-production.md >}}) for more information on installing and upgrading Dapr using Helm.
### (optional) Install the Dapr dashboard as part of the control plane

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@ -8,12 +8,13 @@ description: "Overview of how to get Dapr running on your Kubernetes cluster"
Dapr can be configured to run on any supported versions of Kubernetes. To achieve this, Dapr begins by deploying the following Kubernetes services, which provide first-class integration to make running applications with Dapr easy.
| Kubernetes services | Description |
| ------------------- | ----------- |
| `dapr-operator` | Manages [component]({{< ref components >}}) updates and Kubernetes services endpoints for Dapr (state stores, pub/subs, etc.) |
| Kubernetes services | Description |
|-------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `dapr-operator` | Manages [component]({{< ref components >}}) updates and Kubernetes services endpoints for Dapr (state stores, pub/subs, etc.) |
| `dapr-sidecar-injector` | Injects Dapr into [annotated](#adding-dapr-to-a-kubernetes-deployment) deployment pods and adds the environment variables `DAPR_HTTP_PORT` and `DAPR_GRPC_PORT` to enable user-defined applications to easily communicate with Dapr without hard-coding Dapr port values. |
| `dapr-placement` | Used for [actors]({{< ref actors >}}) only. Creates mapping tables that map actor instances to pods |
| `dapr-sentry` | Manages mTLS between services and acts as a certificate authority. For more information read the [security overview]({{< ref "security-concept.md" >}}) |
| `dapr-placement` | Used for [actors]({{< ref actors >}}) only. Creates mapping tables that map actor instances to pods |
| `dapr-sentry` | Manages mTLS between services and acts as a certificate authority. For more information read the [security overview]({{< ref "security-concept.md" >}}) |
| `dapr-scheduler` | Provides distributed job scheduling capabilities used by the Jobs API, Workflow API, and Actor Reminders |
<img src="/images/overview-kubernetes.png" width=1000>

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@ -95,19 +95,40 @@ For a new Dapr deployment, HA mode can be set with both:
For an existing Dapr deployment, [you can enable HA mode in a few extra steps]({{< ref "#enabling-high-availability-in-an-existing-dapr-deployment" >}}).
### Individual service HA Helm configuration
You can configure HA mode via Helm across all services by setting the `global.ha.enabled` flag to `true`. By default, `--set global.ha.enabled=true` is fully respected and cannot be overridden, making it impossible to simultaneously have either the placement or scheduler service as a single instance.
> **Note:** HA for scheduler and placement services is not the default setting.
To scale scheduler and placement to three instances independently of the `global.ha.enabled` flag, set `global.ha.enabled` to `false` and `dapr_scheduler.ha` and `dapr_placement.ha` to `true`. For example:
```bash
helm upgrade --install dapr dapr/dapr \
--version={{% dapr-latest-version short="true" %}} \
--namespace dapr-system \
--create-namespace \
--set global.ha.enabled=false \
--set dapr_scheduler.ha=true \
--set dapr_placement.ha=true \
--wait
```
## Setting cluster critical priority class name for control plane services
In some scenarios, nodes may have memory and/or cpu pressure and the Dapr control plane pods might get selected
for eviction. To prevent this, you can set a critical priority class name for the Dapr control plane pods. This ensures that
the Dapr control plane pods are not evicted unless all other pods with lower priority are evicted.
It's particularly important to protect the Dapr control plane components from eviction, especially the Scheduler service. When Schedulers are rescheduled or restarted, it can be highly disruptive to inflight jobs, potentially causing them to fire duplicate times. To prevent such disruptions, you should ensure the Dapr control plane components have a higher priority class than your application workloads.
Learn more about [Protecting Mission-Critical Pods](https://kubernetes.io/blog/2023/01/12/protect-mission-critical-pods-priorityclass/).
There are two built-in critical priority classes in Kubernetes:
- `system-cluster-critical`
- `system-node-critical` (highest priority)
It's recommended to set the `priorityClassName` to `system-cluster-critical` for the Dapr control plane pods.
It's recommended to set the `priorityClassName` to `system-cluster-critical` for the Dapr control plane pods. If you have your own custom priority classes for your applications, ensure they have a lower priority value than the one assigned to the Dapr control plane to maintain system stability and prevent disruption of core Dapr services.
For a new Dapr control plane deployment, the `system-cluster-critical` priority class mode can be set via the helm value `global.priorityClassName`.
@ -136,7 +157,6 @@ spec:
values: [system-cluster-critical]
```
## Deploy Dapr with Helm
[Visit the full guide on deploying Dapr with Helm]({{< ref "kubernetes-deploy.md#install-with-helm-advanced" >}}).

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@ -72,7 +72,7 @@ spec:
## Configuring metrics for error codes
You can enable additional metrics for [Dapr API error codes](https://docs.dapr.io/reference/api/error_codes/) by setting `spec.metrics.recordErrorCodes` to `true`. Dapr APIs which communicate back to their caller may return standardized error codes. As described in the [Dapr development docs](https://github.com/dapr/dapr/blob/master/docs/development/dapr-metrics.md), a new metric called `error_code_total` is recorded, which allows monitoring of error codes triggered by application, code, and category. See [the `errorcodes` package](https://github.com/dapr/dapr/blob/master/pkg/messages/errorcodes/errorcodes.go) for specific codes and categories.
You can enable additional metrics for [Dapr API error codes](https://docs.dapr.io/reference/api/error_codes/) by setting `spec.metrics.recordErrorCodes` to `true`. Dapr APIs which communicate back to their caller may return standardized error codes. [A new metric called `error_code_total` is recorded]({{< ref errors-overview.md >}}), which allows monitoring of error codes triggered by application, code, and category. See [the `errorcodes` package](https://github.com/dapr/dapr/blob/master/pkg/messages/errorcodes/errorcodes.go) for specific codes and categories.
Example configuration:
```yaml

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@ -63,7 +63,7 @@ You must propagate the headers from `service A` to `service B`. For example: `In
##### Pub/sub messages
Dapr generates the trace headers in the published message topic. These trace headers are propagated to any services listening on that topic.
Dapr generates the trace headers in the published message topic. For `rawPayload` messages, it is possible to specify the `traceparent` header to propagate the tracing information. These trace headers are propagated to any services listening on that topic.
#### Propagating multiple different service calls

