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Mastering Distributed Tracing
Mastering Distributed Tracing

Mastering Distributed Tracing: Analyzing performance in microservices and complex systems

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Mastering Distributed Tracing

Part II. Data Gathering Problem

Data Gathering Problem

Chapter 4. Instrumentation Basics with OpenTracing

Instrumentation Basics with OpenTracing

In the previous chapter, we looked into the theory behind end-to-end tracing, and various architectural decisions one must make when building a distributed tracing infrastructure, including which data formats can be used for propagating metadata between processes and for exporting tracing data to a tracing backend. Fortunately, as we will see in this chapter, an end user of a tracing infrastructure, someone who wants to instrument their business application, or their open source framework, or library, typically does not need to worry about those decisions.

We only briefly touched upon the notion of instrumentation and trace points before, so in this chapter, we will dive deep into the question of instrumentation, using three canonical "Hello, World!" applications in Go, Java, and Python. You may be having Jules Winnfield's reflex right now: "Say Hello, World! again," but I promise to make it interesting...

Chapter 5. Instrumentation of Asynchronous Applications

Instrumentation of Asynchronous Applications

In Chapter 4, Instrumentation Basics with OpenTracing, we reviewed the basics of instrumenting a microservices-based application for distributed tracing using the OpenTracing APIs. If you went through all the exercises, you deserve a medal! The Hello application was intentionally very simple and involved only blocking synchronous calls between microservices.

In this chapter, we will attempt to instrument an online chat application, Tracing Talk, which uses asynchronous messaging-based interactions between microservices built on top of Apache Kafka. We will see how metadata context can be passed through messaging systems using the same OpenTracing primitives we already discussed, and how causal relationships between spans can be modeled differently than in the plain RPC scenarios.

We will continue using the OpenTracing API, even though the same instrumentation principles would apply to other tracing APIs, such as Zipkin...

Chapter 6. Tracing Standards and Ecosystem

Tracing Standards and Ecosystem

In the microservices-ruled world, end-to-end tracing is no longer a "nice-to-have" feature, but "table stakes" for understanding modern cloud-native applications. In Chapter 4, Instrumentation Basics with OpenTracing, we saw an example of what it takes to manually instrument a simple "Hello, world!" application for distributed tracing. If, after reading it, you were left with the impression, "oh, that is a lot of work," then it achieved one of its goals. Developing and deploying instrumentation in order to get high-quality tracing data is absolutely the largest, but not the only, challenge of rolling out a distributed tracing solution in an organization. In Chapter 13, Implementing Tracing in Large Organizations, we will review some practical techniques for making the process easier from an organizational point of view.

In this chapter, we will discuss:

  • Open source projects aiming to...

Chapter 7. Tracing with Service Meshes

Tracing with Service Meshes

In previous chapters, we discussed multiple methods of extracting tracing data from applications, either by adding instrumentation directly to the application code, or enabling the instrumentation dynamically at runtime through configuration. I also mentioned agent-based instrumentation, often provided by commercial APM vendors, which works by injecting trace points externally into the customer's program, using techniques such as monkey-patching and bytecode manipulation. All these methods can be classified as white-box instrumentation, since they all require modification of the application's code, either explicitly or implicitly at runtime. In Chapter 3, Distributed Tracing Fundamentals, we also discussed black-box techniques that work purely by correlating externally observed telemetry, such as logs used by the Mystery Machine [1].

In this chapter, we will discuss and try in practice how service meshes...

Chapter 8. All About Sampling

All About Sampling

The gathering of monitoring data in production is always a compromise between the costs, in terms of storage and performance overhead, and the expressiveness of the collected data. The more data we collect, the better we hope to be able to diagnose the situation, should something go wrong, yet we don't want to slow down the applications or pay exorbitant bills for storage. Even though most logging frameworks support multiple levels of log severity, a common wisdom is to tune the loggers in production to discard anything logged with the debug level or lower. Many organizations even adopt the rule that successful requests should leave no logs at all, and you only log when there is some issue with the request.

Distributed tracing is not immune to this compromise either. Depending on the verbosity of the instrumentation, tracing data can easily exceed the volume of the actual business traffic sustained by an application. Collecting all that data in...

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Key benefits

  • A thorough conceptual introduction to distributed tracing
  • An exploration of the most important open standards in the space
  • A how-to guide for code instrumentation and operating a tracing infrastructure

Description

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.

Who is this book for?

Any developer interested in testing large systems will find this book very revealing and in places, surprising. Every microservice architect and developer should have an insight into distributed tracing, and the book will help them on their way. System administrators with some development skills will also benefit. No particular programming language skills are required, although an ability to read Java, while non-essential, will help with the core chapters.

What you will learn

  • How to get started with using a distributed tracing system
  • How to get the most value out of end-to-end tracing
  • Learn about open standards in the space
  • Learn about code instrumentation and operating a tracing infrastructure
  • Learn where distributed tracing fits into microservices as a core function
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Publication date : Feb 28, 2019
Length: 444 pages
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Language : English
ISBN-13 : 9781788628464
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Publication date : Feb 28, 2019
Length: 444 pages
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ISBN-13 : 9781788628464
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Table of Contents

7 Chapters
I. Introduction Chevron down icon Chevron up icon
II. Data Gathering Problem Chevron down icon Chevron up icon
III. Getting Value from Tracing Chevron down icon Chevron up icon
IV. Deploying and Operating Tracing Infrastructure Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Leave a review - let other readers know what you think Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(3 Ratings)
5 star 100%
4 star 0%
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1 star 0%
Anonymous Reader Nov 21, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The chapters range from concepts to practical, a must read for those into the topic.
Amazon Verified review Amazon
Richard Marques Oct 13, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very good book for everyone that wants to understand the problems that tracing will be solved using those technologies: open tracing and jaeger.
Amazon Verified review Amazon
X Feb 17, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is the rare book that is more helpful than existing online documentation. I was tasked with implementing distributed tracing in a Go application; after several days of struggling to piece together an understanding of distributed tracing theory and how to use Jaeger and OpenTracing, I gave in and purchased this book. After reading the first few chapters, I was able to get a proof of concept implemented and understand it well enough to explain it to a coworker. It's an optimal combination of theory and application.While all of the information in this book can be found online; it was far more helpful to have it all in one place, organized by topic. I really appreciated the language-specific examples (fwiw, the languages included are Java, Python, and Go), as they provided a starting point that made it easier to understand the docs for the Go Jaeger library.For context, I am a junior level SRE at a FAANG; prior to this I was a full-stack developer for 3-4 years. I've worked with microservices for the past several years.
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