Operational excellence in GCP - Mini Project: Build & Apply

Operational Excellence with Google Cloud

📖 Scenario: You are working as a cloud engineer for a small company that wants to improve its cloud operations on Google Cloud Platform (GCP). The company wants to set up a simple monitoring and alerting system to ensure their application runs smoothly and any issues are quickly detected and resolved.

🎯 Goal: Build a basic Google Cloud Monitoring setup that includes a metric descriptor, an alerting policy, and a notification channel to achieve operational excellence by monitoring uptime and sending alerts.

📋 What You'll Learn

Create a custom metric descriptor for tracking application uptime

Configure a notification channel for email alerts

Set up an alerting policy that triggers when uptime drops below 99%

Use clear and exact resource names and values as specified

💡 Why This Matters

🌍 Real World

Monitoring uptime and setting alerts is critical for maintaining reliable cloud applications and quickly responding to issues.

💼 Career

Cloud engineers and DevOps professionals use monitoring and alerting to ensure operational excellence and meet service level objectives.

1

Create a custom metric descriptor for uptime

Create a variable called uptime_metric_descriptor that holds a dictionary with these exact keys and values: name set to "custom.googleapis.com/application/uptime", type set to "GAUGE", and description set to "Tracks application uptime percentage".

GCP

# Create the uptime_metric_descriptor dictionary with name, type, and description
# Your code here

Hint

Use a Python dictionary with keys exactly as name, type, and description.

2

Configure an email notification channel

Create a variable called email_notification_channel that holds a dictionary with these exact keys and values: type set to "email", and email_address set to "[e-mail]".

GCP

uptime_metric_descriptor = {
    "name": "custom.googleapis.com/application/uptime",
    "type": "GAUGE",
    "description": "Tracks application uptime percentage"
}
# Create the email_notification_channel dictionary with type and email_address
# Your code here

Hint

Use a dictionary with keys type and email_address exactly as shown.

3

Set up an alerting policy for uptime

Create a variable called alert_policy that holds a dictionary with these exact keys and values: display_name set to "Uptime Alert", condition set to a nested dictionary with metric equal to "custom.googleapis.com/application/uptime" and threshold equal to 99, and notification_channels set to a list containing the string "email_notification_channel".

GCP

uptime_metric_descriptor = {
    "name": "custom.googleapis.com/application/uptime",
    "type": "GAUGE",
    "description": "Tracks application uptime percentage"
}

email_notification_channel = {
    "type": "email",
    "email_address": "[e-mail]"
}
# Create the alert_policy dictionary with display_name, condition, and notification_channels
# Your code here

Hint

Use nested dictionaries for condition and a list for notification_channels.

4

Complete the monitoring configuration

Add a variable called monitoring_configuration that holds a dictionary with these exact keys and values: metric_descriptor set to the variable uptime_metric_descriptor, notification_channel set to the variable email_notification_channel, and alert_policy set to the variable alert_policy.

GCP

uptime_metric_descriptor = {
    "name": "custom.googleapis.com/application/uptime",
    "type": "GAUGE",
    "description": "Tracks application uptime percentage"
}

email_notification_channel = {
    "type": "email",
    "email_address": "[e-mail]"
}

alert_policy = {
    "display_name": "Uptime Alert",
    "condition": {
        "metric": "custom.googleapis.com/application/uptime",
        "threshold": 99
    },
    "notification_channels": ["email_notification_channel"]
}
# Create the monitoring_configuration dictionary linking metric_descriptor, notification_channel, and alert_policy
# Your code here

Hint

Combine the previous variables into one dictionary for the full monitoring setup.

Practice

1. What is the main goal of operational excellence in cloud systems?

easy

A. Keep systems healthy and reliable

B. Write complex code

C. Reduce cloud costs only

D. Avoid using monitoring tools

Solution

1. Step 1: Understand operational excellence purpose

   Operational excellence focuses on keeping cloud systems running well and reliable.

2. Step 2: Compare options with this goal

   Only Keep systems healthy and reliable matches this goal; others focus on unrelated tasks.

3. Final Answer:

   Keep systems healthy and reliable -> Option A

4. Quick Check:

   Operational excellence = system health and reliability [OK]

Hint: Operational excellence means system health and reliability [OK]

Common Mistakes:

• Confusing cost reduction with operational excellence
• Thinking it means writing complex code
• Ignoring monitoring and alerts

2. Which GCP service is used to monitor cloud resources and send alerts?

easy

A. Cloud Monitoring

B. Cloud Storage

C. Cloud Functions

D. Cloud SQL

Solution

1. Step 1: Identify monitoring service in GCP

   Cloud Monitoring is designed to track resource health and send alerts.

