Grafana for visualization in Kubernetes - Step-by-Step Execution

Process Flow - Grafana for visualization

Start Kubernetes Cluster

↓

Deploy Prometheus for Metrics

↓

Deploy Grafana Pod

↓

Configure Grafana Datasource -> Prometheus

↓

Create Dashboards in Grafana

↓

Visualize Metrics in Browser

↓

Monitor & Alert Based on Visuals

This flow shows how Grafana connects to Prometheus in Kubernetes to visualize metrics step-by-step.

Execution Sample

Kubernetes

kubectl apply -f prometheus.yaml
kubectl apply -f grafana.yaml
kubectl port-forward svc/grafana 3000:3000
# Open http://localhost:3000
# Add Prometheus as datasource
# Create dashboard to view metrics

Deploy Prometheus and Grafana in Kubernetes, forward Grafana port, then configure and view metrics dashboards.

Process Table

Step	Action	Command/Config	Result/Output
1	Deploy Prometheus	kubectl apply -f prometheus.yaml	Prometheus pods running, collecting metrics
2	Deploy Grafana	kubectl apply -f grafana.yaml	Grafana pod running, service created
3	Port-forward Grafana	kubectl port-forward svc/grafana 3000:3000	Local port 3000 forwards to Grafana service
4	Access Grafana UI	Open http://localhost:3000	Grafana login page appears
5	Add Prometheus datasource	Configure datasource URL to Prometheus service	Datasource connected successfully
6	Create dashboard	Use Grafana UI to add panels with Prometheus queries	Dashboard shows live metrics graphs
7	Visualize metrics	Interact with dashboard	Metrics update in real-time
8	Exit	Stop port-forward or pods	Visualization stops, pods terminate

💡 User stops port-forward or deletes pods, ending visualization session

Status Tracker

Component	Initial State	After Deployment	After Config	Final State
Prometheus	Not running	Running, scraping metrics	Running	Running, metrics available
Grafana	Not running	Running pod and service	Datasource connected	Dashboard showing metrics
Port-forward	Not active	Active forwarding localhost:3000	Active	Stopped after exit

Key Moments - 3 Insights

Why can't I see metrics in Grafana after deployment?

Check the execution_table step 5: Grafana datasource must be correctly configured to Prometheus URL. Without this, no data appears.

What happens if I stop the port-forward command?

Refer to execution_table step 8: Stopping port-forward breaks connection to Grafana UI on localhost, so visualization stops.

Why do I need Prometheus before Grafana?

Prometheus collects metrics; Grafana only visualizes them. Without Prometheus running (step 1), Grafana has no data source (step 5).

Visual Quiz - 3 Questions

Look at the execution_table at step 3. What does the port-forward command do?

A It configures Prometheus datasource

B It deploys Grafana pod

C It forwards local port 3000 to Grafana service in Kubernetes

D It creates a dashboard in Grafana

💡 Hint

See execution_table step 3 under Result/Output

At which step does Grafana get connected to Prometheus?

A Step 2

B Step 5

C Step 6

D Step 4

💡 Hint

Check execution_table step 5 Action and Result

If you stop the port-forward, what happens to the visualization?

A Visualization stops because connection is lost

B Metrics continue updating in browser

C Prometheus stops collecting metrics

D Grafana dashboard deletes automatically

💡 Hint

Refer to execution_table step 8 and variable_tracker Port-forward final state

Concept Snapshot

Grafana visualizes metrics by connecting to Prometheus in Kubernetes.
Deploy Prometheus and Grafana pods.
Use kubectl port-forward to access Grafana UI locally.
Configure Prometheus as Grafana datasource.
Create dashboards to see live metrics.
Stop port-forward to end visualization.

Full Transcript

This visual execution shows how to use Grafana for visualization in Kubernetes. First, Prometheus is deployed to collect metrics. Then Grafana is deployed as a pod with a service. Using kubectl port-forward, we forward local port 3000 to Grafana's service port. Opening http://localhost:3000 shows the Grafana UI. We add Prometheus as a datasource in Grafana to connect it to the metrics. Next, dashboards are created in Grafana to visualize live metrics from Prometheus. Metrics update in real-time on the dashboard. Finally, stopping the port-forward or deleting pods stops the visualization. Key points include ensuring datasource configuration and understanding port-forward's role in accessing Grafana UI.

