Grafana for visualization in Kubernetes - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Grafana used for in Kubernetes?

Grafana is used to create visual dashboards that show metrics and logs from Kubernetes clusters. It helps you understand the health and performance of your apps and infrastructure.

beginner

How does Grafana get data to display?

Grafana connects to data sources like Prometheus, which collects metrics from Kubernetes. It queries these sources to get data and then shows it in charts and graphs.

beginner

What is a Grafana dashboard?

A dashboard is a collection of visual panels in Grafana. Each panel shows a graph, chart, or table with data from your Kubernetes cluster.

intermediate

How do you add Prometheus as a data source in Grafana?

In Grafana, go to Configuration > Data Sources > Add data source. Choose Prometheus, then enter the URL where Prometheus runs (like http://prometheus-server). Save to connect.

beginner

Why is Grafana important for DevOps teams?

Grafana helps DevOps teams quickly see problems and trends in their Kubernetes clusters. It makes monitoring easier and supports faster decisions to keep apps running well.

What type of data does Grafana primarily visualize?

A Source code

B Metrics and logs

C User interface designs

D Database schemas

Grafana visualizes metrics and logs collected from systems like Kubernetes to help monitor performance.

Which tool is commonly used as a data source for Grafana in Kubernetes?

A Prometheus

B Docker

C GitHub

D Jenkins

Prometheus collects metrics from Kubernetes and is a common data source for Grafana dashboards.

Where do you configure data sources in Grafana?

A Dashboard > Panels

B Settings > Users

C Configuration > Data Sources

D Alerts > Notifications

Data sources are added and managed under Configuration > Data Sources in Grafana.

What is a Grafana panel?

A A command line tool

B A Kubernetes pod

C A user account

D A visual element showing data like graphs or tables

Panels are the building blocks of dashboards that display data visually.

Why use Grafana with Kubernetes?

A To visualize cluster health and app performance

B To deploy containers

C To write application code

D To manage user permissions

Grafana helps visualize metrics that show how healthy and performant your Kubernetes cluster and apps are.

Explain how Grafana works with Prometheus to visualize Kubernetes metrics.

Think about how data flows from collection to visualization.

Describe the steps to create a basic Grafana dashboard for Kubernetes monitoring.

Focus on setup and building visual panels.

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