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GSP1167

Overview

Datadog is an observability service for cloud-scale applications, providing monitoring of servers, databases, tools, and services, through a SaaS-based data analytics platform. Google Kubernetes Engine (GKE), a service on the Google Cloud Platform (GCP), is a hosted platform for running and orchestrating containerized applications. Due to Kubernetes’s compartmentalized nature and dynamic scheduling, it can be difficult to diagnose points of failure or track down other issues in your infrastructure. Datadog Agent helps to collect metrics from Docker, Kubernetes, and your containerized applicationss. In this lab you will run the Datadog Agent in a Kubernetes cluster as a DaemonSet in order to start collecting your cluster and applications metrics, traces, and logs. You can deploy a Datadog Agent with a Helm chart or directly with a DaemonSet object YAML definition.

In this lab, you will be explaining and using those options in a real cluster, checking in real time the features they enable.

Objectives

In this lab, you will learn about using the Helm chart used to install the Datadog Agent. You will learn how to:

• Deploy a GKE Standard Cluster.
• Deploy the Datadog Operator and the Datadog Agent.
• Deploy a application.
• Check the default dashboard.
• Check the Logs.
• Create a custom dashboard.

Setup and Requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This Qwiklabs hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

What you need

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).
• Time to complete the lab.

Note: If you already have your own personal Google Cloud account or project, do not use it for this lab.

Note: If you are using a Pixelbook, open an Incognito window to run this lab.

How to start your lab and sign in to the Google Cloud Console

1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is a panel populated with the temporary credentials that you must use for this lab.

   

2. Copy the username, and then click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.

   

   Tip: Open the tabs in separate windows, side-by-side.

3. In the Sign in page, paste the username that you copied from the Connection Details panel. Then copy and paste the password.

   Important: You must use the credentials from the Connection Details panel. Do not use your Qwiklabs credentials. If you have your own Google Cloud account, do not use it for this lab (avoids incurring charges).

4. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Cloud Console opens in this tab.

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

In the Cloud Console, in the top right toolbar, click the Activate Cloud Shell button.

Click Continue.

It takes a few moments to provision and connect to the environment. When you are connected, you are already authenticated, and the project is set to your PROJECT_ID. For example:

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

You can list the active account name with this command:

gcloud auth list

(Output)

Credentialed accounts: - <myaccount>@<mydomain>.com (active)

(Example output)

Credentialed accounts: - google1623327_student@qwiklabs.net

You can list the project ID with this command:

gcloud config list project

(Output)

[core] project = <project_ID>

(Example output)

[core] project = qwiklabs-gcp-44776a13dea667a6

Task 1. Deploy a GKE Standard Cluster

In Cloud Shell, run the following commands to create a GKE Standard Kubernetes cluster

gcloud container clusters create "datadog" \ --machine-type="e2-medium" \ --num-nodes="2" \ --zone={{{project_0.default_zone|ZONE}}} Note: you may get a deprecation warning, you can ignore that.

Click Check my progress to verify the objective. Deploy a GKE Standard Cluster.

After a few minutes this will spin up a new Kubernetes cluster with the name datadog. In the meantime you can move on with the Datadog account creation step.

Task 2. Set up a Datadog trial account and get your Datadog API key

If you already have a trial account set up, you can use that. It is recommended that you do not use your production Datadog account to avoid cluttering the environment with test and training assets.

1. Navigate to https://us5.datadoghq.com/signup and enter your name, email, company, and a password.

2. On the next page, you will see a list of available Agent installations. Click Kubernetes.

3. Click Select API Key, then Create New.

4. Enter a name and click Save.

5. Select the row with the new key, and click Use API Key.

6. Copy the Datadog API key.

7. In the Cloud Console, navigate to your Cloud Shell window. Type in the following command, replace <YOUR_DATADOG_API_KEY> with the Datadog API key you copied, and then run the command:

export DD_API_KEY=<YOUR_DATADOG_API_KEY> Note! If you close the Cloud Shell, you'll lose the environment variable you just added. Keep it somewhere safe temporarily, or just leave the page open with the API key as mentioned above in case you need to add it again.

8. In Datadog's console, enable logging by going to Logs and clicking on Getting Started.

Task 3. Deploy the Datadog Operator and Agent

In this section, you will deploy the Datadog Operator.

