Why Distributed tracing (Jaeger, Zipkin) in Microservices? - Purpose & Use Cases

The Big Idea

What if you could instantly see every step your app takes, like a GPS for your code?

The Scenario

Imagine you run a busy restaurant with many chefs in different kitchens. When a customer orders a complex meal, you try to track which chef is cooking which part by asking each chef separately and writing notes by hand.

The Problem

This manual tracking is slow and confusing. You get lost in notes, miss steps, and can't quickly find where delays or mistakes happen. It's hard to fix problems or improve service because you don't see the full picture.

The Solution

Distributed tracing tools like Jaeger and Zipkin automatically follow each customer order through every kitchen station. They collect clear, connected records of every step, showing exactly where time is spent and where issues occur.

Before vs After

✗ Before

Log each service call separately without linking context

✓ After

Use tracing libraries to auto-inject trace IDs and collect spans across services

What It Enables

It lets you see the entire journey of a request across many services, making it easy to find and fix bottlenecks or errors fast.

Real Life Example

A large online store uses distributed tracing to quickly spot why checkout is slow—finding a slow payment service call—and fixes it before customers complain.

Key Takeaways

Manual tracking of requests across services is confusing and error-prone.

Distributed tracing automatically links all steps of a request for clear visibility.

This helps teams quickly find and solve performance or error issues in complex systems.

Practice

1. What is the main purpose of distributed tracing tools like Jaeger or Zipkin in microservices?

easy

A. To track and visualize requests as they flow through multiple services

B. To store large amounts of user data securely

C. To replace load balancers in service communication

D. To encrypt network traffic between microservices

Solution

1. Step 1: Understand the role of distributed tracing

   Distributed tracing tools help monitor how requests move through different microservices by collecting timing and metadata.

2. Step 2: Identify the main function of Jaeger and Zipkin

   They visualize and analyze traces made of spans to find bottlenecks or errors in service chains.

3. Final Answer:

   To track and visualize requests as they flow through multiple services -> Option A

4. Quick Check:

   Distributed tracing = track requests flow [OK]

Hint: Distributed tracing = tracking requests across services [OK]

Common Mistakes:

• Confusing tracing with data storage
• Thinking tracing replaces load balancers
• Assuming tracing encrypts traffic

2. Which of the following is the correct way to propagate trace context between microservices using HTTP headers?

easy

A. Add Cookie header with span ID

B. Add Authorization header with trace ID

C. Add X-B3-TraceId and X-B3-SpanId headers to the outgoing request

D. Add Content-Type header with trace ID value

Solution

1. Step 1: Recall standard trace context headers

   Distributed tracing uses specific headers like X-B3-TraceId and X-B3-SpanId to pass trace info between services.

2. Step 2: Identify correct header usage

   Headers like Authorization, Content-Type, or Cookie are unrelated to tracing context propagation.

3. Final Answer:

   Add X-B3-TraceId and X-B3-SpanId headers to the outgoing request -> Option C

4. Quick Check:

   Trace context headers = X-B3-TraceId, X-B3-SpanId [OK]

Hint: Trace context uses X-B3 headers, not auth or content-type [OK]

Common Mistakes:

• Using unrelated HTTP headers for trace context
• Forgetting to propagate span ID
• Confusing trace ID with authentication tokens

3. Given the following trace spans collected by Zipkin, what is the total time taken by the root request?

Span A (root): start=0ms, duration=50ms
Span B (child of A): start=10ms, duration=20ms
Span C (child of A): start=35ms, duration=10ms

medium

A. 50ms

B. 40ms

C. 30ms

D. 60ms

Solution

1. Step 1: Understand root span duration

   The root span duration represents the total time of the entire request, including child spans.

2. Step 2: Analyze given spans

   Span A starts at 0ms and lasts 50ms, so total time is 50ms regardless of child spans.

3. Final Answer:

   50ms -> Option A

4. Quick Check:

   Root span duration = total request time = 50ms [OK]

Hint: Root span duration = total request time [OK]

Common Mistakes:

• Adding child spans durations incorrectly
• Ignoring root span duration
• Confusing start times with total duration

4. You notice that your distributed tracing data in Jaeger shows many missing spans for some services. What is the most likely cause?

medium

A. The network latency is too low

B. The services have too many CPU cores

C. The database is down

D. The services are not propagating the trace context headers correctly

Solution

1. Step 1: Identify cause of missing spans

   If spans are missing, it usually means trace context was not passed properly between services.

2. Step 2: Eliminate unrelated causes

   CPU cores, database status, or low network latency do not cause missing trace spans.

3. Final Answer:

   The services are not propagating the trace context headers correctly -> Option D

4. Quick Check:

   Missing spans = trace context not propagated [OK]

Hint: Missing spans? Check trace context propagation [OK]

Common Mistakes:

• Blaming unrelated system resources
• Ignoring header propagation
• Assuming network latency causes missing spans

5. You want to design a distributed tracing system for a microservices architecture with 100 services and high request volume. Which approach best ensures scalability and minimal overhead?

hard

A. Trace every request fully and store all spans in a single central database

B. Use sampling to trace only a subset of requests and propagate trace context with lightweight headers

C. Disable trace context propagation and log spans locally in each service

D. Use synchronous calls to the tracing backend for every span creation

Solution

1. Step 1: Consider scalability needs

   Tracing every request fully in a large system causes high overhead and storage issues.

2. Step 2: Identify best practice for high volume tracing

   Sampling reduces load by tracing only some requests, and lightweight headers keep propagation efficient.

3. Step 3: Eliminate poor options

   Disabling propagation loses trace linkage; synchronous calls add latency; central DB can bottleneck.

4. Final Answer:

   Use sampling to trace only a subset of requests and propagate trace context with lightweight headers -> Option B

5. Quick Check:

   Sampling + lightweight headers = scalable tracing [OK]

Hint: Sampling + lightweight headers = scalable tracing [OK]

Common Mistakes:

• Tracing all requests causing overhead
• Ignoring trace context propagation
• Using synchronous calls causing latency