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

Performance analysis


Using tracing data for application performance analysis is the classic use case of distributed tracing. Different aspects of application performance can be investigated via tracing:

  • Availability: Is the application responding to end user queries? When the application is not responding to end users, or in other words we are in an outage situation, tracing can be used to pinpoint the location in a complex architecture where things go wrong.

  • Correctness: Does the application provide accurate answers? For example, if you are trying to call a car in the middle of New York City using a ride-sharing mobile app and the system responds with an expected time of arrival of 55 minutes, the correctness of that response is suspicious. Perhaps the system was trying to use a microservice with a highly accurate but slow algorithm, which did not finish in time, and the request failed over to another microservice with a faster but less accurate algorithm. By following the path of a request...