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@ -1,330 +0,0 @@
---
type: docs
title: "Resiliency policies"
linkTitle: "Policies"
weight: 200
description: "Configure resiliency policies for timeouts, retries, and circuit breakers"
---
Define timeouts, retries, and circuit breaker policies under `policies`. Each policy is given a name so you can refer to them from the `targets` section in the resiliency spec.
> Note: Dapr offers default retries for specific APIs. [See here]({{< ref "#overriding-default-retries" >}}) to learn how you can overwrite default retry logic with user defined retry policies.
## Timeouts
Timeouts are optional policies that can be used to early-terminate long-running operations. If you've exceeded a timeout duration:
- The operation in progress is terminated (if possible).
- An error is returned.
Valid values are of the form accepted by Go's [time.ParseDuration](https://pkg.go.dev/time#ParseDuration), for example: `15s`, `2m`, `1h30m`. Timeouts have no set maximum value.
Example:
```yaml
spec:
policies:
# Timeouts are simple named durations.
timeouts:
general: 5s
important: 60s
largeResponse: 10s
```
If you don't specify a timeout value, the policy does not enforce a time and defaults to whatever you set up per the request client.
## Retries
With `retries`, you can define a retry strategy for failed operations, including requests failed due to triggering a defined timeout or circuit breaker policy.
{{% alert title="Pub/sub component retries vs inbound resiliency" color="warning" %}}
Each [pub/sub component]({{< ref supported-pubsub >}}) has its own built-in retry behaviors. Explicity applying a Dapr resiliency policy doesn't override these implicit retry policies. Rather, the resiliency policy augments the built-in retry, which can cause repetitive clustering of messages.
{{% /alert %}}
The following retry options are configurable:
| Retry option | Description |
| ------------ | ----------- |
| `policy` | Determines the back-off and retry interval strategy. Valid values are `constant` and `exponential`.<br/>Defaults to `constant`. |
| `duration` | Determines the time interval between retries. Only applies to the `constant` policy.<br/>Valid values are of the form `200ms`, `15s`, `2m`, etc.<br/> Defaults to `5s`.|
| `maxInterval` | Determines the maximum interval between retries to which the `exponential` back-off policy can grow.<br/>Additional retries always occur after a duration of `maxInterval`. Defaults to `60s`. Valid values are of the form `5s`, `1m`, `1m30s`, etc |
| `maxRetries` | The maximum number of retries to attempt. <br/>`-1` denotes an unlimited number of retries, while `0` means the request will not be retried (essentially behaving as if the retry policy were not set).<br/>Defaults to `-1`. |
| `matching.httpStatusCodes` | Optional: a comma-separated string of HTTP status codes or code ranges to retry. Status codes not listed are not retried.<br/>Valid values: 100-599, [Reference](https://developer.mozilla.org/en-US/docs/Web/HTTP/Status)<br/>Format: `<code>` or range `<start>-<end>`<br/>Example: "429,501-503"<br/>Default: empty string `""` or field is not set. Retries on all HTTP errors. |
| `matching.gRPCStatusCodes` | Optional: a comma-separated string of gRPC status codes or code ranges to retry. Status codes not listed are not retried.<br/>Valid values: 0-16, [Reference](https://grpc.io/docs/guides/status-codes/)<br/>Format: `<code>` or range `<start>-<end>`<br/>Example: "1,501-503"<br/>Default: empty string `""` or field is not set. Retries on all gRPC errors. |
{{% alert title="httpStatusCodes and gRPCStatusCodes format" color="warning" %}}
The field values should follow the format as specified in the field description or in the "Example 2" below.
An incorrectly formatted value will produce an error log ("Could not read resiliency policy") and `daprd` startup sequence will proceed.
{{% /alert %}}
The exponential back-off window uses the following formula:
```
BackOffDuration = PreviousBackOffDuration * (Random value from 0.5 to 1.5) * 1.5
if BackOffDuration > maxInterval {
BackoffDuration = maxInterval
}
```
Example:
```yaml
spec:
policies:
# Retries are named templates for retry configurations and are instantiated for life of the operation.
retries:
pubsubRetry:
policy: constant
duration: 5s
maxRetries: 10
retryForever:
policy: exponential
maxInterval: 15s
maxRetries: -1 # Retry indefinitely
```
Example 2:
```yaml
spec:
policies:
retries:
retry5xxOnly:
policy: constant
duration: 5s
maxRetries: 3
matching:
httpStatusCodes: "429,500-599" # retry the HTTP status codes in this range. All others are not retried.
gRPCStatusCodes: "1-4,8-11,13,14" # retry gRPC status codes in these ranges and separate single codes.
```
## Circuit Breakers
Circuit Breaker (CB) policies are used when other applications/services/components are experiencing elevated failure rates. CBs monitor the requests and shut off all traffic to the impacted service when a certain criteria is met ("open" state). By doing this, CBs give the service time to recover from their outage instead of flooding it with events. The CB can also allow partial traffic through to see if the system has healed ("half-open" state). Once requests resume being successful, the CB gets into "closed" state and allows traffic to completely resume.
| Retry option | Description |
| ------------ | ----------- |
| `maxRequests` | The maximum number of requests allowed to pass through when the CB is half-open (recovering from failure). Defaults to `1`. |
| `interval` | The cyclical period of time used by the CB to clear its internal counts. If set to 0 seconds, this never clears. Defaults to `0s`. |
| `timeout` | The period of the open state (directly after failure) until the CB switches to half-open. Defaults to `60s`. |
| `trip` | A [Common Expression Language (CEL)](https://github.com/google/cel-spec) statement that is evaluated by the CB. When the statement evaluates to true, the CB trips and becomes open. Defaults to `consecutiveFailures > 5`. |
Example:
```yaml
spec:
policies:
circuitBreakers:
pubsubCB:
maxRequests: 1
interval: 8s
timeout: 45s
trip: consecutiveFailures > 8
```
## Overriding default retries
Dapr provides default retries for any unsuccessful request, such as failures and transient errors. Within a resiliency spec, you have the option to override Dapr's default retry logic by defining policies with reserved, named keywords. For example, defining a policy with the name `DaprBuiltInServiceRetries`, overrides the default retries for failures between sidecars via service-to-service requests. Policy overrides are not applied to specific targets.
> Note: Although you can override default values with more robust retries, you cannot override with lesser values than the provided default value, or completely remove default retries. This prevents unexpected downtime.
Below is a table that describes Dapr's default retries and the policy keywords to override them:
| Capability | Override Keyword | Default Retry Behavior | Description |
| ------------------ | ------------------------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------- |
| Service Invocation | DaprBuiltInServiceRetries | Per call retries are performed with a backoff interval of 1 second, up to a threshold of 3 times. | Sidecar-to-sidecar requests (a service invocation method call) that fail and result in a gRPC code `Unavailable` or `Unauthenticated` |
| Actors | DaprBuiltInActorRetries | Per call retries are performed with a backoff interval of 1 second, up to a threshold of 3 times. | Sidecar-to-sidecar requests (an actor method call) that fail and result in a gRPC code `Unavailable` or `Unauthenticated` |
| Actor Reminders | DaprBuiltInActorReminderRetries | Per call retries are performed with an exponential backoff with an initial interval of 500ms, up to a maximum of 60s for a duration of 15mins | Requests that fail to persist an actor reminder to a state store |
| Initialization Retries | DaprBuiltInInitializationRetries | Per call retries are performed 3 times with an exponential backoff, an initial interval of 500ms and for a duration of 10s | Failures when making a request to an application to retrieve a given spec. For example, failure to retrieve a subscription, component or resiliency specification |
The resiliency spec example below shows overriding the default retries for _all_ service invocation requests by using the reserved, named keyword 'DaprBuiltInServiceRetries'.
Also defined is a retry policy called 'retryForever' that is only applied to the appB target. appB uses the 'retryForever' retry policy, while all other application service invocation retry failures use the overridden 'DaprBuiltInServiceRetries' default policy.
```yaml
spec:
policies:
retries:
DaprBuiltInServiceRetries: # Overrides default retry behavior for service-to-service calls
policy: constant
duration: 5s
maxRetries: 10
retryForever: # A user defined retry policy replaces default retries. Targets rely solely on the applied policy.
policy: exponential
maxInterval: 15s
maxRetries: -1 # Retry indefinitely
targets:
apps:
appB: # app-id of the target service
retry: retryForever
```
## Setting default policies
In resiliency you can set default policies, which have a broad scope. This is done through reserved keywords that let Dapr know when to apply the policy. There are 3 default policy types:
- `DefaultRetryPolicy`
- `DefaultTimeoutPolicy`
- `DefaultCircuitBreakerPolicy`
If these policies are defined, they are used for every operation to a service, application, or component. They can also be modified to be more specific through the appending of additional keywords. The specific policies follow the following pattern, `Default%sRetryPolicy`, `Default%sTimeoutPolicy`, and `Default%sCircuitBreakerPolicy`. Where the `%s` is replaced by a target of the policy.
Below is a table of all possible default policy keywords and how they translate into a policy name.
| Keyword | Target Operation | Example Policy Name |
| -------------------------------- | ---------------------------------------------------- | ----------------------------------------------------------- |
| `App` | Service invocation. | `DefaultAppRetryPolicy` |
| `Actor` | Actor invocation. | `DefaultActorTimeoutPolicy` |
| `Component` | All component operations. | `DefaultComponentCircuitBreakerPolicy` |
| `ComponentInbound` | All inbound component operations. | `DefaultComponentInboundRetryPolicy` |
| `ComponentOutbound` | All outbound component operations. | `DefaultComponentOutboundTimeoutPolicy` |
| `StatestoreComponentOutbound` | All statestore component operations. | `DefaultStatestoreComponentOutboundCircuitBreakerPolicy` |
| `PubsubComponentOutbound` | All outbound pubusub (publish) component operations. | `DefaultPubsubComponentOutboundRetryPolicy` |
| `PubsubComponentInbound` | All inbound pubsub (subscribe) component operations. | `DefaultPubsubComponentInboundTimeoutPolicy` |
| `BindingComponentOutbound` | All outbound binding (invoke) component operations. | `DefaultBindingComponentOutboundCircuitBreakerPolicy` |
| `BindingComponentInbound` | All inbound binding (read) component operations. | `DefaultBindingComponentInboundRetryPolicy` |
| `SecretstoreComponentOutbound` | All secretstore component operations. | `DefaultSecretstoreComponentTimeoutPolicy` |
| `ConfigurationComponentOutbound` | All configuration component operations. | `DefaultConfigurationComponentOutboundCircuitBreakerPolicy` |
| `LockComponentOutbound` | All lock component operations. | `DefaultLockComponentOutboundRetryPolicy` |
### Policy hierarchy resolution
Default policies are applied if the operation being executed matches the policy type and if there is no more specific policy targeting it. For each target type (app, actor, and component), the policy with the highest priority is a Named Policy, one that targets that construct specifically.
If none exists, the policies are applied from most specific to most broad.
#### How default policies and built-in retries work together
In the case of the [built-in retries]({{< ref "policies.md#Override Default Retries" >}}), default policies do not stop the built-in retry policies from running. Both are used together but only under specific circumstances.
For service and actor invocation, the built-in retries deal specifically with issues connecting to the remote sidecar (when needed). As these are important to the stability of the Dapr runtime, they are not disabled **unless** a named policy is specifically referenced for an operation. In some instances, there may be additional retries from both the built-in retry and the default retry policy, but this prevents an overly weak default policy from reducing the sidecar's availability/success rate.
Policy resolution hierarchy for applications, from most specific to most broad:
1. Named Policies in App Targets
2. Default App Policies / Built-In Service Retries
3. Default Policies / Built-In Service Retries
Policy resolution hierarchy for actors, from most specific to most broad:
1. Named Policies in Actor Targets
2. Default Actor Policies / Built-In Actor Retries
3. Default Policies / Built-In Actor Retries
Policy resolution hierarchy for components, from most specific to most broad:
1. Named Policies in Component Targets
2. Default Component Type + Component Direction Policies / Built-In Actor Reminder Retries (if applicable)
3. Default Component Direction Policies / Built-In Actor Reminder Retries (if applicable)
4. Default Component Policies / Built-In Actor Reminder Retries (if applicable)
5. Default Policies / Built-In Actor Reminder Retries (if applicable)
As an example, take the following solution consisting of three applications, three components and two actor types:
Applications:
- AppA
- AppB
- AppC
Components:
- Redis Pubsub: pubsub
- Redis statestore: statestore
- CosmosDB Statestore: actorstore
Actors:
- EventActor
- SummaryActor
Below is policy that uses both default and named policies as applies these to the targets.
```yaml
spec:
policies:
retries:
# Global Retry Policy
DefaultRetryPolicy:
policy: constant
duration: 1s
maxRetries: 3
# Global Retry Policy for Apps
DefaultAppRetryPolicy:
policy: constant
duration: 100ms
maxRetries: 5
# Global Retry Policy for Apps
DefaultActorRetryPolicy:
policy: exponential
maxInterval: 15s
maxRetries: 10
# Global Retry Policy for Inbound Component operations
DefaultComponentInboundRetryPolicy:
policy: constant
duration: 5s
maxRetries: 5
# Global Retry Policy for Statestores
DefaultStatestoreComponentOutboundRetryPolicy:
policy: exponential
maxInterval: 60s
maxRetries: -1
# Named policy
fastRetries:
policy: constant
duration: 10ms
maxRetries: 3
# Named policy
retryForever:
policy: exponential
maxInterval: 10s
maxRetries: -1
targets:
apps:
appA:
retry: fastRetries
appB:
retry: retryForever
actors:
EventActor:
retry: retryForever
components:
actorstore:
retry: fastRetries
```
The table below is a break down of which policies are applied when attempting to call the various targets in this solution.
| Target | Policy Used |
| ------------------ | ----------------------------------------------- |
| AppA | fastRetries |
| AppB | retryForever |
| AppC | DefaultAppRetryPolicy / DaprBuiltInActorRetries |
| pubsub - Publish | DefaultRetryPolicy |
| pubsub - Subscribe | DefaultComponentInboundRetryPolicy |
| statestore | DefaultStatestoreComponentOutboundRetryPolicy |
| actorstore | fastRetries |
| EventActor | retryForever |
| SummaryActor | DefaultActorRetryPolicy |
## Next steps
Try out one of the Resiliency quickstarts:
- [Resiliency: Service-to-service]({{< ref resiliency-serviceinvo-quickstart.md >}})
- [Resiliency: State Management]({{< ref resiliency-state-quickstart.md >}})

