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

Understanding indexing in Cosmos DB


The default configuration for indexing in Cosmos DB makes indexing happen automatically. Hence, whenever we create or update a document in a document collection, all the keys included in the document are indexed. This might sound counter-intuitive, but it is how the system is designed to work. No need for index management, unless you want to optimize your costs better or you require specific queries.

Note

Keep in mind that every index you have in your dataset will have its toll on request units consumed and storage space used. Hence, if you are indexing keys that you are never going to use in search criteria, you are wasting resource units in every write operation.

In contrast, sometimes removing an index can increase the request unit cost of a query as well. Thus, it is very convenient to make sure that we don't remove indexes for keys that are included in search criteria. It is vital to use indexing strategically to come up with the best implementation...