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

Long-term profiling


Performance optimization is a job that is never done. Applications are constantly evolving, developing new features, and adapting to new business requirements. This adds complexity and introduces new, often unexpected behaviors into the system. Optimizations done previously may no longer apply, or be offset by performance degradation elsewhere, due to new interactions. The discipline of long-term profiling allows us to keep the degradations in check, detect them early, and fix them before they become a real problem.

The simplest step toward long-term profiling is to monitor trends in performance. If we plot the p99.9 latency of an endpoint today and compare it with a plot captured last month, do we see more recent plots being consistently higher? Then we have a degradation. However, we know better by now than to compare two numbers, so instead, we can compare the distributions of latency, via histograms. Do we see today's histogram shifting toward the long tail? Then we...