2. Step 2: Eliminate unrelated services

   Cloud Storage stores data, Cloud Functions run code, Cloud SQL manages databases.

3. Final Answer:

   Cloud Monitoring -> Option A

4. Quick Check:

   Monitoring and alerts = Cloud Monitoring [OK]

Hint: Monitoring and alerts in GCP use Cloud Monitoring [OK]

Common Mistakes:

• Choosing Cloud Storage for monitoring
• Confusing Cloud Functions with monitoring
• Selecting Cloud SQL instead of monitoring

3. Given this snippet of a GCP alert policy configuration:

{
  "conditions": [{
    "displayName": "High CPU Usage",
    "conditionThreshold": {
      "filter": "metric.type=\"compute.googleapis.com/instance/cpu/utilization\"",
      "comparison": "COMPARISON_GT",
      "thresholdValue": 0.8,
      "duration": "300s"
    }
  }]
}

What does this alert do?

medium

A. Triggers if CPU usage is above 80% for 30 seconds

B. Triggers if CPU usage is below 80% for 5 minutes

C. Triggers if CPU usage is below 80% for 30 seconds

D. Triggers if CPU usage is above 80% for 5 minutes

Solution

1. Step 1: Analyze threshold and duration

   ThresholdValue 0.8 means 80%, duration 300s means 5 minutes.

2. Step 2: Understand comparison operator

   COMPARISON_GT means greater than, so alert triggers if CPU > 80% for 5 minutes.

3. Final Answer:

   Triggers if CPU usage is above 80% for 5 minutes -> Option D

4. Quick Check:

   CPU > 80% for 300s = alert triggers [OK]

Hint: COMPARISON_GT with 0.8 threshold means CPU > 80% [OK]

Common Mistakes:

• Misreading COMPARISON_GT as less than
• Confusing duration seconds with minutes
• Ignoring threshold value meaning

4. You wrote this alert policy JSON but it never triggers:

{
  "conditions": [{
    "displayName": "Low Disk Space",
    "conditionThreshold": {
      "filter": "metric.type=\"compute.googleapis.com/instance/disk/bytes_used\"",
      "comparison": "COMPARISON_LT",
      "thresholdValue": 1000000000,
      "duration": "600s"
    }
  }]
}

What is the likely problem?

medium

A. Threshold value is too high

B. Using COMPARISON_LT instead of COMPARISON_GT

C. Filter metric type is incorrect

D. Duration is too short

Solution

1. Step 1: Understand alert intent

   Low disk space alert should trigger when disk usage is high, so comparison should be greater than.

2. Step 2: Check comparison operator

   COMPARISON_LT triggers when usage is less than threshold, opposite of intent.

3. Final Answer:

   Using COMPARISON_LT instead of COMPARISON_GT -> Option B

4. Quick Check:

   Low disk space needs COMPARISON_GT [OK]

Hint: Low disk space alert needs COMPARISON_GT, not LT [OK]

Common Mistakes:

• Using less than instead of greater than
• Misunderstanding metric meaning
• Ignoring alert duration importance

5. Your team wants to improve operational excellence by automating recovery when a VM instance becomes unresponsive. Which GCP service combination best supports this goal?

hard

A. Cloud Storage + Cloud SQL for backups

B. Cloud Pub/Sub + BigQuery for data analysis

C. Cloud Monitoring + Cloud Functions to restart VM automatically

D. Cloud Run + App Engine for hosting apps

Solution

1. Step 1: Identify automation for recovery

   Cloud Monitoring detects issues; Cloud Functions can run code to fix problems automatically.

2. Step 2: Evaluate other options

   Cloud Storage and SQL are for data, Pub/Sub and BigQuery for messaging and analysis, Cloud Run and App Engine for app hosting, not recovery automation.

3. Final Answer:

   Cloud Monitoring + Cloud Functions to restart VM automatically -> Option C

4. Quick Check:

   Monitoring + automation = Cloud Monitoring + Cloud Functions [OK]

Hint: Use Monitoring to detect and Functions to automate fixes [OK]

Common Mistakes:

• Choosing storage or database services for automation
• Confusing data analysis tools with recovery tools
• Ignoring automation in operational excellence