Production guidelines on Kubernetes

For a production-ready Kubernetes cluster deployment, it is recommended you run a cluster of at least 3 worker nodes to support a highly-available control plane installation. Use the following resource settings as a starting point. Requirements will vary depending on cluster size and other factors, so perform individual testing to find the right values for your environment:

Note

For more info, read the concept article on CPU and Memory resource units and their meaning.

When installing Dapr using Helm, no default limit/request values are set. Each component has a option (for example, dapr_dashboard.resources), which you can use to tune the Dapr control plane to fit your environment. The has detailed information and examples. For local/dev installations, you might simply want to skip configuring the resources options.

Optional components

The following Dapr control plane deployments are optional:

  • Placement - Needed for Dapr Actors
  • Sentry - Needed for mTLS for service to service invocation
  • Dashboard - Needed for operational view of the cluster

Sidecar resource settings

To set the resource assignments for the Dapr sidecar, see the annotations here. The specific annotations related to resource constraints are:

  • dapr.io/sidecar-cpu-limit
  • dapr.io/sidecar-memory-request

If not set, the Dapr sidecar will run without resource settings, which may lead to issues. For a production-ready setup it is strongly recommended to configure these settings.

For more details on configuring resource in Kubernetes see and Assign CPU Resources to Containers and Pods.

Example settings for the Dapr sidecar in a production-ready setup:

CPUMemory
Limit: 300m, Request: 100mLimit: 1000Mi, Request: 250Mi

Note

Since Dapr is intended to do much of the I/O heavy lifting for your app, it’s expected that the resources given to Dapr enable you to drastically reduce the resource allocations for the application.

The CPU and memory limits above account for the fact that Dapr is intended to a high number of I/O bound operations. It is strongly recommended that you use a monitoring tool to baseline the sidecar (and app) containers and tune these settings based on those baselines.

When deploying Dapr in a production-ready configuration, it’s recommended to deploy with a highly available (HA) configuration of the control plane, which creates 3 replicas of each control plane pod in the dapr-system namespace. This configuration allows the Dapr control plane to retain 3 running instances and survive node failures and other outages.

For a new Dapr deployment, the HA mode can be set with both the Dapr CLI and with .

For an existing Dapr deployment, enabling the HA mode requires additional steps. Please refer to this paragraph for more details.

Deploying Dapr with Helm

Visit the full guide on deploying Dapr with Helm.

Parameters file

For a full list of all available options you can set in the values file (or by using the --set command-line option), see https://github.com/dapr/dapr/blob/master/charts/dapr/README.md.

Instead of using either or helm upgrade as shown below, you can also run helm upgrade --install - this will dynamically determine whether to install or upgrade.

This command will run 3 replicas of each control plane service in the dapr-system namespace.

Note

The Dapr Helm chart automatically deploys with affinity for nodes with the label kubernetes.io/os=linux. You can deploy the Dapr control plane to Windows nodes, but most users should not need to. For more information see Deploying to a Hybrid Linux/Windows K8s Cluster.

Dapr supports zero downtime upgrades. The upgrade path includes the following steps:

  1. Upgrading a CLI version (optional but recommended)
  2. Updating the Dapr control plane
  3. Updating the data plane (Dapr sidecars)

To upgrade the Dapr CLI, of the CLI and ensure it’s in your path.

Upgrading the control plane

See .

Updating the data plane (sidecars)

The last step is to update pods that are running Dapr to pick up the new version of the Dapr runtime. To do that, simply issue a rollout restart command for any deployment that has the annotation:

To see a list of all your Dapr enabled deployments, you can either use the or run the following command using the Dapr CLI:

Enabling HA mode for an existing Dapr deployment requires two steps:

  1. Delete the existing placement stateful set:

  2. Issue the upgrade command:

You delete the placement stateful set because, in the HA mode, the placement service adds Raft for leader election. However, Kubernetes only allows for limited fields in stateful sets to be patched, subsequently failing upgrade of the placement service.

Deletion of the existing placement stateful set is safe. The agents will reconnect and re-register with the newly created placement service, which will persist its table in Raft.

It is recommended that a production-ready deployment includes the following settings:

  1. Mutual Authentication (mTLS) should be enabled. Note that Dapr has mTLS on by default. For details on how to bring your own certificates, see here

  2. App to Dapr API authentication is enabled. This is the communication between your application and the Dapr sidecar. To secure the Dapr API from unauthorized application access, it is recommended to enable Dapr’s token based auth. See for details

  3. Dapr to App API authentication is enabled. This is the communication between Dapr and your application. This ensures that Dapr knows that it is communicating with an authorized application. See Authenticate requests from Dapr using token authentication for details

  4. All component YAMLs should have secret data configured in a secret store and not hard-coded in the YAML file. See on how to use secrets with Dapr components

  5. The Dapr control plane is installed on a dedicated namespace such as dapr-system.

  6. Dapr also supports scoping components for certain applications. This is not a required practice, and can be enabled according to your security needs. See here for more info.

Dapr has tracing and metrics enabled by default. It is recommended that you set up distributed tracing and metrics for your applications and the Dapr control plane in production.

If you already have your own observability set-up, you can disable tracing and metrics for Dapr.

Tracing

To configure a tracing backend for Dapr visit this link.

Metrics

For metrics, Dapr exposes a Prometheus endpoint listening on port 9090 which can be scraped by Prometheus.

To setup Prometheus, Grafana and other monitoring tools with Dapr, visit this link.

Best Practices

Watch this video for a deep dive into the best practices for running Dapr in production with Kubernetes