The documentation you are viewing is for Dapr v1.5 which is an older version of Dapr. For up-to-date documentation, see the latest version.
Production guidelines on Kubernetes
Cluster capacity requirements
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 might serve as a starting point. Requirements will vary depending on cluster size and other factors, so individual testing is needed to find the right values for your environment:
Note: For more info on CPU and Memory resource units and their meaning, see this link
|Operator||Limit: 1, Request: 100m||Limit: 200Mi, Request: 100Mi|
|Sidecar Injector||Limit: 1, Request: 100m||Limit: 200Mi, Request: 30Mi|
|Sentry||Limit: 1, Request: 100m||Limit: 200Mi, Request: 30Mi|
|Placement||Limit: 1, Request: 250m||Limit: 150Mi, Request: 75Mi|
|Dashboard||Limit: 200m, Request: 50m||Limit: 200Mi, Request: 20Mi|
When installing Dapr using Helm, no default limit/request values are set. Each component has a
resources option (for example,
dapr_dashboard.resources), which you can use to tune the Dapr control plane to fit your environment. The Helm chart readme has detailed information and examples. For local/dev installations, you might simply want to skip configuring the
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:
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.
Example settings for the dapr sidecar in a production-ready setup:
|Limit: 300m, Request: 100m||Limit: 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 for the Dapr control plane to survive node failures and other outages.
For an existing Dapr deployment, enabling the HA mode requires additional steps. Please refer to this paragraph for more details.
Deploying Dapr with Helm
For a full guide on deploying Dapr with Helm visit this guide.
It is recommended to create a values file instead of specifying parameters on the command-line. This file should be checked in to source control so that you can track changes made to it.
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
helm install or
helm upgrade as shown below, you can also run
helm upgrade --install - this will dynamically determine whether to install or upgrade.
# add/update the helm repo helm repo add dapr https://dapr.github.io/helm-charts/ helm repo update # See which chart versions are available helm search repo dapr --devel --versions # create a values file to store variables touch values.yml cat << EOF >> values.yml global: ha: enabled: true EOF # run install/upgrade helm install dapr dapr/dapr \ --version=<Dapr chart version> \ --namespace dapr-system \ --create-namespace \ --values values.yml \ --wait # verify the installation kubectl get pods --namespace dapr-system
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
Upgrading Dapr with Helm
Dapr supports zero downtime upgrades. The upgrade path includes the following steps:
- Upgrading a CLI version (optional but recommended)
- Updating the Dapr control plane
- Updating the data plane (Dapr sidecars)
Upgrading the CLI
To upgrade the Dapr CLI, download the latest version of the CLI and ensure it’s in your path.
Upgrading the control plane
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
kubectl rollout restart deploy/<Application deployment name>
To see a list of all your Dapr enabled deployments, you can either use the Dapr Dashboard or run the following command using the Dapr CLI:
dapr list -k APP ID APP PORT AGE CREATED nodeapp 3000 16h 2020-07-29 17:16.22
Enabling high-availability in an existing Dapr deployment
Enabling HA mode for an existing Dapr deployment requires two steps.
First, delete the existing placement stateful set:
kubectl delete statefulset.apps/dapr-placement-server -n dapr-system
Second, issue the upgrade command:
helm upgrade dapr ./charts/dapr -n dapr-system --set global.ha.enabled=true
The reason for deletion of the placement stateful set is 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.
Recommended security configuration
When properly configured, Dapr ensures secure communication. It can also make your application more secure with a number of built-in features.
It is recommended that a production-ready deployment includes the following settings:
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
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 enable API token authentication in Dapr for details
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
All component YAMLs should have secret data configured in a secret store and not hard-coded in the YAML file. See here on how to use secrets with Dapr components
The Dapr control plane is installed on a dedicated namespace such as
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.
Tracing and metrics configuration
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.
To configure a tracing backend for Dapr visit this link.
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.
Watch this video for a deep dive into the best practices for running Dapr in production with Kubernetes