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Cloud-Native Observability with OpenTelemetry
Cloud-Native Observability with OpenTelemetry

Cloud-Native Observability with OpenTelemetry: Learn to gain visibility into systems by combining tracing, metrics, and logging with OpenTelemetry

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Cloud-Native Observability with OpenTelemetry

Chapter 1: The History and Concepts of Observability

The term observability has only been around in the software industry for a short time, but the concepts and goals it represents have been around for much longer. Indeed, ever since the earliest days of computing, programmers have been trying to answer the question: is the system doing what I think it should be?

For some, observability consists of buying a one-size-fits-all solution that includes logs, metrics, and traces, then configuring some off-the-shelf integrations and calling it a day. These tools can be used to increase visibility into a piece of software's behavior by providing mechanisms to produce and collect telemetry. The following are some examples of telemetry that can be added to a system:

  • Keeping a count of the number of requests received
  • Adding a log entry when an event occurs
  • Recording a value for current memory consumption on a machine
  • Tracing a request from a client all the way to a backend service

However, producing high-quality telemetry is only one part of the observability challenge. The other part is ensuring that events occurring across the different types of telemetry can be correlated in meaningful ways during analysis. The goal of observability is to answer questions that you may have about the system:

  • If a problem occurred in production, what evidence would you have to be able to identify it?
  • Why is this service suddenly overwhelmed when it was fine just a minute ago?
  • If a specific condition from a client triggers an anomaly in some underlying service, would you know it without customers or support calling you?

These are some of the questions that the domain of observability can help answer. Observability is about empowering the people who build and operate distributed applications to understand their code's behavior while running in production. In this chapter, we will explore the following:

  • Understanding cloud-native applications
  • Looking at the shift to DevOps
  • Reviewing the history of observability
  • Understanding the history of OpenTelemetry
  • Understanding the concepts of OpenTelemetry

Before we begin looking at the history of observability, it's important to understand the changes in the software industry that have led to the need for observability in the first place. Let's start with the shift to the cloud.

Understanding cloud-native applications

The way applications are built and deployed has drastically changed in the past few years with the increased adoption of the internet. An unprecedented increase in demand for services (for example, streaming media, social networks, and online shopping) powered by software has raised expectations for those services to be readily available. In addition, this increase in demand has fueled the need for developers to be able to scale their applications quickly. Cloud providers, such as Microsoft, Google, and Amazon, offer infrastructure to run applications at the click of a button and at a fraction of the cost, and reduce the risk of deploying servers in traditional data centers. This enables developers to experiment more freely and reach a wider audience. Alongside this infrastructure, these cloud providers also offer managed services for databases, networking infrastructure, message queues, and many other services that, in the past, organizations would control internally.

One of the advantages these cloud-based providers offer is freeing up organizations to focus on the code that matters to their businesses. This replaces costly and time-consuming hardware implementations, or operating services they lack expertise in. To take full advantage of cloud platforms, developers started looking at how applications that were originally developed as monoliths could be re-architected to take advantage of cloud platforms. The following are challenges that could be encountered when deploying monoliths to a cloud provider:

  • Scaling a monolith is traditionally done by increasing the number of resources available to the monolith, also known as vertical scaling. Vertically scaling applications can only go as far as the largest available resource offered by a cloud provider.
  • Improving the reliability of a monolith means deploying multiple instances to handle multiple failures, thus avoiding downtime. This is also known as horizontal scaling. Depending on the size of the monolith, this could quickly ramp up costs. This can also be wasteful if not all components of the monolith need to be replicated.

The specific challenges of building applications on cloud platforms have led developers to increasingly adopt a service-oriented architecture, or microservice architecture, that organizes applications as loosely coupled services, each with limited scope. The following figure shows a monolith architecture on the left, where all the services in the application are tightly coupled and operate within the same boundary. In contrast, the microservices architecture on the right shows us that the services are loosely coupled, and each service operates independently:

Figure 1.1 – Monolith versus microservices architecture

Figure 1.1 – Monolith versus microservices architecture

Applications built using microservices architecture provide developers with the ability to scale only the components needed to handle the additional load, meaning horizontal scaling becomes a much more attractive option. As it often does, a new architecture comes with its own set of trade-offs and challenges. The following are some of the new challenges cloud-native architecture presents that did not exist in traditional monolithic systems:

  • Latency introduced where none existed before, causing applications to fail in unexpected ways.
  • Dependencies can and will fail, so applications must be built defensively to minimize cascading failures.
  • Managing configuration and secrets across services is difficult.
  • Service orchestration becomes complex.

With this change in architecture, the scope of each application is reduced significantly, making it easier to understand the needs of scaling each component. However, the increased number of independent services and added complexity also creates challenges for traditional operations (ops) teams, meaning organizations would also need to adapt.

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Key benefits

  • Get to grips with OpenTelemetry, an open-source cloud-native software observability standard
  • Use vendor-neutral tools to instrument applications to produce better telemetry and improve observability
  • Understand how telemetry data can be correlated and interpreted to understand distributed systems

Description

Cloud-Native Observability with OpenTelemetry is a guide to helping you look for answers to questions about your applications. This book teaches you how to produce telemetry from your applications using an open standard to retain control of data. OpenTelemetry provides the tools necessary for you to gain visibility into the performance of your services. It allows you to instrument your application code through vendor-neutral APIs, libraries and tools. By reading Cloud-Native Observability with OpenTelemetry, you’ll learn about the concepts and signals of OpenTelemetry - traces, metrics, and logs. You’ll practice producing telemetry for these signals by configuring and instrumenting a distributed cloud-native application using the OpenTelemetry API. The book also guides you through deploying the collector, as well as telemetry backends necessary to help you understand what to do with the data once it's emitted. You’ll look at various examples of how to identify application performance issues through telemetry. By analyzing telemetry, you’ll also be able to better understand how an observable application can improve the software development life cycle. By the end of this book, you’ll be well-versed with OpenTelemetry, be able to instrument services using the OpenTelemetry API to produce distributed traces, metrics and logs, and more.

