Metrics and dashboards in GCP - Step-by-Step Execution

Process Flow - Metrics and dashboards

Start: Define Metrics

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Collect Data from Resources

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Store Metrics in Monitoring Service

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Create Dashboard

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Add Widgets to Dashboard

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View Dashboard for Insights

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Set Alerts (Optional)

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End

This flow shows how metrics are defined, collected, stored, and visualized on dashboards in GCP Monitoring.

Execution Sample

GCP

1. Define metric: CPU usage
2. Collect CPU data from VM
3. Store metric in Cloud Monitoring
4. Create dashboard
5. Add CPU chart widget
6. View dashboard

This sequence collects CPU usage data from a VM and shows it on a dashboard widget.

Process Table

Step	Action	Input/Condition	Output/Result
1	Define metric	Metric name: CPU usage	Metric created in Monitoring
2	Collect data	VM sends CPU usage data	Data points received by Monitoring
3	Store metric	Data points received	Metrics stored and indexed
4	Create dashboard	Dashboard name: VM Metrics	Dashboard created
5	Add widget	Widget type: Line chart, Metric: CPU usage	Widget added to dashboard
6	View dashboard	Dashboard with widget	CPU usage graph displayed
7	Set alert (optional)	Threshold: CPU > 80%	Alert policy created
8	End	All steps done	Dashboard ready for monitoring

💡 All steps complete, dashboard shows live CPU usage metrics

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	Final
Metric Definition	None	CPU usage metric defined	CPU usage metric defined	CPU usage metric stored	CPU usage metric stored	CPU usage metric stored	CPU usage metric stored	CPU usage metric stored
Data Points	None	None	CPU data points received	CPU data points stored	CPU data points stored	CPU data points stored	CPU data points stored	CPU data points stored
Dashboard	None	None	None	Dashboard created	Dashboard with widget	Dashboard with widget	Dashboard with widget	Dashboard with widget
Alert Policy	None	None	None	None	None	None	None	Alert policy created

Key Moments - 3 Insights

Why do we need to define a metric before collecting data?

Defining a metric sets the name and type so Monitoring knows how to store and display data. See execution_table step 1 and 2 where metric is defined before data collection.

What happens if no dashboard is created after metrics are stored?

Metrics are collected and stored but not visualized. Without a dashboard (step 4), you cannot see the data graphically. See execution_table step 3 vs step 4.

Can alerts be set without dashboards?

Yes, alerts monitor metrics independently. But dashboards help visualize data trends. Alerts are optional as shown in step 7.

Visual Quiz - 3 Questions

Look at the execution_table, what is the output after step 3?

A Dashboard created

B Metrics stored and indexed

C CPU usage graph displayed

D Alert policy created

💡 Hint

Check the 'Output/Result' column for step 3 in execution_table

At which step is the dashboard first created?

A Step 4

B Step 2

C Step 5

D Step 6

💡 Hint

Look for 'Dashboard created' in the Output/Result column

If the alert threshold changes from 80% to 90%, which step in the execution_table would change?

A Step 1

B Step 5

C Step 7

D Step 6

💡 Hint

Alerts are created in step 7; threshold changes affect alert policy creation

Concept Snapshot

Metrics and dashboards in GCP Monitoring:
- Define metrics to specify data type
- Collect and store metric data from resources
- Create dashboards to visualize metrics
- Add widgets like charts to dashboards
- Optionally set alerts on metric thresholds
- Dashboards provide real-time insights

Full Transcript

In Google Cloud Platform, metrics are defined first to specify what data to collect, such as CPU usage. Then, data is collected from resources like virtual machines and stored in Cloud Monitoring. Dashboards are created to visualize these metrics by adding widgets such as line charts. Optionally, alert policies can be set to notify when metrics cross thresholds. This process helps monitor resource health and performance visually and proactively.