Book Image

Guide to NoSQL with Azure Cosmos DB

By : Gaston C. Hillar, Daron Yöndem
Book Image

Guide to NoSQL with Azure Cosmos DB

By: Gaston C. Hillar, Daron Yöndem

Overview of this book

Cosmos DB is a NoSQL database service included in Azure that is continuously adding new features and has quickly become one of the most innovative services found in Azure, targeting mission-critical applications at a global scale. This book starts off by showing you the main features of Cosmos DB, their supported NoSQL data models and the foundations of its scalable and distributed architecture. You will learn to work with the latest available tools that simplify your tasks with Cosmos DB and reduce development costs, such as the Data Explorer in the Azure portal, Microsoft Azure Storage Explorer, and the Cosmos DB Emulator. Next, move on to working with databases and document collections. We will use the tools to run schema agnostic queries against collections with the Cosmos DB SQL dialect and understand their results. Then, we will create a first version of an application that uses the latest .NET Core SDK to interact with Cosmos DB. Next, we will create a second version of the application that will take advantage of important features that the combination of C# and the .NET Core SDK provides, such as POCOs and LINQ queries. By the end of the book, you will be able to build an application that works with a Cosmos DB NoSQL document database with C#, the .NET Core SDK, LINQ, and JSON.
Table of Contents (13 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Tracking consumed request units with client-side code


There are many variables that determine the way Cosmos DB calculates the request unit charge for each operation. The first variable is the amount of data an operation or query reads or writes. 1 RU is how much effort it takes to read 1 KB of data from Cosmos DB that directly references the document with its URI or self link. Writes are more expensive than reads because they require more resources. The amount of properties and data you have in a document affects the cost as well. The data consistency levels, such as strong or bounded staleness, can cause more reads. Indexes affect your query costs. Your query patterns and the finally stored procedures and triggers you defined will add more request units with more complicated query executions. These are all factors that can be optimized, fine-tuned, and monitored.

Now we will establish a breakpoint in one of the methods that reads and updates a scheduled competition with platforms to inspect...