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---
type: docs
title: "Resiliency policies"
linkTitle: "Policies"
weight: 200
description: "Configure resiliency policies for timeouts, retries, and circuit breakers"
---
Define timeouts, retries, and circuit breaker policies under `policies`. Each policy is given a name so you can refer to them from the [`targets` section in the resiliency spec]({{< ref targets.md >}}).

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---
type: docs
title: "Circuit breaker resiliency policies"
linkTitle: "Circuit breakers"
weight: 30
description: "Configure resiliency policies for circuit breakers"
---
Circuit breaker policies are used when other applications/services/components are experiencing elevated failure rates. Circuit breakers reduce load by monitoring the requests and shutting off all traffic to the impacted service when a certain criteria is met.
After a certain number of requests fail, circuit breakers "trip" or open to prevent cascading failures. By doing this, circuit breakers give the service time to recover from their outage instead of flooding it with events.
The circuit breaker can also enter a “half-open” state, allowing partial traffic through to see if the system has healed.
Once requests resume being successful, the circuit breaker gets into "closed" state and allows traffic to completely resume.
## Circuit breaker policy format
```yaml
spec:
policies:
circuitBreakers:
pubsubCB:
maxRequests: 1
interval: 8s
timeout: 45s
trip: consecutiveFailures > 8
```
## Spec metadata
| Retry option | Description |
| ------------ | ----------- |
| `maxRequests` | The maximum number of requests allowed to pass through when the circuit breaker is half-open (recovering from failure). Defaults to `1`. |
| `interval` | The cyclical period of time used by the circuit breaker to clear its internal counts. If set to 0 seconds, this never clears. Defaults to `0s`. |
| `timeout` | The period of the open state (directly after failure) until the circuit breaker switches to half-open. Defaults to `60s`. |
| `trip` | A [Common Expression Language (CEL)](https://github.com/google/cel-spec) statement that is evaluated by the circuit breaker. When the statement evaluates to true, the circuit breaker trips and becomes open. Defaults to `consecutiveFailures > 5`. Other possible values are `requests` and `totalFailures` where `requests` represents the number of either successful or failed calls before the circuit opens and `totalFailures` represents the total (not necessarily consecutive) number of failed attempts before the circuit opens. Example: `requests > 5` and `totalFailures >3`.|
## Next steps
- [Learn more about default resiliency policies]({{< ref default-policies.md >}})
- Learn more about:
- [Retry policies]({{< ref retries-overview.md >}})
- [Timeout policies]({{< ref timeouts.md >}})
## Related links
Try out one of the Resiliency quickstarts:
- [Resiliency: Service-to-service]({{< ref resiliency-serviceinvo-quickstart.md >}})
- [Resiliency: State Management]({{< ref resiliency-state-quickstart.md >}})

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---
type: docs
title: "Default resiliency policies"
linkTitle: "Default policies"
weight: 40
description: "Learn more about the default resiliency policies for timeouts, retries, and circuit breakers"
---
In resiliency, you can set default policies, which have a broad scope. This is done through reserved keywords that let Dapr know when to apply the policy. There are 3 default policy types:
- `DefaultRetryPolicy`
- `DefaultTimeoutPolicy`
- `DefaultCircuitBreakerPolicy`
If these policies are defined, they are used for every operation to a service, application, or component. They can also be modified to be more specific through the appending of additional keywords. The specific policies follow the following pattern, `Default%sRetryPolicy`, `Default%sTimeoutPolicy`, and `Default%sCircuitBreakerPolicy`. Where the `%s` is replaced by a target of the policy.
Below is a table of all possible default policy keywords and how they translate into a policy name.
| Keyword | Target Operation | Example Policy Name |
| -------------------------------- | ---------------------------------------------------- | ----------------------------------------------------------- |
| `App` | Service invocation. | `DefaultAppRetryPolicy` |
| `Actor` | Actor invocation. | `DefaultActorTimeoutPolicy` |
| `Component` | All component operations. | `DefaultComponentCircuitBreakerPolicy` |
| `ComponentInbound` | All inbound component operations. | `DefaultComponentInboundRetryPolicy` |
| `ComponentOutbound` | All outbound component operations. | `DefaultComponentOutboundTimeoutPolicy` |
| `StatestoreComponentOutbound` | All statestore component operations. | `DefaultStatestoreComponentOutboundCircuitBreakerPolicy` |
| `PubsubComponentOutbound` | All outbound pubusub (publish) component operations. | `DefaultPubsubComponentOutboundRetryPolicy` |
| `PubsubComponentInbound` | All inbound pubsub (subscribe) component operations. | `DefaultPubsubComponentInboundTimeoutPolicy` |
| `BindingComponentOutbound` | All outbound binding (invoke) component operations. | `DefaultBindingComponentOutboundCircuitBreakerPolicy` |
| `BindingComponentInbound` | All inbound binding (read) component operations. | `DefaultBindingComponentInboundRetryPolicy` |
| `SecretstoreComponentOutbound` | All secretstore component operations. | `DefaultSecretstoreComponentTimeoutPolicy` |
| `ConfigurationComponentOutbound` | All configuration component operations. | `DefaultConfigurationComponentOutboundCircuitBreakerPolicy` |
| `LockComponentOutbound` | All lock component operations. | `DefaultLockComponentOutboundRetryPolicy` |
## Policy hierarchy resolution
Default policies are applied if the operation being executed matches the policy type and if there is no more specific policy targeting it. For each target type (app, actor, and component), the policy with the highest priority is a Named Policy, one that targets that construct specifically.
If none exists, the policies are applied from most specific to most broad.
## How default policies and built-in retries work together
In the case of the [built-in retries]({{< ref override-default-retries.md >}}), default policies do not stop the built-in retry policies from running. Both are used together but only under specific circumstances.
For service and actor invocation, the built-in retries deal specifically with issues connecting to the remote sidecar (when needed). As these are important to the stability of the Dapr runtime, they are not disabled **unless** a named policy is specifically referenced for an operation. In some instances, there may be additional retries from both the built-in retry and the default retry policy, but this prevents an overly weak default policy from reducing the sidecar's availability/success rate.
Policy resolution hierarchy for applications, from most specific to most broad:
1. Named Policies in App Targets
2. Default App Policies / Built-In Service Retries
3. Default Policies / Built-In Service Retries
Policy resolution hierarchy for actors, from most specific to most broad:
1. Named Policies in Actor Targets
2. Default Actor Policies / Built-In Actor Retries
3. Default Policies / Built-In Actor Retries
Policy resolution hierarchy for components, from most specific to most broad:
1. Named Policies in Component Targets
2. Default Component Type + Component Direction Policies / Built-In Actor Reminder Retries (if applicable)
3. Default Component Direction Policies / Built-In Actor Reminder Retries (if applicable)
4. Default Component Policies / Built-In Actor Reminder Retries (if applicable)
5. Default Policies / Built-In Actor Reminder Retries (if applicable)
As an example, take the following solution consisting of three applications, three components and two actor types:
Applications:
- AppA
- AppB
- AppC
Components:
- Redis Pubsub: pubsub
- Redis statestore: statestore
- CosmosDB Statestore: actorstore
Actors:
- EventActor
- SummaryActor
Below is policy that uses both default and named policies as applies these to the targets.
```yaml
spec:
policies:
retries:
# Global Retry Policy
DefaultRetryPolicy:
policy: constant
duration: 1s
maxRetries: 3
# Global Retry Policy for Apps
DefaultAppRetryPolicy:
policy: constant
duration: 100ms
maxRetries: 5
# Global Retry Policy for Apps
DefaultActorRetryPolicy:
policy: exponential
maxInterval: 15s
maxRetries: 10
# Global Retry Policy for Inbound Component operations
DefaultComponentInboundRetryPolicy:
policy: constant
duration: 5s
maxRetries: 5
# Global Retry Policy for Statestores
DefaultStatestoreComponentOutboundRetryPolicy:
policy: exponential
maxInterval: 60s
maxRetries: -1
# Named policy
fastRetries:
policy: constant
duration: 10ms
maxRetries: 3
# Named policy
retryForever:
policy: exponential
maxInterval: 10s
maxRetries: -1
targets:
apps:
appA:
retry: fastRetries
appB:
retry: retryForever
actors:
EventActor:
retry: retryForever
components:
actorstore:
retry: fastRetries
```
The table below is a break down of which policies are applied when attempting to call the various targets in this solution.
| Target | Policy Used |
| ------------------ | ----------------------------------------------- |
| AppA | fastRetries |
| AppB | retryForever |
| AppC | DefaultAppRetryPolicy / DaprBuiltInActorRetries |
| pubsub - Publish | DefaultRetryPolicy |
| pubsub - Subscribe | DefaultComponentInboundRetryPolicy |
| statestore | DefaultStatestoreComponentOutboundRetryPolicy |
| actorstore | fastRetries |
| EventActor | retryForever |
| SummaryActor | DefaultActorRetryPolicy |
## Next steps
[Learn how to override default retry policies.]({{< ref override-default-retries.md >}})
## Related links
Try out one of the Resiliency quickstarts:
- [Resiliency: Service-to-service]({{< ref resiliency-serviceinvo-quickstart.md >}})
- [Resiliency: State Management]({{< ref resiliency-state-quickstart.md >}})

