Distributed tracing allows you to analyze latency issues in microservices applications. It works by collecting transaction data from each service call into trace trees with unique IDs. Zipkin is an open source distributed tracing system that uses this approach. It provides visualization of traces and spans to help identify latency bottlenecks and determine which microservice was responsible. Spring Cloud Sleuth is a tracer that can integrate services with Zipkin by adding trace IDs and span IDs to logs to correlate data.

1. Distributed Tracing
How to do latency analysis for
microservice-based applications
Reshmi Krishna
@reshmi9k

2. About Me
 Software Engineer
 Platform Architect, Pivotal
 Women In Tech Community Members
Twitter : @reshmi9k
MeetUp : Cloud-Native-New-York

3. Agenda
 Distributed Tracing
 Tracers and Tracing Systems
 Zipkin
 Incorporating distributed tracing into an existing micro service
 Demo

4. From Monolith ….
Customer
Loyalty
Notifications
Payment
Web Frontend

5. To Microservices .

6. Troubleshooting Latency issues
 When was the event? How long did it take?
 How do I know it was slow?
 Why did it take so long?
 Which microservice was responsible?

7. Distributed Tracing
 Distributed Tracing is a process of collecting end-to-end transaction graphs in near real
time
 A trace represents the entire journey of a request
 A span represents single operation call
 Distributed Tracing Systems are often used for this purpose. Zipkin is an example
 As a request is flowing from one microservice to another, tracers add logic to create
unique trace Id, span Id

8. Visualization - Traces & Spans
UI
Trace Id : 1, Span Id : 1
Account-Microservice
Trace Id : 1, Parent Id : 2, Span Id : 5
Back-Office-Microservice
Trace Id : 1, Parent Id : 1, Span Id : 2
Customer-Microservice
Trace Id : 1, Parent Id : 2, Span Id : 4

9. Dapper Paper By Google
@reshmi9k
@reshmi9k
This paper described Dapper, which is Google’s production distributed systems
tracing infrastructure
Design Goals :
Low overhead
Application-level transparency
Scalability

10. Zipkin
Zipkin is a distributed tracing system
Implementation based on Dapper paper, Google
Aggregate spans into trace trees
Manages both collection and lookup of the data
In 2015, OpenZipkin became the primary fork

11. Initial Zipkin Architecture

12. Tracers
 Tracers add logic to create unique trace ID
 Trace ID is generated when the first request is made
 Span ID is generated as the request arrives at each microservice
 Example tracer is Spring Cloud Sleuth
 Tracers execute in your production apps! They are written to not log too much
 Tracers have instrumentation or sampling policy

13. Demo : Architecture Diagram
Spring Cloud
Sleuth
Collector
Span
Store
Transport
Mq/Http/Log
Spring Cloud
Sleuth
Spring Cloud
Sleuth
Spring Cloud
Sleuth
Query
ServerZipkin UI
ZIPKIN
APP
APP
APP
APP

14. Let’s look at some code & Demo

25. Summary
 Distributed tracing allows you to quickly see latency issues in your system
 Zipkin is a great tool to visualize the latency graph and system dependencies
 Spring Cloud Sleuth integrates with Zipkin and grants you log correlation
 Log correlation allows you to match logs for a given trace
 Pivotal Cloud Foundry makes integration of your apps and Spring Cloud Sleuth and Zipkin
easier

26. Links
 Dapper, Google : http://research.google.com/pubs/pub36356.html
 Code for this presentation : https://github.com/reshmik/DistributedTracingDemo_Velocity2016.git
 Sleuth’s documentation: http://cloud.spring.io/spring-cloud-sleuth/spring-cloud-sleuth.html
 Repo with Spring Boot Zipkin server: https://github.com/openzipkin/zipkin-reporter-java.git
 Zipkin deployed as an PCF :https://github.com/reshmik/Zipkin/tree/master/spring-cloud-sleuth-
samples/spring-cloud-sleuth-sample-zipkin-stream
 Pivotal Web Services trial : https://run.pivotal.io/
 PivotalCloudFoundry on your laptop : https://docs.pivotal.io/pcf-dev/
@reshmi9k