1. Install the Datadog Operator by running the following command:

helm repo add datadog https://helm.datadoghq.com helm install datadog-operator datadog/datadog-operator kubectl create secret generic datadog-secret --from-literal api-key=$DD_API_KEY

2. Start by deploying the Datadog Agent by running the following command:

echo "apiVersion: datadoghq.com/v2alpha1 kind: DatadogAgent metadata: name: datadog spec: global: clusterName: datadog registry: gcr.io/datadoghq site: us5.datadoghq.com tags: - env:dev - gcp:datadog credentials: apiSecret: secretName: datadog-secret keyName: api-key features: logCollection: enabled: true containerCollectAll: true" > datadog-agent.yaml

3. Deploy the agent to your cluster.

kubectl apply -f datadog-agent.yaml Note: you may get a deprecation warning, you can ignore that.

Click Check my progress to verify the objective. Deploy Datadog Operator.

4. Run the following to check the secrets that were created:

kubectl get secrets

You should get an output similar to this:

NAME TYPE DATA AGE datadog-secret Opaque 1 11m datadog-token Opaque 1 11m sh.helm.release.v1.datadog-operator.v1 helm.sh/release.v1 1 11m webhook-certificate Opaque 2 11m

5. The most important one is the one called datadog-secret. This is a secret that was automatically created that contains your API key.

Check that the secret actually contains your API key getting the value and base64 decoding it:

kubectl get secret datadog-secret --template='{{index .data "api-key"}}' | base64 -d

6. The other two token secrets are the ones used by the service accounts to communicate with the API server. Check the workloads that have been deployed:

kubectl get deployments NAME READY UP-TO-DATE AVAILABLE AGE datadog-cluster-agent 1/1 1 1 10m datadog-operator 1/1 1 1 12m

7. Run the following command to verify the Datadog Agent is running in your environment as a DaemonSet:

kubectl get daemonset

(Output)

NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE datadog-agent 2 2 2 2 2 <NONE> 33m

8. Check the status of the Datadog agent:

kubectl exec -ti $(kubectl get pod -l app.kubernetes.io/component=agent -o name) -- agent status

9. Check the output and look at the different checks that are running by default.

Task 4. Deploy an Application

1. Download the application manifest. This application code lives here

curl https://raw.githubusercontent.com/GoogleCloudPlatform/microservices-demo/main/release/kubernetes-manifests.yaml -o manifest.yaml

2. Apply the manifest to your cluster

kubectl apply -f manifest.yaml

Click Check my progress to verify the objective. Apply the manifest to cluster.

3. Watch the status of your deployment

kubectl get po -w After viewing the status you can exit the command by using CTRL + C.

Task 5. Datadog Kubernetes Explorer

1. Go to the Kubernetes Explorer. Infrastructure > Kubernetes Explorer.

2. You can observe that some of the pods are stuck in pending.

kubectl get po | grep Pending adservice-694f4ff98-49trp 0/1 Pending 0 4m22s cartservice-859c54495f-d78p8 0/1 Pending 0 4m24s loadgenerator-65779994db-mhb24 0/1 Pending 0 4m23s shippingservice-ff5f4d7d-s974d 0/1 Pending 0 4m23s

The exact pods pending might be differently named, but in any case--you don't enough CPU or Memory to spin up all the pods.

Task 6. Scale the cluster

1. GKE has great support for auto scaling node pools based on demand. Set up autoscaling for default-pool.

gcloud container clusters update datadog \ --enable-autoscaling \ --node-pool=default-pool \ --min-nodes=2 \ --max-nodes=5 \ --zone={{{project_0.default_zone|ZONE}}}

2. After a few minutes you will see this command succeed. And a few minutes later you will see one or two new nodes show up. You can watch the pods using to check that the pods are now all in running state.

kubectl get po -w After checking the pod status you can exit the command by using CTRL + C.

3. Because all your pods had not started before, the application could not run properly. But now that all your pods are running, you can access the application via the public IP address of the LoadBalancer.

kubectl get svc frontend-external -o json | jq '.status.loadBalancer.ingress[].ip'

4. In your browser, you can visit the ip to see the online boutique website.

Click Check my progress to verify the objective. Scale the size of default node pool.

Task 7. Check Logs in Datadog

1. Go to Logs.

2. Your Apps have been exporting logs that the Datadog agent has been picking up.

3. You can filter these logs based on service, source, and status. You should see quite a few logs since you have loadgenerator service running.

Congratulations

In this lab, you got hands-on experience using the Datadog Helm Chart and installed the Datadog Agent.

Next Steps / Learn More

Be sure to check out the following labs for more practice with Datadog:

• Datadog Docs
• Datadog Labs
• Datadog Learn
• Find Datadog in the Google Cloud Marketplace

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Manual Last Updated May 8, 2024

Lab Last Tested May 8, 2024

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