Cloud Monitoring overview in GCP - Step-by-Step Execution

Process Flow - Cloud Monitoring overview

Start: Setup Monitoring Workspace

↓

Collect Metrics from Resources

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Store Metrics in Time Series Database

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Analyze Metrics & Logs

↓

Create Dashboards & Alerts

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Respond to Alerts & Optimize

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End

This flow shows how Cloud Monitoring collects data, stores it, analyzes it, and helps you respond with alerts and dashboards.

Execution Sample

GCP

1. Enable Monitoring API
2. Create Workspace
3. Add VM instance to monitor
4. View metrics on dashboard
5. Set alert policy for CPU usage

This sequence sets up Cloud Monitoring, collects VM metrics, visualizes them, and creates alerts.

Process Table

Step	Action	System State Change	Output/Result
1	Enable Monitoring API	API enabled for project	Monitoring services ready
2	Create Workspace	Workspace created and linked	Central place for metrics
3	Add VM instance to monitor	VM metrics start streaming	Metrics appear in time series
4	View metrics on dashboard	Dashboard shows VM CPU, memory	Visual graphs displayed
5	Set alert policy for CPU usage	Alert triggers if CPU > 80%	Alerts ready to notify
6	CPU usage exceeds 80%	Alert fires	Notification sent to user
7	User responds to alert	Issue investigated and fixed	System performance restored
8	End	Monitoring cycle continues	Continuous visibility maintained

💡 Monitoring runs continuously; this trace shows initial setup and alert cycle.

Status Tracker

Variable	Start	After Step 3	After Step 5	After Step 6	Final
Monitoring API	Disabled	Enabled	Enabled	Enabled	Enabled
Workspace	None	Created	Created	Created	Created
VM Metrics	None	Streaming	Streaming	Streaming	Streaming
Dashboard	Empty	Empty	Populated	Populated	Populated
Alert Policy	None	None	Set	Triggered	Set

Key Moments - 3 Insights

Why do metrics only appear after adding the VM instance?

Metrics start streaming only after the VM is added to the workspace, as shown in execution_table step 3.

What causes the alert to trigger in step 6?

The alert triggers because CPU usage exceeded the threshold set in step 5, as seen in execution_table step 6.

Does monitoring stop after the initial setup?

No, monitoring runs continuously as noted in the exit_note and step 8 of the execution_table.

Visual Quiz - 3 Questions

Look at the execution table, what is the system state after step 3?

A VM metrics start streaming

B Dashboard shows VM CPU and memory

C Alert triggers if CPU > 80%

D API disabled for project

💡 Hint

Check the 'System State Change' column for step 3 in the execution_table.

At which step does the alert policy get set?

A Step 2

B Step 5

C Step 4

D Step 6

💡 Hint

Look for 'Set alert policy' in the 'Action' column in the execution_table.

If the VM was never added, what would happen to the dashboard?

A Alerts would trigger automatically

B It would show CPU usage graphs

C It would show empty or no metrics

D Workspace would be deleted

💡 Hint

Refer to variable_tracker for 'VM Metrics' and 'Dashboard' states after step 3.

Concept Snapshot

Cloud Monitoring overview:
- Enable Monitoring API to start
- Create a Workspace to collect data
- Add resources (like VMs) to monitor
- View metrics on dashboards
- Set alerts to notify on issues
- Monitoring runs continuously for visibility

Full Transcript

Cloud Monitoring starts by enabling the API and creating a workspace. Then you add resources like virtual machines to collect metrics. These metrics are stored and shown on dashboards for easy viewing. You can set alert policies to notify you when something needs attention, such as high CPU usage. When alerts trigger, you respond to fix issues. This cycle runs continuously to keep your cloud resources healthy and visible.

Practice

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

easy

A. To watch and track the health and performance of cloud resources

B. To create virtual machines automatically

C. To store large amounts of data securely

D. To manage user permissions and roles

Solution

1. Step 1: Understand Cloud Monitoring's role

   Cloud Monitoring is designed to observe and report on the status and performance of cloud resources.

2. Step 2: Compare options with this role

   Options B, C, and D describe other services like Compute Engine, Cloud Storage, and IAM, not monitoring.