Practice

1. What is the main purpose of Grafana in a Kubernetes environment?

easy

A. To visualize and monitor data from Kubernetes clusters

B. To deploy applications automatically

C. To manage Kubernetes user permissions

D. To store container images

Solution

1. Step 1: Understand Grafana's role

   Grafana is a tool designed to create visual dashboards from data sources.

2. Step 2: Connect Grafana to Kubernetes data

   In Kubernetes, Grafana connects to metrics sources to visualize cluster health and performance.

3. Final Answer:

   To visualize and monitor data from Kubernetes clusters -> Option A

4. Quick Check:

   Grafana = Visualization and Monitoring [OK]

Hint: Grafana = Visualize data, not deploy or store [OK]

Common Mistakes:

• Confusing Grafana with deployment tools
• Thinking Grafana manages permissions
• Assuming Grafana stores images

2. Which Kubernetes resource is commonly used to deploy Grafana?

easy

A. Pod

B. Deployment

C. ConfigMap

D. ServiceAccount

Solution

1. Step 1: Identify deployment method

   Grafana runs as an application that needs to be managed and scaled.

2. Step 2: Choose Kubernetes resource for managing apps

   Deployments manage pods and allow updates and scaling.

3. Final Answer:

   Deployment -> Option B

4. Quick Check:

   Deployments = Manage app lifecycle [OK]

Hint: Use Deployment to run and scale Grafana pods [OK]

Common Mistakes:

• Using Pod directly without Deployment
• Confusing ConfigMap with deployment
• Thinking ServiceAccount deploys apps

3. Given this snippet of a Grafana dashboard JSON, what type of visualization will it create?

{
  "panels": [
    {
      "type": "graph",
      "title": "CPU Usage"
    }
  ]
}

medium

A. A graph chart displaying CPU usage over time

B. A table showing CPU usage data

C. A text panel with CPU usage summary

D. A heatmap of CPU usage

Solution

1. Step 1: Identify panel type in JSON

   The panel type is "graph", which means a line or bar chart.

2. Step 2: Match visualization to type

   Graph panels show data trends over time, suitable for CPU usage.

3. Final Answer:

   A graph chart displaying CPU usage over time -> Option A

4. Quick Check:

   Panel type 'graph' = Chart visualization [OK]

Hint: Panel type 'graph' means line/bar chart [OK]

Common Mistakes:

• Confusing 'graph' with 'table'
• Assuming 'graph' means text
• Mixing heatmap with graph

4. You deployed Grafana on Kubernetes but the dashboard shows no data. Which fix is most likely correct?

medium

A. Increase the CPU limits of the Grafana pod

B. Restart the Kubernetes cluster

C. Check if the data source is configured and connected properly

D. Delete the Grafana deployment and recreate it

Solution

1. Step 1: Identify cause of no data in Grafana

   No data usually means Grafana cannot read from its data source.

2. Step 2: Verify data source configuration

   Ensure the data source (like Prometheus) is added and reachable in Grafana settings.

3. Final Answer:

   Check if the data source is configured and connected properly -> Option C

4. Quick Check:

   No data = Check data source connection [OK]

Hint: No data? Verify data source setup first [OK]

Common Mistakes:

• Restarting cluster unnecessarily
• Deleting deployment without checking config
• Changing CPU limits unrelated to data

5. You want to create a Grafana dashboard that shows CPU and memory usage side by side for multiple Kubernetes nodes. Which approach is best?

hard

A. Use a text panel describing CPU and memory usage

B. Use a single panel with combined CPU and memory metrics in one graph

C. Create separate dashboards for CPU and memory usage

D. Create a dashboard JSON with two panels: one for CPU and one for memory, each querying node metrics

Solution

1. Step 1: Understand dashboard layout needs

   Side by side means multiple panels on one dashboard.

2. Step 2: Design panels for each metric

   Create one panel for CPU and another for memory, each querying node metrics separately.

3. Step 3: Avoid combining unrelated metrics in one graph

   Separate panels improve clarity and comparison.

4. Final Answer:

   Create a dashboard JSON with two panels: one for CPU and one for memory, each querying node metrics -> Option D

5. Quick Check:

   Separate panels = Clear side-by-side view [OK]

Hint: Use separate panels for different metrics side by side [OK]

Common Mistakes:

• Combining CPU and memory in one confusing graph
• Making separate dashboards instead of one
• Using text panels instead of graphs