Cloud Trace for latency analysis in GCP - Mini Project: Build & Apply

Cloud Trace for latency analysis

📖 Scenario: You work for a company that wants to understand how long their web application takes to respond to user requests. They want to use Google Cloud Trace to collect and analyze latency data.

🎯 Goal: Set up a basic Cloud Trace configuration to collect latency data for a sample web service and analyze the trace spans.

📋 What You'll Learn

Create a dictionary called trace_data with sample trace spans and their durations

Add a configuration variable called latency_threshold_ms to set a latency limit

Use a list comprehension to filter trace spans that exceed the latency threshold

Add a final configuration to enable trace export to Google Cloud Trace

💡 Why This Matters

🌍 Real World

Cloud Trace helps developers and operators understand where delays happen in their applications by collecting latency data from different parts of the system.

💼 Career

Knowing how to configure and analyze trace data is important for cloud engineers and developers to improve application performance and user experience.

1

Create sample trace data

Create a dictionary called trace_data with these exact entries: 'span1': 120, 'span2': 85, 'span3': 200, 'span4': 50. The numbers represent latency in milliseconds.

GCP

# Create the trace_data dictionary with span names and latency values
# Your code here

Need a hint?

Use curly braces to create a dictionary with keys as span names and values as latency in ms.

2

Set latency threshold

Add a variable called latency_threshold_ms and set it to 100 to represent the maximum acceptable latency in milliseconds.

GCP

trace_data = {'span1': 120, 'span2': 85, 'span3': 200, 'span4': 50}
# Set latency_threshold_ms to 100
# Your code here

Need a hint?

Just assign the number 100 to the variable latency_threshold_ms.

3

Filter slow trace spans

Use a list comprehension to create a list called slow_spans that contains the names of spans from trace_data whose latency is greater than latency_threshold_ms.

GCP

trace_data = {'span1': 120, 'span2': 85, 'span3': 200, 'span4': 50}
latency_threshold_ms = 100
# Create slow_spans list with spans exceeding latency_threshold_ms
# Your code here

Need a hint?

Use a list comprehension with for span, latency in trace_data.items() and an if condition.

4

Enable trace export configuration

Add a dictionary called trace_config with a key 'export_enabled' set to true to enable exporting trace data to Google Cloud Trace.

GCP

trace_data = {'span1': 120, 'span2': 85, 'span3': 200, 'span4': 50}
latency_threshold_ms = 100
slow_spans = [span for span, latency in trace_data.items() if latency > latency_threshold_ms]
# Create trace_config dictionary with export_enabled set to true
# Your code here

Need a hint?

Create a dictionary with the key 'export_enabled' and value true.