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---
type: docs
title: "Retry and back-off resiliency policies"
linkTitle: "Retries"
weight: 20
description: "Configure resiliency policies for retries and back-offs"
---

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---
type: docs
title: "Override default retry resiliency policies"
linkTitle: "Override default retries"
weight: 20
description: "Learn how to override the default retry resiliency policies for specific APIs"
---
Dapr provides [default retries]({{< ref default-policies.md >}}) for any unsuccessful request, such as failures and transient errors. Within a resiliency spec, you have the option to override Dapr's default retry logic by defining policies with reserved, named keywords. For example, defining a policy with the name `DaprBuiltInServiceRetries`, overrides the default retries for failures between sidecars via service-to-service requests. Policy overrides are not applied to specific targets.
> Note: Although you can override default values with more robust retries, you cannot override with lesser values than the provided default value, or completely remove default retries. This prevents unexpected downtime.
Below is a table that describes Dapr's default retries and the policy keywords to override them:
| Capability | Override Keyword | Default Retry Behavior | Description |
| ------------------ | ------------------------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------- |
| Service Invocation | DaprBuiltInServiceRetries | Per call retries are performed with a backoff interval of 1 second, up to a threshold of 3 times. | Sidecar-to-sidecar requests (a service invocation method call) that fail and result in a gRPC code `Unavailable` or `Unauthenticated` |
| Actors | DaprBuiltInActorRetries | Per call retries are performed with a backoff interval of 1 second, up to a threshold of 3 times. | Sidecar-to-sidecar requests (an actor method call) that fail and result in a gRPC code `Unavailable` or `Unauthenticated` |
| Actor Reminders | DaprBuiltInActorReminderRetries | Per call retries are performed with an exponential backoff with an initial interval of 500ms, up to a maximum of 60s for a duration of 15mins | Requests that fail to persist an actor reminder to a state store |
| Initialization Retries | DaprBuiltInInitializationRetries | Per call retries are performed 3 times with an exponential backoff, an initial interval of 500ms and for a duration of 10s | Failures when making a request to an application to retrieve a given spec. For example, failure to retrieve a subscription, component or resiliency specification |
The resiliency spec example below shows overriding the default retries for _all_ service invocation requests by using the reserved, named keyword 'DaprBuiltInServiceRetries'.
Also defined is a retry policy called 'retryForever' that is only applied to the appB target. appB uses the 'retryForever' retry policy, while all other application service invocation retry failures use the overridden 'DaprBuiltInServiceRetries' default policy.
```yaml
spec:
policies:
retries:
DaprBuiltInServiceRetries: # Overrides default retry behavior for service-to-service calls
policy: constant
duration: 5s
maxRetries: 10
retryForever: # A user defined retry policy replaces default retries. Targets rely solely on the applied policy.
policy: exponential
maxInterval: 15s
maxRetries: -1 # Retry indefinitely
targets:
apps:
appB: # app-id of the target service
retry: retryForever
```
## Related links
Try out one of the Resiliency quickstarts:
- [Resiliency: Service-to-service]({{< ref resiliency-serviceinvo-quickstart.md >}})
- [Resiliency: State Management]({{< ref resiliency-state-quickstart.md >}})

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---
type: docs
title: "Retry resiliency policies"
linkTitle: "Overview"
weight: 10
description: "Configure resiliency policies for retries"
---
Requests can fail due to transient errors, like encountering network congestion, reroutes to overloaded instances, and more. Sometimes, requests can fail due to other resiliency policies set in place, like triggering a defined timeout or circuit breaker policy.
In these cases, configuring `retries` can either:
- Send the same request to a different instance, or
- Retry sending the request after the condition has cleared.
Retries and timeouts work together, with timeouts ensuring your system fails fast when needed, and retries recovering from temporary glitches.
Dapr provides [default resiliency policies]({{< ref default-policies.md >}}), which you can [overwrite with user-defined retry policies.]({{< ref override-default-retries.md >}})
{{% alert title="Important" color="warning" %}}
Each [pub/sub component]({{< ref supported-pubsub >}}) has its own built-in retry behaviors. Explicity applying a Dapr resiliency policy doesn't override these implicit retry policies. Rather, the resiliency policy augments the built-in retry, which can cause repetitive clustering of messages.
{{% /alert %}}
## Retry policy format
**Example 1**
```yaml
spec:
policies:
# Retries are named templates for retry configurations and are instantiated for life of the operation.
retries:
pubsubRetry:
policy: constant
duration: 5s
maxRetries: 10
retryForever:
policy: exponential
maxInterval: 15s
maxRetries: -1 # Retry indefinitely
```
**Example 2**
```yaml
spec:
policies:
retries:
retry5xxOnly:
policy: constant
duration: 5s
maxRetries: 3
matching:
httpStatusCodes: "429,500-599" # retry the HTTP status codes in this range. All others are not retried.
gRPCStatusCodes: "1-4,8-11,13,14" # retry gRPC status codes in these ranges and separate single codes.
```
## Spec metadata
The following retry options are configurable:
| Retry option | Description |
| ------------ | ----------- |
| `policy` | Determines the back-off and retry interval strategy. Valid values are `constant` and `exponential`.<br/>Defaults to `constant`. |
| `duration` | Determines the time interval between retries. Only applies to the `constant` policy.<br/>Valid values are of the form `200ms`, `15s`, `2m`, etc.<br/> Defaults to `5s`.|
| `maxInterval` | Determines the maximum interval between retries to which the [`exponential` back-off policy](#exponential-back-off-policy) can grow.<br/>Additional retries always occur after a duration of `maxInterval`. Defaults to `60s`. Valid values are of the form `5s`, `1m`, `1m30s`, etc |
| `maxRetries` | The maximum number of retries to attempt. <br/>`-1` denotes an unlimited number of retries, while `0` means the request will not be retried (essentially behaving as if the retry policy were not set).<br/>Defaults to `-1`. |
| `matching.httpStatusCodes` | Optional: a comma-separated string of [HTTP status codes or code ranges to retry](#retry-status-codes). Status codes not listed are not retried.<br/>Valid values: 100-599, [Reference](https://developer.mozilla.org/en-US/docs/Web/HTTP/Status)<br/>Format: `<code>` or range `<start>-<end>`<br/>Example: "429,501-503"<br/>Default: empty string `""` or field is not set. Retries on all HTTP errors. |
| `matching.gRPCStatusCodes` | Optional: a comma-separated string of [gRPC status codes or code ranges to retry](#retry-status-codes). Status codes not listed are not retried.<br/>Valid values: 0-16, [Reference](https://grpc.io/docs/guides/status-codes/)<br/>Format: `<code>` or range `<start>-<end>`<br/>Example: "4,8,14"<br/>Default: empty string `""` or field is not set. Retries on all gRPC errors. |
## Exponential back-off policy
The exponential back-off window uses the following formula:
```
BackOffDuration = PreviousBackOffDuration * (Random value from 0.5 to 1.5) * 1.5
if BackOffDuration > maxInterval {
BackoffDuration = maxInterval
}
```
## Retry status codes
When applications span multiple services, especially on dynamic environments like Kubernetes, services can disappear for all kinds of reasons and network calls can start hanging. Status codes provide a glimpse into our operations and where they may have failed in production.
### HTTP
The following table includes some examples of HTTP status codes you may receive and whether you should or should not retry certain operations.
| HTTP Status Code | Retry Recommended? | Description |
| ------------------------- | ---------------------- | ---------------------------- |
| 404 Not Found | ❌ No | The resource doesn't exist. |
| 400 Bad Request | ❌ No | Your request is invalid. |
| 401 Unauthorized | ❌ No | Try getting new credentials. |
| 408 Request Timeout | ✅ Yes | The server timed out waiting for the request. |
| 429 Too Many Requests | ✅ Yes | (Respect the `Retry-After` header, if present). |
| 500 Internal Server Error | ✅ Yes | The server encountered an unexpected condition. |
| 502 Bad Gateway | ✅ Yes | A gateway or proxy received an invalid response. |
| 503 Service Unavailable | ✅ Yes | Service might recover. |
| 504 Gateway Timeout | ✅ Yes | Temporary network issue. |
### gRPC
The following table includes some examples of gRPC status codes you may receive and whether you should or should not retry certain operations.
| gRPC Status Code | Retry Recommended? | Description |
| ------------------------- | ----------------------- | ---------------------------- |
| Code 1 CANCELLED | ❌ No | N/A |
| Code 3 INVALID_ARGUMENT | ❌ No | N/A |
| Code 4 DEADLINE_EXCEEDED | ✅ Yes | Retry with backoff |
| Code 5 NOT_FOUND | ❌ No | N/A |
| Code 8 RESOURCE_EXHAUSTED | ✅ Yes | Retry with backoff |
| Code 14 UNAVAILABLE | ✅ Yes | Retry with backoff |
### Retry filter based on status codes
The retry filter enables granular control over retry policies by allowing users to specify HTTP and gRPC status codes or ranges for which retries should apply.
```yml
spec:
policies:
retries:
retry5xxOnly:
# ...
matching:
httpStatusCodes: "429,500-599" # retry the HTTP status codes in this range. All others are not retried.
gRPCStatusCodes: "4,8-11,13,14" # retry gRPC status codes in these ranges and separate single codes.
```
{{% alert title="Note" color="primary" %}}
Field values for status codes must follow the format specified above. An incorrectly formatted value produces an error log ("Could not read resiliency policy") and the `daprd` startup sequence will proceed.
{{% /alert %}}
## Demo
Watch a demo presented during [Diagrid's Dapr v1.15 celebration](https://www.diagrid.io/videos/dapr-1-15-deep-dive) to see how to set retry status code filters using Diagrid Conductor
<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/NTnwoDhHIcQ?si=8k1IhRazjyrIJE3P&amp;start=4565" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
## Next steps
- [Learn how to override default retry policies for specific APIs.]({[< ref override-default-retries.md >]})
- [Learn how to target your retry policies from the resiliency spec.]({{< ref targets.md >}})
- Learn more about:
- [Timeout policies]({{< ref timeouts.md >}})
- [Circuit breaker policies]({{< ref circuit-breakers.md >}})
## Related links
Try out one of the Resiliency quickstarts:
- [Resiliency: Service-to-service]({{< ref resiliency-serviceinvo-quickstart.md >}})
- [Resiliency: State Management]({{< ref resiliency-state-quickstart.md >}})

