Three pillars (metrics, logs, traces) in Microservices - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to collect system performance data using the correct pillar.

Microservices

system_data = collect_[1]()  # Collects numerical data like CPU usage

Drag options to blanks, or click blank then click option'

Alogs

Btraces

Calerts

Dmetrics

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing logs with metrics

Using traces instead of metrics

Metrics are numerical measurements like CPU or memory usage collected over time.

2fill in blank

medium

Complete the code to record detailed event information for debugging.

Microservices

write_[1]('User login failed due to invalid password')

Drag options to blanks, or click blank then click option'

Aalerts

Blogs

Ctraces

Dmetrics

Attempts:

3 left

💡 Hint

Common Mistakes

Using metrics instead of logs

Confusing traces with logs

Logs record detailed text events useful for debugging and auditing.

3fill in blank

hard

Fix the error in the code to track the path of a request through services.

Microservices

trace = start_[1]('request-123')  # Tracks request flow across microservices

Drag options to blanks, or click blank then click option'

Atraces

Bmetrics

Clogs

Devents

Attempts:

3 left

💡 Hint

Common Mistakes

Using logs instead of traces

Confusing metrics with traces

Traces follow the journey of a request through multiple services to find delays or errors.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters logs by severity and extracts timestamps.

Microservices

{log['timestamp']: log['message'] for log in logs if log['severity'] [1] 'ERROR' and log['timestamp'] [2] 0}

Drag options to blanks, or click blank then click option'

A==

B>

C<

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators

Mixing up equality and inequality

The code filters logs where severity equals 'ERROR' and timestamps are positive.

5fill in blank

hard

Fill all three blanks to build a trace span dictionary with service name, duration, and status check.

Microservices

span = {
  'service': '[1]',
  'duration_ms': [2],
  'status': 'success' if [3] < 500 else 'failure'
}

Drag options to blanks, or click blank then click option'

Aauth-service

Bresponse_time

Dpayment-service

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing service names

Using wrong variable for duration or status

The span dictionary records the service name, duration in milliseconds, and status based on response time.

Practice

1. Which of the following best describes the role of metrics in microservices monitoring?

easy

A. They track the path of a request through multiple services.

B. They record detailed events and errors in the system.

C. They provide numerical data about system performance over time.

D. They store configuration settings for microservices.

Solution

1. Step 1: Understand what metrics represent

   Metrics are numerical measurements like CPU usage, request counts, or latency that show system health over time.

2. Step 2: Differentiate metrics from logs and traces

   Logs record events, traces follow request paths, but metrics summarize performance data.

3. Final Answer:

   They provide numerical data about system performance over time. -> Option C

4. Quick Check:

   Metrics = numerical performance data [OK]

Hint: Metrics = numbers about performance, not events or paths [OK]

Common Mistakes:

• Confusing metrics with logs as event records
• Thinking traces are numerical data
• Assuming metrics store configurations

2. Which syntax correctly represents a log entry in a microservice system?

easy

A. [2024-06-01 12:00:00] ERROR Failed to connect

B. {"timestamp": "2024-06-01T12:00:00Z", "level": "ERROR", "message": "Failed to connect"}

C. Failed to connect

D. ERROR 2024-06-01T12:00:00Z Failed to connect

Solution

1. Step 1: Identify standard log formats

   JSON format is widely used for structured logs in microservices for easy parsing and querying.

2. Step 2: Compare options for correctness

   {"timestamp": "2024-06-01T12:00:00Z", "level": "ERROR", "message": "Failed to connect"} is a valid JSON log entry with timestamp, level, and message fields. Others are less structured or not JSON.

3. Final Answer:

   {"timestamp": "2024-06-01T12:00:00Z", "level": "ERROR", "message": "Failed to connect"} -> Option B

4. Quick Check:

   Structured JSON logs = {"timestamp": "2024-06-01T12:00:00Z", "level": "ERROR", "message": "Failed to connect"} [OK]

Hint: Logs are best as structured JSON for easy use [OK]

Common Mistakes:

• Using unstructured plain text logs
• Confusing XML-like logs with JSON
• Ignoring timestamp or level fields

3. Given this trace data snippet for a request through three microservices, what is the total time spent processing the request?

{
  "traceId": "abc123",
  "spans": [
    {"service": "A", "duration_ms": 50},
    {"service": "B", "duration_ms": 30},
    {"service": "C", "duration_ms": 20}
  ]
}

medium

A. 100 ms

B. 50 ms

C. 30 ms

D. 20 ms

Solution

1. Step 1: Understand trace spans and durations

   Each span shows time spent in a service. Total time is sum if services are sequential.

2. Step 2: Sum durations of all spans

   50 ms + 30 ms + 20 ms = 100 ms total processing time.

3. Final Answer:

   100 ms -> Option A

4. Quick Check:

   Sum spans durations = 100 ms [OK]

Hint: Add all span durations for total trace time [OK]

Common Mistakes:

• Taking only the longest span as total time
• Ignoring some spans in calculation
• Confusing traceId with duration

4. A developer notices that logs are missing trace IDs in a microservices system. What is the most likely cause?

medium

A. Services are using different programming languages.

B. Metrics collection is disabled.

C. Logs are stored in a different database.

D. Trace context is not propagated between services.

Solution

1. Step 1: Understand trace ID propagation

   Trace IDs must be passed along service calls to link logs and traces.

2. Step 2: Identify cause of missing trace IDs

   If trace context is not propagated, logs won't have trace IDs, breaking trace-log correlation.

3. Final Answer:

   Trace context is not propagated between services. -> Option D

4. Quick Check:

   Missing trace IDs = missing context propagation [OK]

Hint: Trace IDs must flow between services to appear in logs [OK]

Common Mistakes:

• Confusing metrics with trace IDs
• Assuming storage location causes missing IDs
• Blaming programming language differences

5. You are designing a microservices system and want to implement the three pillars: metrics, logs, and traces. Which approach best ensures scalability and effective monitoring?

hard

A. Use a centralized monitoring system that collects metrics via Prometheus, logs via ELK stack, and traces via OpenTelemetry.

B. Store all logs and traces locally on each service to reduce network overhead.

C. Only collect metrics and ignore logs and traces to save storage space.

D. Send all raw logs and traces directly to the client application for analysis.

Solution

1. Step 1: Identify best practices for scalable monitoring

   Centralized systems like Prometheus for metrics, ELK for logs, and OpenTelemetry for traces are industry standards for scalability and analysis.

2. Step 2: Evaluate options for scalability and effectiveness

   Local storage limits analysis and scalability; ignoring logs/traces loses insights; sending raw data to clients is inefficient and insecure.

3. Final Answer:

   Use a centralized monitoring system that collects metrics via Prometheus, logs via ELK stack, and traces via OpenTelemetry. -> Option A

4. Quick Check:

   Centralized, specialized tools = scalable monitoring [OK]

Hint: Centralize collection with proven tools for all three pillars [OK]

Common Mistakes:

• Storing logs/traces locally only
• Ignoring logs or traces
• Sending raw data directly to clients