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.

Practice

1. What is the main purpose of a dashboard in Google Cloud Monitoring?

easy

A. To visually display metrics and help monitor cloud resources

B. To store data permanently for backup

C. To write code for cloud functions

D. To manage user permissions

Solution

1. Step 1: Understand what dashboards do

   Dashboards show visual summaries of metrics to help users watch cloud resources easily.

2. Step 2: Identify the correct purpose

   Among the options, only displaying metrics visually matches the dashboard's role.

3. Final Answer:

   To visually display metrics and help monitor cloud resources -> Option A

4. Quick Check:

   Dashboard = Visual metric display [OK]

Hint: Dashboards show data visually, not store or code [OK]

Common Mistakes:

• Confusing dashboards with storage or coding tools
• Thinking dashboards manage permissions

2. Which of the following is the correct way to filter CPU usage metric in a Google Cloud Monitoring dashboard widget?

easy

A. filter: cpu_usage_metric

B. metric = "cpu_usage"

C. metric.type = "compute.googleapis.com/instance/cpu/utilization"

D. metric.cpu = "usage"

Solution

1. Step 1: Recall correct metric filter syntax

   Google Cloud Monitoring uses metric.type with full metric names like "compute.googleapis.com/instance/cpu/utilization".

2. Step 2: Match options to syntax

   Only metric.type = "compute.googleapis.com/instance/cpu/utilization" uses the correct full metric.type format.

3. Final Answer:

   metric.type = "compute.googleapis.com/instance/cpu/utilization" -> Option C

4. Quick Check:

   Correct metric filter uses metric.type with full name [OK]

Hint: Use full metric.type path for filters [OK]

Common Mistakes:

• Using incomplete or shorthand metric names
• Wrong filter keywords like 'filter:' or 'metric.cpu'

3. Given this snippet for a dashboard widget filter:
metric.type = "compute.googleapis.com/instance/disk/read_bytes_count" AND resource.label.instance_id = "12345"
What does this filter show?

medium

A. Disk read bytes count for instance with ID 12345

B. CPU usage for instance 12345

C. Network bytes sent by instance 12345

D. Disk write bytes count for instance 12345

Solution

1. Step 1: Analyze metric.type value

   The metric is "disk/read_bytes_count", which means bytes read from disk.

2. Step 2: Check resource label filter

   The filter limits data to instance with ID "12345".

3. Final Answer:

   Disk read bytes count for instance with ID 12345 -> Option A

4. Quick Check:

   Disk read bytes + instance filter = Disk read bytes count for instance with ID 12345 [OK]

Hint: Match metric.type and resource label to understand filter [OK]

Common Mistakes:

• Confusing read bytes with write bytes
• Mixing disk metrics with CPU or network

4. You created a dashboard widget but it shows no data. Which of these is the most likely cause?

medium

A. The user has read access to the project

B. The dashboard widget is set to refresh every 5 seconds

C. The dashboard has more than 5 widgets

D. The metric filter uses an incorrect metric type name

Solution

1. Step 1: Identify common reasons for no data

   Incorrect metric type names cause filters to match no data, so widget shows empty.

2. Step 2: Evaluate other options

   Refresh rate or widget count do not prevent data display; read access is needed but assumed correct here.

3. Final Answer:

   The metric filter uses an incorrect metric type name -> Option D

4. Quick Check:

   No data usually means wrong metric filter [OK]

Hint: Check metric type spelling if no data shows [OK]

Common Mistakes:

• Blaming refresh rate or widget count
• Ignoring permission issues but assuming access is correct

5. You want to create a dashboard showing both CPU utilization and network bytes sent for multiple instances, but only for instances tagged with "env:production". Which approach is best?

hard

A. Create a widget with a filter for "env:production" but only for disk metrics

B. Create two widgets with filters for CPU and network metrics, each filtering resource labels for "env:production"

C. Create a widget filtering only CPU metrics for all instances, ignoring network metrics

D. Create one widget showing CPU and network metrics without filters, then manually check instance tags

Solution

1. Step 1: Understand the requirement

   You need to show two different metrics (CPU and network) filtered by instance tag "env:production".

2. Step 2: Choose filtering and widget setup

   Best practice is separate widgets for each metric with proper filters on resource labels for the tag.

3. Step 3: Evaluate other options

   Options A, B, and C either miss filtering, use wrong metrics, or ignore one of the required metrics.

4. Final Answer:

   Create two widgets with filters for CPU and network metrics, each filtering resource labels for "env:production" -> Option B

5. Quick Check:

   Separate widgets + correct filters = best dashboard [OK]

Hint: Use separate widgets with filters for each metric [OK]

Common Mistakes:

• Trying to combine unrelated metrics in one widget without filters
• Ignoring resource label filters for tags
• Showing irrelevant metrics