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---
type: docs
title: "Timeout resiliency policies"
linkTitle: "Timeouts"
weight: 10
description: "Configure resiliency policies for timeouts"
---
Network calls can fail for many reasons, causing your application to wait indefinitely for responses. By setting a timeout duration, you can cut off those unresponsive services, freeing up resources to handle new requests.
Timeouts are optional policies that can be used to early-terminate long-running operations. Set a realistic timeout duration that reflects actual response times in production. If you've exceeded a timeout duration:
- The operation in progress is terminated (if possible).
- An error is returned.
## Timeout policy format
```yaml
spec:
policies:
# Timeouts are simple named durations.
timeouts:
timeoutName: timeout1
general: 5s
important: 60s
largeResponse: 10s
```
### Spec metadata
| Field | Details | Example |
| timeoutName | Name of the timeout policy | `timeout1` |
| general | Time duration for timeouts marked as "general". Uses Go's [time.ParseDuration](https://pkg.go.dev/time#ParseDuration) format. No set maximum value. | `15s`, `2m`, `1h30m` |
| important | Time duration for timeouts marked as "important". Uses Go's [time.ParseDuration](https://pkg.go.dev/time#ParseDuration) format. No set maximum value. | `15s`, `2m`, `1h30m` |
| largeResponse | Time duration for timeouts awaiting a large response. Uses Go's [time.ParseDuration](https://pkg.go.dev/time#ParseDuration) format. No set maximum value. | `15s`, `2m`, `1h30m` |
> If you don't specify a timeout value, the policy does not enforce a time and defaults to whatever you set up per the request client.
## Next steps
- [Learn more about default resiliency policies]({{< ref default-policies.md >}})
- Learn more about:
- [Retry policies]({{< ref retries-overview.md >}})
- [Circuit breaker policies]({{< ref circuit-breakers.md >}})
## Related links
Try out one of the Resiliency quickstarts:
- [Resiliency: Service-to-service]({{< ref resiliency-serviceinvo-quickstart.md >}})
- [Resiliency: State Management]({{< ref resiliency-state-quickstart.md >}})

