OpenTelemetry is a revolution in observability data. Instead of running multiple uncoordinated pipelines, OpenTelemetry provides users with a single integrated stream of data, providing multiple sources of high-quality telemetry data: tracing, metrics, logs, RUM, eBPF, and more. This practical guide shows you how to set up, operate, and troubleshoot the OpenTelemetry observability system. Authors Austin Parker, head of developer relations at Lightstep and OpenTelemetry Community Maintainer, and Ted Young, cofounder of the OpenTelemetry project, cover every OpenTelemetry component, as well as observability best practices for many popular cloud, platform, and data services such as Kubernetes and AWS Lambda. You'll learn how OpenTelemetry enables OSS libraries and services to provide their own native instrumentation--a first in the industry. Ideal for application developers, OSS maintainers, operators and infrastructure teams, and managers and team leaders, this book guides you through: The principles of modern observability All OpenTelemetry components--and how they fit together A practical approach to instrumenting platforms and applications Methods for installing, operating, and troubleshooting an OpenTelemetry-based observability solution Ways to roll out and maintain end-to-end observability across a large organization How to write and maintain consistent, high-quality instrumentation without a lot of work

This Springer Brief presents a basic algorithm that provides a correct solution to finding an optimal state change attempt, as well as an enhanced algorithm that is built on top of the well-known trie data structure. It explores correctness and algorithmic complexity results for both algorithms and experiments comparing their performance on both real-world and synthetic data. Topics addressed include optimal state change attempts, state change effectiveness, different kind of effect estimators, planning under uncertainty and experimental evaluation. These topics will help researchers analyze tabular data, even if the data contains states (of the world) and events (taken by an agent) whose effects are not well understood. Event DBs are omnipresent in the social sciences and may include diverse scenarios from political events and the state of a country to education-related actions and their effects on a school system. With a wide range of applications in computer science and the social sciences, the information in this Springer Brief is valuable for professionals and researchers dealing with tabular data, artificial intelligence and data mining. The applications are also useful for advanced-level students of computer science.

Most applications today are distributed in some fashion. Monitoring the health and performance of these distributed architectures requires a new approach. Enter distributed tracing, a method of profiling and monitoring applications-especially those that use microservice architectures. There's just one problem: distributed tracing can be hard. But it doesn't have to be. With this practical guide, you'll learn what distributed tracing is and how to use it to understand the performance and operation of your software. Key players at LightStep walk you through instrumenting your code for tracing, collecting the data that your instrumentation produces, and turning it into useful, operational insights. If you want to start implementing distributed tracing, this book tells you what you need to know. You'll learn: The pieces of a distributed tracing deployment: Instrumentation, data collection, and delivering value Best practices for instrumentation (the methods for generating trace data from your service) How to deal with or avoid overhead, costs, and sampling How to work with spans (the building blocks of request-based distributed traces) and choose span characteristics that lead to valuable traces Where distributed tracing is headed in the future