Who is this book for?

This book is for software engineers, library authors, and systems operators looking to better understand their infrastructure, services and applications by leveraging telemetry data like never before. Working knowledge of Python programming is assumed for the example applications that you’ll be building and instrumenting using the OpenTelemetry API and SDK. Some familiarity with Go programming, Linux, and Docker is preferable to help you set up additional components in various examples throughout the book.

What you will learn

  • Understand the core concepts of OpenTelemetry
  • Explore concepts in distributed tracing, metrics, and logging
  • Discover the APIs and SDKs necessary to instrument an application using OpenTelemetry
  • Explore what auto-instrumentation is and how it can help accelerate application instrumentation
  • Configure and deploy the OpenTelemetry Collector
  • Get to grips with how different open-source backends can be used to analyze telemetry data
  • Understand how to correlate telemetry in common scenarios to get to the root cause of a problem
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Length: 386 pages
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Language : English
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Table of Contents

16 Chapters
Section 1: The Basics Chevron down icon Chevron up icon
Chapter 1: The History and Concepts of Observability Chevron down icon Chevron up icon
Chapter 2: OpenTelemetry Signals – Traces, Metrics, and Logs Chevron down icon Chevron up icon
Chapter 3: Auto-Instrumentation Chevron down icon Chevron up icon
Section 2: Instrumenting an Application Chevron down icon Chevron up icon
Chapter 4: Distributed Tracing – Tracing Code Execution Chevron down icon Chevron up icon
Chapter 5: Metrics – Recording Measurements Chevron down icon Chevron up icon
Chapter 6: Logging – Capturing Events Chevron down icon Chevron up icon
Chapter 7: Instrumentation Libraries Chevron down icon Chevron up icon
Section 3: Using Telemetry Data Chevron down icon Chevron up icon
Chapter 8: OpenTelemetry Collector Chevron down icon Chevron up icon
Chapter 9: Deploying the Collector Chevron down icon Chevron up icon
Chapter 10: Configuring Backends Chevron down icon Chevron up icon
Chapter 11: Diagnosing Problems Chevron down icon Chevron up icon
Chapter 12: Sampling Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(8 Ratings)
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4 star 25%
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Sean Spencer Jun 13, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Before reading this book, I had tried to get involved in the OpenTelemetry project, both as a user and as a contributor, but I never really had a picture or mental model for what all the manifold pieces were really doing or what they were there for.This book brings that mental model to the fore, moreso than any other documentation or resource I've found so far. Truly indispensable.
Amazon Verified review Amazon
Phillip Carter May 31, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I really enjoyed the book overall. If you're new to OpenTelemetry and you're trying to use it to get Observability into your systems, get this book right now!The book covers all of the essentials: the main concepts, how to set up tracing/metrics/logs with the SDK, best practices for generating data, setting up and using different features of the OpenTelemetry Collector, strategies for deploying the collector in production, and an overview of Sampling strategies. In time, the OpenTelemetry documentation may be comprehensive enough to obviate the contents of this book. But until then, get this book! It might take a while before that happens.The highlights for me were:* The succinct yet complete overview of the OpenTelemetry collector* Best practices around generating telemetry data (in particular, how to set up Resources and what kinds of stuff you'd like to capture)* Really all of Section 2* Overview of the different major concepts (tracing/metrics/logs signals, semantic conventions, etc.)The areas I felt could improve:* Spending time on history of the project felt unnecessary to me, even though I can see why it would be added. As someone who came into OpenTelemetry and Observability in Summer of 2021, I feel like the only thing I need to know is that there's some legacy stuff called OpenTracing and OpenCensus and if I run into it, OpenTelemetry lets me interoperate with it. But otherwise, I'd rather not spend time learning about these older projects when I'm looking to learn OpenTelemetry instead* Second 2 had several chapters showing manual instrumentation, and had a sort of coup de grace with the autoinstrumentation. I can understand why that particular decision was made, but I feel like it could have also been inverted: lead with autoinstrumentation and how powerful it is, then enrich what's generated on my behalf. This is perhaps more of a philosophical point than anything else.I've been working in and around OpenTelemetry since Fall 2021, and it's been difficult to comprehend due to its vastness and lack of documentation. This book was very helpful, and I learned a lot from it, even though I've been contributing to OpenTelemetry for a few months.I'll reiterate my first point: if you are new to OpenTelemetry and you're trying to use it, get this book now! You won't find a better, more comprehensive guide.
Amazon Verified review Amazon
Ocelotl May 23, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book! Clearly written, will help anyone who is interested in OpenTelemetry get up and running. Experienced users will also enjoy the detailed explanations on how the standard works at a higher level.
Amazon Verified review Amazon
Matt W May 10, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
OpenTelemetry is becoming the go-to standard for open source, vendor neutral observability. The vast ecosystem of tools is both powerful and flexible once you can get past the learning curve. This is the most up to date and comprehensive book on the topic and it will prepare you with all the knowledge you need to put OpenTelemetry to use in your organization.
Amazon Verified review Amazon
Peter May 10, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book has a good primer on how the industry has reached the point it has with telemetry and tracing. Then dives into the specifics of open telemetry. If you're just getting started, I think this is a good place to start.
Amazon Verified review Amazon
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