Three pillars (metrics, logs, traces) in Microservices - Cheat Sheet & Quick Revision

Recall & Review

beginner

What are the three pillars of observability in microservices?

The three pillars are Metrics, Logs, and Traces. They help monitor and understand system behavior.

beginner

Define Metrics in the context of microservices observability.

Metrics are numerical data points collected over time, like CPU usage or request counts, used to track system health.

beginner

What role do Logs play in microservices observability?

Logs are detailed records of events or messages generated by services, useful for debugging and understanding specific incidents.

intermediate

Explain Traces and their importance in microservices.

Traces track the path of a request as it travels through multiple services, helping identify bottlenecks and failures.

intermediate

How do Metrics, Logs, and Traces complement each other?

Metrics give a high-level overview, Logs provide detailed event info, and Traces show request flow across services, together offering full observability.

Which pillar provides numerical data like request rates and error counts?

A Traces

B Metrics

C Logs

D Alerts

Metrics are numerical measurements collected over time to monitor system health.

What pillar helps track the journey of a request through multiple microservices?

A Traces

B Logs

C Metrics

D Events

Traces follow requests across services to identify delays or failures.

Which pillar is best for detailed debugging of specific errors?

A Metrics

B Dashboards

C Traces

D Logs

Logs contain detailed event messages useful for debugging.

Which pillar would alert you if CPU usage suddenly spikes?

A Traces

B Logs

C Metrics

D Events

Metrics track system resource usage like CPU and memory.

What is NOT a benefit of combining metrics, logs, and traces?

A Automatic code generation

B Complete system observability

C Faster debugging

D Better performance monitoring

Automatic code generation is unrelated to observability pillars.

Describe the three pillars of observability and how each helps monitor microservices.

Think about numbers, messages, and request paths.

Explain a real-life example of how metrics, logs, and traces can help solve a performance issue in a microservices system.

Imagine a slow website and how you would investigate.

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