# Guidelines for production ready deployments on Kubernetes This section outlines recommendations and practices for deploying Dapr to a Kubernetes cluster in a production ready configuration. ## Contents - [Cluster capacity requirements](#cluster-capacity-requirements) - [Sidecar resource requirements](#sidecar-resource-requirements) - [Deploying Dapr with Helm](#deploying-dapr-with-helm) - [Upgrading Dapr with Helm](#upgrading-dapr-with-helm) - [Recommended security configuration](#recommended-security-configuration) - [Tracing and metrics configuration](#tracing-and-metrics-configuration) ## Cluster capacity requirements For a production ready Kubernetes cluster deployment, it is recommended you run a cluster of 3 worker nodes to support a highly-available setup of the control plane. The Dapr control plane pods are designed to be lightweight and require the following resources in a production-ready setup: *Note: For more info on CPU and Memory resource units and their meaning, see [this](https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#resource-units-in-kubernetes) link* | Deployment | CPU | Memory | ------------| ---- | ------ | Operator | Limit: 1, Request: 100m | Limit: 200Mi, Request: 20Mi | Sidecar Injector | Limit: 1, Request: 100m | Limit: 200Mi, Request: 20Mi | Sentry | Limit: 1, Request: 100m | Limit: 200Mi, Request: 20Mi | Placement | Limit: 1, Request: 250m | Limit: 500Mi, Request: 100Mi | Dashboard | Limit: 200m, Request: 50m | Limit: 200Mi, Request: 20Mi To change the resource assignments for the Dapr sidecar, see the annotations [here](../configure-k8s/). The specific annotations related to resource constraints are: * `dapr.io/sidecar-cpu-limit` * `dapr.io/sidecar-memory-limit` * `dapr.io/sidecar-cpu-request` * `dapr.io/sidecar-memory-request` For more details on configuring resource in Kubernetes see [Assign Memory Resources to Containers and Pods](https://kubernetes.io/docs/tasks/configure-pod-container/assign-memory-resource/) and [Assign CPU Resources to Containers and Pods](https://kubernetes.io/docs/tasks/configure-pod-container/assign-cpu-resource/) ### 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 requirements The Dapr sidecar requires the following resources in a production-ready setup: | CPU | Memory | --------- | --------- | | Limit: 4, Request: 100m | Limit: 4000Mi, 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 do a lot of high performant I/O bound operations. Based on your app needs, you can increase or decrease those limits accordingly. ## Deploying Dapr with Helm When deploying to a production cluster, it's recommended to use Helm. The Dapr CLI installation into a Kubernetes cluster is for a development and test only setup. You can find information [here](../../getting-started/environment-setup.md#using-helm-(advanced)) on how to deploy Dapr using Helm. When deploying Dapr in a production-ready configuration, it's recommended to deploy with a highly available configuration of the control plane: ```bash helm install dapr dapr/dapr --namespace dapr-system --set global.ha.enabled=true ``` This command will run 3 replicas of each control plane pod with the exception of the Placement pod 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](../windows-k8s/)* ## Upgrading Dapr with Helm 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) ### 1. Upgrading the CLI To upgrade the Dapr CLI, [download a release version](https://github.com/dapr/cli/releases) of the CLI that matches the Dapr runtime version. For example, if upgrading to Dapr 0.9.0, download a CLI version of 0.9.x. After you downloaded the binary, it's recommended you put the CLI binary in your path. ### Updating the control plane #### Saving the current certificates When upgrading to a new version of Dapr, it is recommended you carry over the root and issuer certificates instead of re-generating them, which might cause a downtime for applications that make use of service invocation or actors. #### Exporting certs with the Dapr CLI To get your current certs with the Dapr CLI, run the following command: ``` dapr mtls export -o ./certs ``` This will save any existing root cert, issuer cert and issuer key in the output dir of your choice. ### Exporting certs manually To get the current root and issuer certificates, run the following command: ``` kubectl get secret dapr-trust-bundle -o yaml -n dapr-system apiVersion: v1 data: ca.crt: issuer.crt: issuer.key: kind: Secret ``` Copy the contents of `ca.crt`, `issuer.crt` and `issuer.key` and base64 decode them. Save these certificates as files. You should have the following files containing the base64 decoded text from the secret saved on your disk: 1. ca.crt 2. issuer.crt 3. issuer.key #### Updating the control plane pods Next, you need to find a Helm chart version that installs the new desired version of Dapr and perform a `helm upgrade` operation. First, update the Helm Chart repos: ```bash helm repo update ``` List all charts in the Dapr repo: ```bash helm search repo dapr NAME CHART VERSION APP VERSION DESCRIPTION dapr/dapr 0.4.3 0.9.0 A Helm chart for Dapr on Kubernetes dapr/dapr 0.4.2 0.8.0 A Helm chart for Dapr on Kubernetes ``` The APP VERSION column tells us which Dapr runtime version is installed by the chart. Use the following command to upgrade Dapr to your desired runtime version providing a path to the certificate files you saved: ```bash helm upgrade dapr dapr/dapr --version --namespace dapr-system --reset-values --set-file dapr_sentry.tls.root.certPEM=ca.crt --set-file dapr_sentry.tls.issuer.certPEM=issuer.crt --set-file dapr_sentry.tls.issuer.keyPEM=issuer.key ``` Kubernetes now performs a rolling update. Wait until all the new pods appear as running: ```bash kubectl get po -n dapr-system -w NAME READY STATUS RESTARTS AGE dapr-dashboard-dcd7cc8fb-ml2p8 1/1 Running 0 12s dapr-operator-7b57d884cd-6spcq 1/1 Running 0 12s dapr-placement-86b76f6545-vkc6f 1/1 Running 0 12s dapr-sentry-7c7bf75d8b-dcnhm 1/1 Running 0 12s dapr-sidecar-injector-7b847db96f-kwst9 1/1 Running 0 12s ``` You can verify the health and version of the control plane using the Dapr CLI: ```bash dapr status -k NAME NAMESPACE HEALTHY STATUS VERSION AGE CREATED dapr-placement dapr-system True Running 0.9.0 12s 2020-07-24 15:38.15 dapr-operator dapr-system True Running 0.9.0 12s 2020-07-24 15:38.15 dapr-sidecar-injector dapr-system True Running 0.9.0 12s 2020-07-24 15:38.16 dapr-sentry dapr-system True Running 0.9.0 12s 2020-07-24 15:38.15 ``` *Note: If new fields have been added to the target Helm Chart being upgraded to, the `helm upgrade` command will fail. If that happens, you need to find which new fields have been added in the new chart and add them as parameters to the upgrade command, for example: `--set =`.* #### 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 `dapr.io/enabled` annotation: ``` kubectl rollout restart deploy/ ``` To see a list of all your Dapr enabled deployments, you can either use the [Dapr Dashboard](https://github.com/dapr/dashboard) or run the following command using the Dapr CLI: ```bash dapr list -k APP ID APP PORT AGE CREATED nodeapp 3000 16h 2020-07-29 17:16.22 ``` ## Recommended security configuration Properly configured, Dapr not only be secured with regards to it's control plane and sidecars communication, but 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: 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](../configure-mtls/README.md#bringing-your-own-certificates) 2. Dapr API authentication is enabled (this is the between your application and the Dapr sidecar). To secure the Dapr API from unauthorized access, it is recommended to enable Dapr's token based auth. See [here](../enable-dapr-api-token-based-authentication/README.md) for details 3. All component YAMLs should have secret data configured in a secret store and not hard-coded in the YAML file. See [here](../../concepts/secrets/component-secrets.md) on how to use secrets with Dapr components 4. The Dapr control plane is installed on a separate namespace such as `dapr-system`, and never into the `default` namespace Dapr also supports scoping components for certain applications. This is not a required practice, and can be enabled according to your Sec-Ops needs. See [here](../components-scopes/README.md) for more info. ## Tracing and metrics configuration Dapr has tracing and metrics enabled by default. To configure a tracing backend for Dapr visit [this](../diagnose-with-tracing) link. For metrics, Dapr exposes a Prometheus endpoint listening on port 9090 which can be scraped by Prometheus. 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 setup Prometheus, Grafana and other monitoring tools with Dapr, visit [this](../setup-monitoring-tools) link.