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

Contextualized logs


We now have a pretty good idea about what the HotROD application does, if not exactly how it does it. For example, why does the frontend service call the /customer endpoint of the customer service? Of course, we can look at the source code, but we are trying to approach this from the point of view of application monitoring. One direction we could take is to look at the logs the application writes to its standard output (Figure 2.7).

Figure 2.7: Typical logging output

It is quite difficult to follow the application logic from these logs and we are only looking at the logs when a single request was executed by the application.

We are also lucky that the logs from four different microservices are combined in a more-or-less consistent stream. Imagine many concurrent requests going through the system and through microservices running in different processes! The logs would become nearly useless in that case. So, let's take a different approach. Let's view the logs collected by...