3. Final Answer:

   To watch and track the health and performance of cloud resources -> Option A

4. Quick Check:

   Cloud Monitoring = watch cloud health [OK]

Hint: Monitoring means watching resource health and performance [OK]

Common Mistakes:

• Confusing monitoring with storage or VM creation
• Thinking monitoring manages user access
• Assuming monitoring creates resources automatically

2. Which command correctly lists all alert policies using the gcloud tool?

easy

A. gcloud alert-policies show

B. gcloud monitoring alerts list

C. gcloud monitoring alert-policies list

D. gcloud monitoring policies get

Solution

1. Step 1: Recall gcloud command structure for alert policies

   The correct command to list alert policies is under 'monitoring alert-policies list'.

2. Step 2: Verify each option's correctness

   gcloud monitoring alert-policies list matches the correct syntax. Options A, B, and D use incorrect verbs or resource names.

3. Final Answer:

   gcloud monitoring alert-policies list -> Option C

4. Quick Check:

   List alert policies = gcloud monitoring alert-policies list [OK]

Hint: Use 'gcloud monitoring alert-policies list' to see alerts [OK]

Common Mistakes:

• Using 'alerts' instead of 'alert-policies'
• Using 'show' instead of 'list' to list policies
• Mixing command verbs and resource names

3. What will be the output of this command?

gcloud monitoring metrics list --filter="metric.type=compute.googleapis.com/instance/cpu/utilization" --limit=1

medium

A. Deletes the CPU utilization metric

B. Shows an error because the filter syntax is wrong

C. Lists all metrics without filtering

D. Lists one metric descriptor for CPU utilization of Compute Engine instances

Solution

1. Step 1: Understand the command components

   The command lists metric descriptors filtered by CPU utilization metric type, limiting output to one.

2. Step 2: Analyze expected output

   The output will show one metric descriptor matching the filter. No deletion or error occurs.

3. Final Answer:

   Lists one metric descriptor for CPU utilization of Compute Engine instances -> Option D

4. Quick Check:

   Filter + limit = one matching metric listed [OK]

Hint: Filter with --filter and limit results with --limit [OK]

Common Mistakes:

• Assuming filter syntax is invalid
• Expecting all metrics without filter
• Confusing list with delete command

4. You tried to create an alert policy with this command:

gcloud monitoring alert-policies create --notification-channels=1234567890 --condition-display-name="High CPU" --condition-filter="metric.type=compute.googleapis.com/instance/cpu/utilization" --condition-threshold-value=0.8

But it failed. What is the likely error?

medium

A. Missing required flag --condition-aggregations

B. Notification channel ID is invalid or missing

C. Condition filter syntax is incorrect

D. Threshold value must be a string, not a number

Solution

1. Step 1: Check notification channel usage

   The notification channel ID must be a valid existing channel; '1234567890' is likely a placeholder or invalid.

2. Step 2: Verify other flags

   Flags like condition filter and threshold value are syntactically correct; aggregation is optional.

3. Final Answer:

   Notification channel ID is invalid or missing -> Option B

4. Quick Check:

   Invalid notification channel causes failure [OK]

Hint: Check notification channel IDs exist before using [OK]

Common Mistakes:

• Assuming aggregation is always required
• Misformatting condition filter
• Using wrong data type for threshold

5. You want to create a dashboard that shows CPU utilization and memory usage for your Compute Engine instances. Which steps should you follow?

hard

A. Use Cloud Console to create a dashboard, add widgets for CPU and memory metrics, then save

B. Run gcloud commands to create a dashboard and add metrics in one command

C. Create alert policies for CPU and memory, then dashboards are created automatically

D. Use Cloud Storage to upload a JSON file with dashboard configuration

Solution

1. Step 1: Understand dashboard creation process

   Dashboards are created in Cloud Console by adding widgets for desired metrics.

2. Step 2: Evaluate options for dashboard creation

   Use Cloud Console to create a dashboard, add widgets for CPU and memory metrics, then save describes the correct manual process. Run gcloud commands to create a dashboard and add metrics in one command is incorrect as gcloud does not support full dashboard creation in one command. Create alert policies for CPU and memory, then dashboards are created automatically confuses alerts with dashboards. Use Cloud Storage to upload a JSON file with dashboard configuration misuses Cloud Storage.

3. Final Answer:

   Use Cloud Console to create a dashboard, add widgets for CPU and memory metrics, then save -> Option A

4. Quick Check:

   Dashboards made via Console with widgets [OK]

Hint: Create dashboards in Console by adding metric widgets [OK]

Common Mistakes:

• Thinking gcloud can create dashboards fully
• Confusing alert policies with dashboards
• Uploading dashboard configs to Cloud Storage