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

Resiliency


I want to finish this chapter with a brief discussion of the importance of designing a tracing backend that is resilient to potential, often unintentional, abuse. I am not talking about an under-provisioned cluster, as there is little that can be done there. While operating Jaeger at Uber, we have experienced a number of tracing service degradations or even outages due to a few common mistakes.

Over-sampling

During development, I often recommend engineers to configure the Jaeger tracer with 100% sampling. Sometimes, inadvertently, the same configuration is pushed to production, and if the service is one of those serving high traffic, the tracing backend gets flooded with tracing data. It does not necessarily kill the backend because, as I mentioned previously, all Jaeger components are built with in-memory buffers for temporary storage of spans and handling short traffic spikes, and when those buffers are full, the components begin shedding their load by discarding some of the data...