Develop a model that denormalizes data across documents

Microsoft Azure Cosmos DB is a powerful, globally distributed, multi-model database service provided by Microsoft Azure. It offers flexibility, scalability, and high availability, making it an ideal choice for application developers. In this article, we will explore how to design and implement native applications using Microsoft Azure Cosmos DB, with a focus on denormalizing data across exam documents.

Denormalization and its Benefits

Denormalization is the process of combining and duplicating data from multiple sources into a single data structure to improve read performance. It eliminates the need for complex joins and enables faster and more efficient querying. Denormalizing data is particularly useful in scenarios where data is frequently read but infrequently updated. Let’s dive into the steps involved in denormalizing exam-related data using Microsoft Azure Cosmos DB.

1. Model Design and Schema Definition

Before denormalizing data, it’s important to identify the relationships between different entities and design an appropriate data model. In our scenario, we’re dealing with exam documents, so we may have entities like exams, questions, answers, and candidates.

2. Data Partitioning

Azure Cosmos DB provides partitioning capabilities to distribute data across multiple logical partitions for scalability and performance. When designing your data model, consider how to partition your data effectively based on access patterns and anticipated read and write loads.

3. Denormalization

To denormalize data, we’ll duplicate relevant information across different documents. For example, if we have an exam document and a question document, we can denormalize the relevant attributes from the question document into the exam document. This denormalization eliminates the need for a costly join operation while querying data related to exams.

4. Document Structure and Design

Azure Cosmos DB stores data in JSON-like documents. Each document can have its own unique structure, enabling flexible schema design. Define the document structure based on your data model and the denormalized data you want to store. Use properties and nested objects to organize the data in a logical manner.

Here’s an example of the document structure for an exam document:


{
"id": "examId",
"title": "Exam Title",
"duration": 120,
"questions": [
{
"id": "questionId1",
"text": "Question Text 1",
"answers": [
{
"id": "answerId1",
"text": "Answer Text 1",
"isCorrect": true
},
{
"id": "answerId2",
"text": "Answer Text 2",
"isCorrect": false
}
]
},
{
"id": "questionId2",
"text": "Question Text 2",
"answers": [
{
"id": "answerId3",
"text": "Answer Text 3",
"isCorrect": true
},
{
"id": "answerId4",
"text": "Answer Text 4",
"isCorrect": false
}
]
}
]
}


In this example, the exam document includes an array of questions, each with nested answers. This denormalized structure allows us to fetch all the relevant data in one query, avoiding additional database requests or complex joins.

5. Querying Denormalized Data

With the data denormalized, querying becomes simpler and more efficient. We can directly access and traverse the relevant properties and objects within a single document. You can use Azure Cosmos DB’s SQL-like query language to query documents based on your requirements.

Here’s an example of a query to retrieve all exams along with their associated questions:


SELECT *
FROM exams e
JOIN q IN e.questions


This query will return all exams along with their respective questions. By denormalizing the data, we eliminate the need for complex joins and improve the performance of our application.

6. Updating Denormalized Data

As we denormalize data, it’s important to handle updates and ensure data consistency. When updating the denormalized data, you need to make sure that all relevant documents are updated consistently. Azure Cosmos DB provides atomic operations and transactional guarantees to maintain data consistency during writes.

For example, if an answer is updated in one question, you need to update the corresponding denormalized answer in all other exam documents that reference that question.

7. Considerations and Best Practices

• Denormalization improves read performance but can increase write and update complexities. Evaluate the trade-offs and choose denormalization for scenarios where read performance is critical.
• Use indexing effectively to optimize query performance. Azure Cosmos DB allows you to define and fine-tune indexes based on your specific queries.
• Monitor and optimize the performance of your denormalized data model using Azure Cosmos DB’s performance monitoring and optimization tools.

In this article, we explored how to design and implement a model that denormalizes data across exam documents using Microsoft Azure Cosmos DB. We discussed the steps involved, such as data partitioning, document structure design, querying, and handling updates. By denormalizing data and leveraging the power of Azure Cosmos DB, you can improve the performance and scalability of your native applications.