Book Image

Microservices Development Cookbook

By : Paul Osman
Book Image

Microservices Development Cookbook

By: Paul Osman

Overview of this book

Microservices have become a popular choice for building distributed systems that power modern web and mobile apps. They enable you to deploy apps as a suite of independently deployable, modular, and scalable services. With over 70 practical, self-contained tutorials, the book examines common pain points during development and best practices for creating distributed microservices. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so you can copy the code and configuration files and modify them for your own needs. You’ll start by understanding microservice architecture. Next, you'll learn to transition from a traditional monolithic app to a suite of small services that interact to ensure your client apps are running seamlessly. The book will then guide you through the patterns you can use to organize services, so you can optimize request handling and processing. In addition this, you’ll understand how to handle service-to-service interactions. As you progress, you’ll get up to speed with securing microservices and adding monitoring to debug problems. Finally, you’ll cover fault-tolerance and reliability patterns that help you use microservices to isolate failures in your apps. By the end of this book, you’ll have the skills you need to work with a team to break a large, monolithic codebase into independently deployable and scalable microservices.
Table of Contents (16 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Collecting metrics with Prometheus


Prometheus is an open source monitoring and alerting toolkit originally developed in 2012 at SoundCloud. It was inspired by Borgmon at Google. In contrast to the push model employed by systems such as statsd, Prometheus uses a pull model for collecting metrics. Instead of each service being responsible for pushing metrics to a statsd server, Prometheus is responsible for scraping an endpoint exposed by services that have metrics. This inversion of responsibilities provides some benefits when operating metrics at scale. Targets in Prometheus can be configured manually or via service discovery.

In contrast to the hierarchical format that systems such as Graphite use to store metrics data, Prometheus employs a multidimensional data model. Time-series data in Prometheus is identified by a metric name (such as http_request_duration_seconds) and one or more labels (such as service=message-service and method=POST). This format can make it easier to standardize...