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

Five shades of tracing


The tracing ecosystem today is fairly fragmented and probably confusing. Traditional APM vendors made a lot of investment in the agent-based instrumentation, all with their own APIs and data protocols. After Twitter released Zipkin, the first industrial-grade open source tracing system, it started gaining traction among the users as the de facto standard, at least for its B3 metadata propagation format (many systems at Twitter were named after birds, and Zipkin was originally called Big Brother Bird, or B3, which was used as the prefix for HTTP headers, for example, X-B3-TraceId).

In 2016, both Google and Amazon announced the availability of their own managed tracing systems, Stackdriver and X-Ray, both with their own metadata and trace data formats. These systems can be used to trace the applications running in the respective clouds, as well as receive tracing data from the internal applications running, by their customers on premises. A number of other tracing systems...