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.
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.
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