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description: "Configure Dapr retries, timeouts, and circuit breakers"
---
Dapr provides a capability for defining and applying fault tolerance resiliency policies via a [resiliency spec]({{< ref "resiliency-overview.md#complete-example-policy" >}}). Resiliency specs are saved in the same location as components specs and are applied when the Dapr sidecar starts. The sidecar determines how to apply resiliency policies to your Dapr API calls. In self-hosted mode, the resiliency spec must be named `resiliency.yaml`. In Kubernetes Dapr finds the named resiliency specs used by your application. Within the resiliency spec, you can define policies for popular resiliency patterns, such as:
Dapr provides the capability for defining and applying fault tolerance resiliency policies via a [resiliency spec]({{< ref "resiliency-overview.md#complete-example-policy" >}}). Resiliency specs are saved in the same location as components specs and are applied when the Dapr sidecar starts. The sidecar determines how to apply resiliency policies to your Dapr API calls.
- **In self-hosted mode:** The resiliency spec must be named `resiliency.yaml`.
- **In Kubernetes:** Dapr finds the named resiliency specs used by your application.
- [Timeouts]({{< ref "policies.md#timeouts" >}})
- [Retries/back-offs]({{< ref "policies.md#retries" >}})
- [Circuit breakers]({{< ref "policies.md#circuit-breakers" >}})
## Policies
Policies can then be applied to [targets]({{< ref "targets.md" >}}), which include:
You can configure Dapr resiliency policies with the following parts:
- Metadata defining where the policy applies (like namespace and scope)
- Policies specifying the resiliency name and behaviors, like:
- [Timeouts]({{< ref timeouts.md >}})
- [Retries]({{< ref retries-overview.md >}})
- [Circuit breakers]({{< ref circuit-breakers.md >}})
- Targets determining which interactions these policies act on, including:
- [Apps]({{< ref "targets.md#apps" >}}) via service invocation
- [Components]({{< ref "targets.md#components" >}})
- [Actors]({{< ref "targets.md#actors" >}})
- [Apps]({{< ref "targets.md#apps" >}}) via service invocation
- [Components]({{< ref "targets.md#components" >}})
- [Actors]({{< ref "targets.md#actors" >}})
Once defined, you can apply this configuration to your local Dapr components directory, or to your Kubernetes cluster using:
Additionally, resiliency policies can be [scoped to specific apps]({{< ref "component-scopes.md#application-access-to-components-with-scopes" >}}).
```bash
kubectl apply -f <resiliency-spec-name>.yaml
```
## Demo video
Additionally, you can scope resiliency policies [to specific apps]({{< ref "component-scopes.md#application-access-to-components-with-scopes" >}}).
Learn more about [how to write resilient microservices with Dapr](https://youtu.be/uC-4Q5KFq98?si=JSUlCtcUNZLBM9rW).
<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/uC-4Q5KFq98?si=JSUlCtcUNZLBM9rW" title="YouTube video player" style="padding-bottom:25px;" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
> See [known limitations](#limitations).
## Resiliency policy structure
@ -166,7 +173,11 @@ spec:
circuitBreaker: pubsubCB
```
## Related links
## Limitations
- **Service invocation via gRPC:** Currently, resiliency policies are not supported for service invocation via gRPC.
## Demos
Watch this video for how to use [resiliency](https://www.youtube.com/watch?t=184&v=7D6HOU3Ms6g&feature=youtu.be):
@ -174,11 +185,20 @@ Watch this video for how to use [resiliency](https://www.youtube.com/watch?t=184
<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/7D6HOU3Ms6g?start=184" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</div>
Learn more about [how to write resilient microservices with Dapr](https://youtu.be/uC-4Q5KFq98?si=JSUlCtcUNZLBM9rW).
<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/uC-4Q5KFq98?si=JSUlCtcUNZLBM9rW" title="YouTube video player" style="padding-bottom:25px;" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
## Next steps
Learn more about resiliency policies and targets:
- [Policies]({{< ref "policies.md" >}})
- Policies
- [Timeouts]({{< ref "timeouts.md" >}})
- [Retries]({{< ref "retries-overview.md" >}})
- [Circuit breakers]({{< ref circuit-breakers.md >}})
- [Targets]({{< ref "targets.md" >}})
## Related links
Try out one of the Resiliency quickstarts:
- [Resiliency: Service-to-service]({{< ref resiliency-serviceinvo-quickstart.md >}})
- [Resiliency: State Management]({{< ref resiliency-state-quickstart.md >}})

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@ -17,9 +17,8 @@ For CLI there is no explicit opt-in, just the version that this was first made a
| --- | --- | --- | --- | --- |
| **Pluggable components** | Allows creating self-hosted gRPC-based components written in any language that supports gRPC. The following component APIs are supported: State stores, Pub/sub, Bindings | N/A | [Pluggable components concept]({{<ref "components-concept#pluggable-components" >}})| v1.9 |
| **Multi-App Run for Kubernetes** | Configure multiple Dapr applications from a single configuration file and run from a single command on Kubernetes | `dapr run -k -f` | [Multi-App Run]({{< ref multi-app-dapr-run.md >}}) | v1.12 |
| **Workflows** | Author workflows as code to automate and orchestrate tasks within your application, like messaging, state management, and failure handling | N/A | [Workflows concept]({{< ref "components-concept#workflows" >}})| v1.10 |
| **Cryptography** | Encrypt or decrypt data without having to manage secrets keys | N/A | [Cryptography concept]({{< ref "components-concept#cryptography" >}})| v1.11 |
| **Actor State TTL** | Allow actors to save records to state stores with Time To Live (TTL) set to automatically clean up old data. In its current implementation, actor state with TTL may not be reflected correctly by clients, read [Actor State Transactions]({{< ref actors_api.md >}}) for more information. | `ActorStateTTL` | [Actor State Transactions]({{< ref actors_api.md >}}) | v1.11 |
| **Component Hot Reloading** | Allows for Dapr-loaded components to be "hot reloaded". A component spec is reloaded when it is created/updated/deleted in Kubernetes or on file when running in self-hosted mode. Ignores changes to actor state stores and workflow backends. | `HotReload`| [Hot Reloading]({{< ref components-concept.md >}}) | v1.13 |
| **Subscription Hot Reloading** | Allows for declarative subscriptions to be "hot reloaded". A subscription is reloaded either when it is created/updated/deleted in Kubernetes, or on file in self-hosted mode. In-flight messages are unaffected when reloading. | `HotReload`| [Hot Reloading]({{< ref "subscription-methods.md#declarative-subscriptions" >}}) | v1.14 |
| **Scheduler Actor Reminders** | Whilst the [Scheduler service]({{< ref "concepts/dapr-services/scheduler.md" >}}) is deployed by default, Scheduler actor reminders (actor reminders stored in the Scheduler control plane service as opposed to the Placement control plane service actor reminder system) are enabled through a preview feature and needs a feature flag. | `SchedulerReminders`| [Scheduler actor reminders]({{< ref "jobs-overview.md#actor-reminders" >}}) | v1.14 |
| **Scheduler Actor Reminders** | Scheduler actor reminders are actor reminders stored in the Scheduler control plane service, as opposed to the Placement control plane service actor reminder system. The `SchedulerReminders` preview feature defaults to `true`, but you can disable Scheduler actor reminders by setting it to `false`. | `SchedulerReminders`| [Scheduler actor reminders]({{< ref "scheduler.md#actor-reminders" >}}) | v1.14 |

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@ -20,7 +20,7 @@ This endpoint lets you encrypt a value provided as a byte array using a specifie
### HTTP Request
```
PUT http://localhost:<daprPort>/v1.0/crypto/<crypto-store-name>/encrypt
PUT http://localhost:<daprPort>/v1.0-alpha1/crypto/<crypto-store-name>/encrypt
```
#### URL Parameters
@ -59,7 +59,7 @@ returns an array of bytes with the encrypted payload.
### Examples
```shell
curl http://localhost:3500/v1.0/crypto/myAzureKeyVault/encrypt \
curl http://localhost:3500/v1.0-alpha1/crypto/myAzureKeyVault/encrypt \
-X PUT \
-H "dapr-key-name: myCryptoKey" \
-H "dapr-key-wrap-algorithm: aes-gcm" \
@ -81,7 +81,7 @@ This endpoint lets you decrypt a value provided as a byte array using a specifie
#### HTTP Request
```
PUT curl http://localhost:3500/v1.0/crypto/<crypto-store-name>/decrypt
PUT curl http://localhost:3500/v1.0-alpha1/crypto/<crypto-store-name>/decrypt
```
#### URL Parameters
@ -116,7 +116,7 @@ returns an array of bytes representing the decrypted payload.
### Examples
```bash
curl http://localhost:3500/v1.0/crypto/myAzureKeyVault/decrypt \
curl http://localhost:3500/v1.0-alpha1/crypto/myAzureKeyVault/decrypt \
-X PUT
-H "dapr-key-name: myCryptoKey"\
-H "Content-Type: application/octet-stream" \

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@ -1,156 +0,0 @@
---
type: docs
title: "Error codes returned by APIs"
linkTitle: "Error codes"
description: "Detailed reference of the Dapr API error codes"
weight: 1400
---
For http calls made to Dapr runtime, when an error is encountered, an error json is returned in http response body. The json contains an error code and an descriptive error message, e.g.
```
{
"errorCode": "ERR_STATE_GET",
"message": "Requested state key does not exist in state store."
}
```
The following tables list the error codes returned by Dapr runtime:
### Actors API
| Error Code | Description |
| -------------------------------- | ------------------------------------------ |
| ERR_ACTOR_INSTANCE_MISSING | Error when an actor instance is missing. |
| ERR_ACTOR_RUNTIME_NOT_FOUND | Error the actor instance. |
| ERR_ACTOR_REMINDER_CREATE | Error creating a reminder for an actor. |
| ERR_ACTOR_REMINDER_DELETE | Error deleting a reminder for an actor. |
| ERR_ACTOR_TIMER_CREATE | Error creating a timer for an actor. |
| ERR_ACTOR_TIMER_DELETE | Error deleting a timer for an actor. |
| ERR_ACTOR_REMINDER_GET | Error getting a reminder for an actor. |
| ERR_ACTOR_INVOKE_METHOD | Error invoking a method on an actor. |
| ERR_ACTOR_STATE_DELETE | Error deleting the state for an actor. |
| ERR_ACTOR_STATE_GET | Error getting the state for an actor. |
| ERR_ACTOR_STATE_TRANSACTION_SAVE | Error storing actor state transactionally. |
| ERR_ACTOR_REMINDER_NON_HOSTED | Error setting reminder for an actor. |
### Workflows API
| Error Code | Description |
| -------------------------------- | ----------------------------------------------------------- |
| ERR_GET_WORKFLOW | Error getting workflow. |
| ERR_START_WORKFLOW | Error starting the workflow. |
| ERR_PAUSE_WORKFLOW | Error pausing the workflow. |
| ERR_RESUME_WORKFLOW | Error resuming the workflow. |
| ERR_TERMINATE_WORKFLOW | Error terminating the workflow. |
| ERR_PURGE_WORKFLOW | Error purging workflow. |
| ERR_RAISE_EVENT_WORKFLOW | Error raising an event within the workflow. |
| ERR_WORKFLOW_COMPONENT_MISSING | Error when a workflow component is missing a configuration. |
| ERR_WORKFLOW_COMPONENT_NOT_FOUND | Error when a workflow component is not found. |
| ERR_WORKFLOW_EVENT_NAME_MISSING | Error when the event name for a workflow is missing. |
| ERR_WORKFLOW_NAME_MISSING | Error when the workflow name is missing. |
| ERR_INSTANCE_ID_INVALID | Error invalid workflow instance ID provided. |
| ERR_INSTANCE_ID_NOT_FOUND | Error workflow instance ID not found. |
| ERR_INSTANCE_ID_PROVIDED_MISSING | Error workflow instance ID was provided but missing. |
| ERR_INSTANCE_ID_TOO_LONG | Error workflow instance ID exceeds allowable length. |
### State Management API
| Error Code | Description |
| ------------------------------------- | ------------------------------------------------------------------------- |
| ERR_STATE_STORE_NOT_FOUND | Error referencing a state store not found. |
| ERR_STATE_STORES_NOT_CONFIGURED | Error no state stores configured. |
| ERR_NOT_SUPPORTED_STATE_OPERATION | Error transaction requested on a state store with no transaction support. |
| ERR_STATE_GET | Error getting a state for state store. |
| ERR_STATE_DELETE | Error deleting a state from state store. |
| ERR_STATE_SAVE | Error saving a state in state store. |
| ERR_STATE_TRANSACTION | Error encountered during state transaction. |
| ERR_STATE_BULK_GET | Error performing bulk retrieval of state entries. |
| ERR_STATE_QUERY | Error querying the state store. |
| ERR_STATE_STORE_NOT_CONFIGURED | Error state store is not configured. |
| ERR_STATE_STORE_NOT_SUPPORTED | Error state store is not supported. |
| ERR_STATE_STORE_TOO_MANY_TRANSACTIONS | Error exceeded maximum allowable transactions. |
### Configuration API
| Error Code | Description |
| -------------------------------------- | -------------------------------------------- |
| ERR_CONFIGURATION_GET | Error retrieving configuration. |
| ERR_CONFIGURATION_STORE_NOT_CONFIGURED | Error configuration store is not configured. |
| ERR_CONFIGURATION_STORE_NOT_FOUND | Error configuration store not found. |
| ERR_CONFIGURATION_SUBSCRIBE | Error subscribing to a configuration. |
| ERR_CONFIGURATION_UNSUBSCRIBE | Error unsubscribing from a configuration. |
### Crypto API
| Error Code | Description |
| ----------------------------------- | ------------------------------------------ |
| ERR_CRYPTO | General crypto building block error. |
| ERR_CRYPTO_KEY | Error related to a crypto key. |
| ERR_CRYPTO_PROVIDER_NOT_FOUND | Error specified crypto provider not found. |
| ERR_CRYPTO_PROVIDERS_NOT_CONFIGURED | Error no crypto providers configured. |
### Secrets API
| Error Code | Description |
| -------------------------------- | ---------------------------------------------------- |
| ERR_SECRET_STORES_NOT_CONFIGURED | Error that no secret store is configured. |
| ERR_SECRET_STORE_NOT_FOUND | Error that specified secret store is not found. |
| ERR_SECRET_GET | Error retrieving the specified secret. |
| ERR_PERMISSION_DENIED | Error access denied due to insufficient permissions. |
### Pub/Sub API
| Error Code | Description |
| --------------------------- | -------------------------------------------------------- |
| ERR_PUBSUB_NOT_FOUND | Error referencing the Pub/Sub component in Dapr runtime. |
| ERR_PUBSUB_PUBLISH_MESSAGE | Error publishing a message. |
| ERR_PUBSUB_FORBIDDEN | Error message forbidden by access controls. |
| ERR_PUBSUB_CLOUD_EVENTS_SER | Error serializing Pub/Sub event envelope. |
| ERR_PUBSUB_EMPTY | Error empty Pub/Sub. |
| ERR_PUBSUB_NOT_CONFIGURED | Error Pub/Sub component is not configured. |
| ERR_PUBSUB_REQUEST_METADATA | Error with metadata in Pub/Sub request. |
| ERR_PUBSUB_EVENTS_SER | Error serializing Pub/Sub events. |
| ERR_PUBLISH_OUTBOX | Error publishing message to the outbox. |
| ERR_TOPIC_NAME_EMPTY | Error topic name for Pub/Sub message is empty. |
### Conversation API
| Error Code | Description |
| ------------------------------- | ----------------------------------------------- |
| ERR_INVOKE_OUTPUT_BINDING | Error invoking an output binding. |
| ERR_DIRECT_INVOKE | Error in direct invocation. |
| ERR_CONVERSATION_INVALID_PARMS | Error invalid parameters for conversation. |
| ERR_CONVERSATION_INVOKE | Error invoking the conversation. |
| ERR_CONVERSATION_MISSING_INPUTS | Error missing required inputs for conversation. |
| ERR_CONVERSATION_NOT_FOUND | Error conversation not found. |
### Distributed Lock API
| Error Code | Description |
| ----------------------------- | ----------------------------------- |
| ERR_TRY_LOCK | Error attempting to acquire a lock. |
| ERR_UNLOCK | Error attempting to release a lock. |
| ERR_LOCK_STORE_NOT_CONFIGURED | Error lock store is not configured. |
| ERR_LOCK_STORE_NOT_FOUND | Error lock store not found. |
### Healthz
| Error Code | Description |
| ----------------------------- | --------------------------------------------------------------- |
| ERR_HEALTH_NOT_READY | Error that Dapr is not ready. |
| ERR_HEALTH_APPID_NOT_MATCH | Error the app-id does not match expected value in health check. |
| ERR_OUTBOUND_HEALTH_NOT_READY | Error outbound connection health is not ready. |
### Common
| Error Code | Description |
| -------------------------- | ------------------------------------------------ |
| ERR_API_UNIMPLEMENTED | Error API is not implemented. |
| ERR_APP_CHANNEL_NIL | Error application channel is nil. |
| ERR_BAD_REQUEST | Error client request is badly formed or invalid. |
| ERR_BODY_READ | Error reading body. |
| ERR_INTERNAL | Internal server error encountered. |
| ERR_MALFORMED_REQUEST | Error with a malformed request. |
| ERR_MALFORMED_REQUEST_DATA | Error request data is malformed. |
| ERR_MALFORMED_RESPONSE | Error response data is malformed. |

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@ -13,11 +13,11 @@ The jobs API is currently in alpha.
With the jobs API, you can schedule jobs and tasks in the future.
> The HTTP APIs are intended for development and testing only. For production scenarios, the use of the SDKs is strongly
> recommended as they implement the gRPC APIs providing higher performance and capability than the HTTP APIs.
> recommended as they implement the gRPC APIs providing higher performance and capability than the HTTP APIs. This is because HTTP does JSON marshalling which can be expensive, while with gRPC, the data is transmitted over the wire and stored as-is being more performant.
## Schedule a job
Schedule a job with a name.
Schedule a job with a name. Jobs are scheduled based on the clock of the server where the Scheduler service is running. The timestamp is not converted to UTC. You can provide the timezone with the timestamp in RFC3339 format to specify which timezone you'd like the job to adhere to. If no timezone is provided, the server's local time is used.
```
POST http://localhost:3500/v1.0-alpha1/jobs/<name>

