gives rise to a range of benefits including individual scaling and individual deployments. However, it also introduces challenges regarding configuration management, load balancing, and latency analysis. Reshmi Krishna discusses how companies like Twitter analyze microservices latency in real time and demonstrates how to integrate popular distributed tracing tools like Zipkin into an existing application with just a few lines of code. At the end, we will also see a demo of tracing capabilities from PCF Metrics.

1. Distributed Tracing
Latency analysis for microservices
Reshmi Krishna
@reshmi9k

2. About Me
 Software Engineer
 Senior Platform Architect, Pivotal
 Conference Speaker
MeetUp : Cloud-Native-New-York
@reshmi9k

4. Agenda
 Distributed Tracing
 Tracers and Tracing Systems
 Zipkin
 Demo – Spring Cloud Sleuth, Zipkin, PCF Metrics

6. Everything is going to be okay!

7. Until

9. Let’s Debug

10. It doesn’t look like this
QueryHandlerService
IndexerService
BackendService
PageRankingService
Web Frontend

11. More like this

13. 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?

14. 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

15. 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

16. Visualization - Traces & Spans
service1
Trace Id : 1, Span Id : 1
service4
Trace Id : 1, Parent Id : 2, Span Id : 4
service2
Trace Id : 1, Parent Id : 1, Span Id : 2
service3
Trace Id : 1, Parent Id : 2, Span Id : 3

17. 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

18. 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

19. Initial Zipkin Architecture

20. 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

21. Let’s look at some code & Demo

22. 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-java
 Zipkin deployed as an PCF https://github.com/spring-cloud-samples/sleuth-documentation-
apps/tree/master/zipkin-server
 Pivotal Web Services trial : https://run.pivotal.io/
 PivotalCloudFoundry on your laptop : https://docs.pivotal.io/pcf-dev/
@reshmi9k