You plan to store order data in Azure Cosmos DB for NoSQL account. The data contains information about orders and their associated items.

You plan to store order data in Azure Cosmos DB for NoSQL account. The data contains information about orders and their associated items.

You need to develop a model that supports order read operations. The solution must minimize the number or requests.
A . Create a single database that contains one container. Store orders and order items in separate documents in the container.
B. Create a single database that contains one container. Create a separate document for each order and embed the order items into the order documents.
C. Create a database for orders and a database for order items.
D. Create a single database that contains a container for order and a container for order items.

View Answer

Answer: B

Explanation:

Azure Cosmos DB is a multi-model database that supports various data models, such as documents, key-value, graph, and column-family3. The core content-model of Cosmos DB’s database engine is based on atom-record-sequence (ARS), which allows it to store and query different types of data in a flexible and efficient way3.

To develop a model that supports order read operations and minimizes the number of requests, you should consider the following factors:

✑ The size and shape of your data

✑ The frequency and complexity of your queries

✑ The latency and throughput requirements of your application

✑ The trade-offs between storage efficiency and query performance

Based on these factors, one possible model that you could implement is

B. Create a single database that contains one container. Create a separate document for each order and embed the order items into the order documents. This model has the following advantages:

✑ It stores orders and order items as self-contained documents that can be easily retrieved by order ID1.

✑ It avoids storing redundant data or creating additional containers for order items1.

✑ It allows you to view the order history of a customer with simple queries1.

✑ It leverages the benefits of embedding data, such as reducing the number of requests, improving query performance, and simplifying data consistency2.

This model also has some limitations, such as:

✑ It may not be suitable for some order items that have data that is greater than 2 KB, as it could exceed the maximum document size limit of 2 MB2.

✑ It may not be optimal for scenarios where order items need to be queried independently from orders or aggregated by other criteria2.

✑ It may not support transactions across multiple orders or customers, as transactions are scoped to a single logical partition2.

Depending on your specific use case and requirements, you may need to adjust this model or choose a different one. For example, you could use a hybrid data model that combines embedding and referencing data2, or you could use a graph data model that expresses entities and relationships as vertices and edges.