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@ -6,7 +6,7 @@ description: "Detailed documentation on the workflow API"
weight: 300
---
Dapr provides users with the ability to interact with workflows and comes with a built-in `dapr` component.
Dapr provides users with the ability to interact with workflows through its built-in workflow engine, which is implemented using Dapr Actors. This workflow engine is accessed using the name `dapr` in API calls as the `workflowComponentName`.
## Start workflow request
@ -36,7 +36,7 @@ Code | Description
---- | -----------
`202` | Accepted
`400` | Request was malformed
`500` | Request formatted correctly, error in dapr code or underlying component
`500` | Request formatted correctly, error in dapr code
### Response content
@ -76,7 +76,7 @@ Code | Description
---- | -----------
`202` | Accepted
`400` | Request was malformed
`500` | Request formatted correctly, error in dapr code or underlying component
`500` | Request formatted correctly, error in dapr code
### Response content
@ -163,7 +163,7 @@ Code | Description
---- | -----------
`202` | Accepted
`400` | Request was malformed
`500` | Error in Dapr code or underlying component
`500` | Error in Dapr code
### Response content
@ -194,7 +194,7 @@ Code | Description
---- | -----------
`202` | Accepted
`400` | Request was malformed
`500` | Error in Dapr code or underlying component
`500` | Error in Dapr code
### Response content
@ -221,7 +221,7 @@ Code | Description
---- | -----------
`200` | OK
`400` | Request was malformed
`500` | Request formatted correctly, error in dapr code or underlying component
`500` | Error in Dapr code
### Response content
@ -244,30 +244,6 @@ Parameter | Description
--------- | -----------
`runtimeStatus` | The status of the workflow instance. Values include: `"RUNNING"`, `"COMPLETED"`, `"CONTINUED_AS_NEW"`, `"FAILED"`, `"CANCELED"`, `"TERMINATED"`, `"PENDING"`, `"SUSPENDED"`
## Component format
A Dapr `workflow.yaml` component file has the following structure:
```yaml
apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
name: <NAME>
spec:
type: workflow.<TYPE>
version: v1.0-alpha1
metadata:
- name: <NAME>
value: <VALUE>
```
| Setting | Description |
| ------- | ----------- |
| `metadata.name` | The name of the workflow component. |
| `spec/metadata` | Additional metadata parameters specified by workflow component |
However, Dapr comes with a built-in `dapr` workflow component that is built on Dapr Actors. No component file is required to use the built-in Dapr workflow component.
## Next Steps
- [Workflow API overview]({{< ref workflow-overview.md >}})

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@ -675,7 +675,12 @@ To perform a `throw-error` operation, invoke the Zeebe command binding with a `P
"data": {
"jobKey": 2251799813686172,
"errorCode": "product-fetch-error",
"errorMessage": "The product could not be fetched"
"errorMessage": "The product could not be fetched",
"variables": {
"productId": "some-product-id",
"productName": "some-product-name",
"productKey": "some-product-key"
}
},
"operation": "throw-error"
}
@ -686,6 +691,11 @@ The data parameters are:
- `jobKey` - the unique job identifier, as obtained when activating the job
- `errorCode` - the error code that will be matched with an error catch event
- `errorMessage` - (optional) an error message that provides additional context
- `variables` - (optional) JSON document that will instantiate the variables at the local scope of the
job's associated task; it must be a JSON object, as variables will be mapped in a
key-value fashion. e.g. { "a": 1, "b": 2 } will create two variables, named "a" and
"b" respectively, with their associated values. [{ "a": 1, "b": 2 }] would not be a
valid argument, as the root of the JSON document is an array and not an object.
##### Response

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@ -0,0 +1,39 @@
---
type: docs
title: "DeepSeek"
linkTitle: "DeepSeek"
description: Detailed information on the DeepSeek conversation component
---
## Component format
A Dapr `conversation.yaml` component file has the following structure:
```yaml
apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
name: deepseek
spec:
type: conversation.deepseek
metadata:
- name: key
value: mykey
- name: maxTokens
value: 2048
```
{{% alert title="Warning" color="warning" %}}
The above example uses secrets as plain strings. It is recommended to use a secret store for the secrets, as described [here]({{< ref component-secrets.md >}}).
{{% /alert %}}
## Spec metadata fields
| Field | Required | Details | Example |
|--------------------|:--------:|---------|---------|
| `key` | Y | API key for DeepSeek. | `mykey` |
| `maxToken` | N | The max amount of tokens for each request. | `2048` |
## Related links
- [Conversation API overview]({{< ref conversation-overview.md >}})

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@ -12,7 +12,7 @@ no_list: true
The following table lists publish and subscribe brokers supported by the Dapr pub/sub building block. [Learn how to set up different brokers for Dapr publish and subscribe.]({{< ref setup-pubsub.md >}})
{{% alert title="Pub/sub component retries vs inbound resiliency" color="warning" %}}
Each pub/sub component has its own built-in retry behaviors. Before explicity applying a [Dapr resiliency policy]({{< ref "policies.md" >}}), make sure you understand the implicit retry policy of the pub/sub component you're using. Instead of overriding these built-in retries, Dapr resiliency augments them, which can cause repetitive clustering of messages.
Each pub/sub component has its own built-in retry behaviors, unique to the message broker solution and unrelated to Dapr. Before explicity applying a [Dapr resiliency policy]({{< ref "resiliency-overview.md" >}}), make sure you understand the implicit retry policy of the pub/sub component you're using. Instead of overriding these built-in retries, Dapr resiliency augments them, which can cause repetitive clustering of messages.
{{% /alert %}}

