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Distributed tracing - get a grasp on your production
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Distributed Tracing Velocity2016

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Distributed Tracing : How to do latency analysis for microservice-based applications Velocity 2016

Distributed Tracing Velocity2016

  1. 1. Distributed Tracing How to do latency analysis for microservice-based applications Reshmi Krishna @reshmi9k
  2. 2. About Me  Software Engineer  Platform Architect, Pivotal  Women In Tech Community Members Twitter : @reshmi9k MeetUp : Cloud-Native-New-York
  3. 3. Agenda  Distributed Tracing  Tracers and Tracing Systems  Zipkin  Incorporating distributed tracing into an existing micro service  Demo
  4. 4. From Monolith …. Customer Loyalty Notifications Payment Web Frontend
  5. 5. To Microservices .
  6. 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. 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. 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. 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. 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. 11. Initial Zipkin Architecture
  12. 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. 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. 14. Let’s look at some code & Demo
  15. 15. 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
  16. 16. 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
  • AnaMariaValarezo

    Sep. 9, 2018
  • taemonz

    Dec. 13, 2017
  • coosuu

    Aug. 11, 2017

Distributed Tracing : How to do latency analysis for microservice-based applications Velocity 2016

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