Book Image

Mastering Distributed Tracing

By : Yuri Shkuro
Book Image

Mastering Distributed Tracing

By: Yuri Shkuro

Overview of this book

Mastering Distributed Tracing will equip you to operate and enhance your own tracing infrastructure. Through practical exercises and code examples, you will learn how end-to-end tracing can be used as a powerful application performance management and comprehension tool. The rise of Internet-scale companies, like Google and Amazon, ushered in a new era of distributed systems operating on thousands of nodes across multiple data centers. Microservices increased that complexity, often exponentially. It is harder to debug these systems, track down failures, detect bottlenecks, or even simply understand what is going on. Distributed tracing focuses on solving these problems for complex distributed systems. Today, tracing standards have developed and we have much faster systems, making instrumentation less intrusive and data more valuable. Yuri Shkuro, the creator of Jaeger, a popular open-source distributed tracing system, delivers end-to-end coverage of the field in Mastering Distributed Tracing. Review the history and theoretical foundations of tracing; solve the data gathering problem through code instrumentation, with open standards like OpenTracing, W3C Trace Context, and OpenCensus; and discuss the benefits and applications of a distributed tracing infrastructure for understanding, and profiling, complex systems.
Table of Contents (21 chapters)
Mastering Distributed Tracing
Contributors
Preface
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15
Afterword
Index

Ad hoc analysis


In October 2018, the members of the tracing team from Facebook gave a presentation at the Distributed Tracing – NYC meetup [2], where they talked about a new direction that they are taking with their tracing system, Canopy. While not based on open source technologies like Apache Flink, the feature extraction framework in Canopy was conceptually similar to the approach we presented in this chapter.

The API for building new feature extractions was open to all Facebook engineers, but it often had a steep learning curve and required fairly deep familiarity with the overall tracing infrastructure and its data models. More importantly, new feature extractors had to be deployed in production as part of Canopy itself, which meant the Canopy team still had to be deeply involved in reviewing the code and deploying the new analysis algorithms. Finally, feature extraction was primarily designed to work on live data, not on historical data. All of this was creating enough procedural friction...