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

Traffic labeling


At a high level, adding metadata to request context and propagating it through the call graph is a way of partitioning the overall traffic to the application along a number of dimensions. As an example, if we label each external request with the type of company product it represents (for Google it could be Gmail, Docs, YouTube, and so on, and for Uber it could be Ridesharing, Uber Eats, Uber Bikes, and so on) and propagate it in the metadata, then we can get a pretty accurate picture of how much traffic served by a data center is attributed to each product line. Strictly speaking, the Pivot Tracing and LDFI techniques I discussed earlier can also be considered as partitioning of the traffic, but the values they pass through metadata are very complex and high cardinality. In this section, I will talk about traffic labeling that uses low-cardinality dimensions.

Testing in production

Testing in production is a common practice today because given the complexity of the internet...