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

Summary


Sampling is used by tracing systems to reduce the performance overhead on the traced applications, and to control the amount of data that needs to be stored in the tracing backends. There are two important sampling techniques: head-based consistent sampling, which makes the sampling decision at the beginning of the request execution, and tail-based sampling, which makes the sampling decision after the execution.

Most existing tracing systems implement head-based sampling that imposes minimum overhead on the applications. Various sampling algorithms can be used to tune the sampling behavior and the impact on the tracing backend. Jaeger implements adaptive sampling that reduces the operational burden for the tracing teams and provides more equitable handling of endpoints with vastly different traffic volumes. A few commercial and open source solutions of tail-based sampling have emerged as well.

This concludes the part of the book dedicated to the data gathering problem in distributed...