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@ -68,7 +68,8 @@ spec:
# value: 5
# - name: concurrencyMode # Optional
# value: "single"
# - name: concurrencyLimit # Optional
# value: "0"
```
@ -98,6 +99,7 @@ The above example uses secrets as plain strings. It is recommended to use [a sec
| disableDeleteOnRetryLimit | N | When set to true, after retrying and failing of `messageRetryLimit` times processing a message, reset the message visibility timeout so that other consumers can try processing, instead of deleting the message from SQS (the default behvior). Default: `"false"` | `"true"`, `"false"`
| assetsManagementTimeoutSeconds | N | Amount of time in seconds, for an AWS asset management operation, before it times out and cancelled. Asset management operations are any operations performed on STS, SNS and SQS, except message publish and consume operations that implement the default Dapr component retry behavior. The value can be set to any non-negative float/integer. Default: `5` | `0.5`, `10`
| concurrencyMode | N | When messages are received in bulk from SQS, call the subscriber sequentially (“single” message at a time), or concurrently (in “parallel”). Default: `"parallel"` | `"single"`, `"parallel"`
| concurrencyLimit | N | Defines the maximum number of concurrent workers handling messages. This value is ignored when concurrencyMode is set to `"single"`. To avoid limiting the number of concurrent workers, set this to `0`. Default: `0` | `100`
### Additional info

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@ -198,6 +198,44 @@ Entity management is only possible when using [Microsoft Entra ID Authentication
> Dapr passes the name of the consumer group to the Event Hub, so this is not supplied in the metadata.
## Receiving custom properties
By default, Dapr does not forward [custom properties](https://learn.microsoft.com/azure/event-hubs/add-custom-data-event). However, by setting the subscription metadata `requireAllProperties` to `"true"`, you can receive custom properties as HTTP headers.
```yaml
apiVersion: dapr.io/v2alpha1
kind: Subscription
metadata:
name: order-pub-sub
spec:
topic: orders
routes:
default: /checkout
pubsubname: order-pub-sub
metadata:
requireAllProperties: "true"
```
The same can be achieved using the Dapr SDK:
{{< tabs ".NET" >}}
{{% codetab %}}
```csharp
[Topic("order-pub-sub", "orders")]
[TopicMetadata("requireAllProperties", "true")]
[HttpPost("checkout")]
public ActionResult Checkout(Order order, [FromHeader] int priority)
{
return Ok();
}
```
{{% /codetab %}}
{{< /tabs >}}
## Subscribing to Azure IoT Hub Events
Azure IoT Hub provides an [endpoint that is compatible with Event Hubs](https://docs.microsoft.com/azure/iot-hub/iot-hub-devguide-messages-read-builtin#read-from-the-built-in-endpoint), so the Azure Event Hubs pubsub component can also be used to subscribe to Azure IoT Hub events.

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@ -54,13 +54,13 @@ The above example uses secrets as plain strings. It is recommended to use a secr
The MQTT pub/sub component has no built-in support for retry strategies. This means that the sidecar sends a message to the service only once. If the service marks the message as not processed, the message won't be acknowledged back to the broker. Only if broker resends the message, would it would be retried.
To make Dapr use more spohisticated retry policies, you can apply a [retry resiliency policy]({{< ref "policies.md#retries" >}}) to the MQTT pub/sub component.
To make Dapr use more spohisticated retry policies, you can apply a [retry resiliency policy]({{< ref "retries-overview.md" >}}) to the MQTT pub/sub component.
There is a crucial difference between the two ways of retries:
1. Re-delivery of unacknowledged messages is completely dependent on the broker. Dapr does not guarantee it. Some brokers like [emqx](https://www.emqx.io/), [vernemq](https://vernemq.com/) etc. support it but it not a part of [MQTT3 spec](http://docs.oasis-open.org/mqtt/mqtt/v3.1.1/os/mqtt-v3.1.1-os.html#_Toc398718103).
2. Using a [retry resiliency policy]({{< ref "policies.md#retries" >}}) makes the same Dapr sidecar retry redelivering the messages. So it is the same Dapr sidecar and the same app receiving the same message.
2. Using a [retry resiliency policy]({{< ref "retries-overview.md" >}}) makes the same Dapr sidecar retry redelivering the messages. So it is the same Dapr sidecar and the same app receiving the same message.
### Communication using TLS

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@ -167,7 +167,7 @@ spec:
### Enabling message delivery retries
The Pulsar pub/sub component has no built-in support for retry strategies. This means that sidecar sends a message to the service only once and is not retried in case of failures. To make Dapr use more spohisticated retry policies, you can apply a [retry resiliency policy]({{< ref "policies.md#retries" >}}) to the Pulsar pub/sub component. Note that it will be the same Dapr sidecar retrying the redelivery the message to the same app instance and not other instances.
The Pulsar pub/sub component has no built-in support for retry strategies. This means that sidecar sends a message to the service only once and is not retried in case of failures. To make Dapr use more spohisticated retry policies, you can apply a [retry resiliency policy]({{< ref "retries-overview.md" >}}) to the Pulsar pub/sub component. Note that it will be the same Dapr sidecar retrying the redelivery the message to the same app instance and not other instances.
### Delay queue

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@ -166,7 +166,7 @@ Note that while the `caCert` and `clientCert` values may not be secrets, they ca
The RabbitMQ pub/sub component has no built-in support for retry strategies. This means that the sidecar sends a message to the service only once. When the service returns a result, the message will be marked as consumed regardless of whether it was processed correctly or not. Note that this is common among all Dapr PubSub components and not just RabbitMQ.
Dapr can try redelivering a message a second time, when `autoAck` is set to `false` and `requeueInFailure` is set to `true`.
To make Dapr use more sophisticated retry policies, you can apply a [retry resiliency policy]({{< ref "policies.md#retries" >}}) to the RabbitMQ pub/sub component.
To make Dapr use more sophisticated retry policies, you can apply a [retry resiliency policy]({{< ref "retries-overview.md" >}}) to the RabbitMQ pub/sub component.
There is a crucial difference between the two ways to retry messages:

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@ -52,7 +52,7 @@ spec:
# Controls the default mode for executing queries. (optional)
#- name: queryExecMode
# value: ""
# Uncomment this if you wish to use PostgreSQL as a state store for actors (optional)
# Uncomment this if you wish to use PostgreSQL as a state store for actors or workflows (optional)
#- name: actorStateStore
# value: "true"
```

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# Controls the default mode for executing queries. (optional)
#- name: queryExecMode
# value: ""
# Uncomment this if you wish to use PostgreSQL as a state store for actors (optional)
# Uncomment this if you wish to use PostgreSQL as a state store for actors or workflows (optional)
#- name: actorStateStore
# value: "true"
```

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@ -1,10 +0,0 @@
---
type: docs
title: "Workflow backend component specs"
linkTitle: "Workflow backend"
weight: 2000
description: The supported workflow backend that orchestrate workflow and save workflow state
no_list: true
---
{{< partial "components/description.html" >}}

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@ -1,24 +0,0 @@
---
type: docs
title: "Actor workflow backend"
linkTitle: "Actor workflow backend"
description: Detailed information on the Actor workflow backend component
---
## Component format
The Actor workflow backend is the default backend in Dapr. If no workflow backend is explicitly defined, the Actor backend will be used automatically.
You don't need to define any components to use the Actor workflow backend. It's ready to use out-of-the-box.
However, if you wish to explicitly define the Actor workflow backend as a component, you can do so, as shown in the example below.
```yaml
apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
name: actorbackend
spec:
type: workflowbackend.actor
version: v1
```

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@ -36,6 +36,7 @@ spec:
labels:
- name: <LABEL-NAME>
regex: {}
recordErrorCodes: <TRUE-OR-FALSE>
latencyDistributionBuckets:
- <BUCKET-VALUE-MS-0>
- <BUCKET-VALUE-MS-1>

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@ -64,7 +64,7 @@ targets: # Required
| Field | Required | Details | Example |
|--------------------|:--------:|---------|---------|
| policies | Y | The configuration of resiliency policies, including: <br><ul><li>`timeouts`</li><li>`retries`</li><li>`circuitBreakers`</li></ul> <br> [See more examples with all of the built-in policies]({{< ref policies.md >}}) | timeout: `general`<br>retry: `retryForever`<br>circuit breaker: `simpleCB` |
| policies | Y | The configuration of resiliency policies, including: <br><ul><li>`timeouts`</li><li>`retries`</li><li>`circuitBreakers`</li></ul> <br> [See more examples with all of the built-in policies]({{< ref resiliency-overview.md >}}) | timeout: `general`<br>retry: `retryForever`<br>circuit breaker: `simpleCB` |
| targets | Y | The configuration for the applications, actors, or components that use the resiliency policies. <br>[See more examples in the resiliency targets guide]({{< ref targets.md >}}) | `apps` <br>`components`<br>`actors` |

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@ -18,3 +18,8 @@
state: Alpha
version: v1
since: "1.15"
- component: DeepSeek
link: deepseek
state: Alpha
version: v1
since: "1.15"

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