This approach, often referred to as “cross-document referencing,” is an integral part of non-relational database models and significantly affects the overall design and performance of your application.
Conceptualizing Cross-Document Referencing
Data stored in Cosmos DB usually exists as independent documents, each with its unique ID. In a traditional relational database, connections between different data pieces are managed via direct links or “relationships.” But in a NoSQL database like Cosmos DB, this is not always the case. NoSQL databases are schema-less, meaning the structure, or schema, of the data isn’t defined when the database is created. You can change the structure of records (referred to as documents) on the fly.
So, how exactly do we establish relationships or references between different documents in Cosmos DB?
Well, one possible way is by embedding linked data directly within the document itself. This approach works well when dealing with one-to-few relationships where the linked data doesn’t need to stand on its own.
However, in scenarios where documents need to link to multiple other documents, embedding isn’t always the best approach. This is where cross-document referencing comes into play. Instead of embedding the actual data, you’d store a reference (usually the document’s unique ID) that points to the linked document. This method is similar to using foreign keys in a relational database.
Practical Usage of Cross-Document Referencing
Let’s say we have an e-commerce application with “Users” and “Orders.” A user can have multiple orders, and each order is linked to a specific user.
Here’s what the ‘User’ document may look like:
{
"id": "1",
"name": "John Doe",
"email": "john.doe@example.com"
}
And here’s an ‘Order’ document:
{
"id": "123",
"userId": "1",
"product": "Laptop",
"price": "1000"
}
In this instance, the ‘Order’ document doesn’t contain the user’s details. Instead, it has a ‘userId’ field that could be used to fetch the user’s details when required.
Benefits and Trade-offs
Using cross-document referencing means your application’s design keeps data normalization principles since the data isn’t duplicated in multiple documents. This approach can significantly reduce the amount of total storage space used. It also ensures data integrity as there’s a single source of truth.
On the other hand, this design relies heavily on the application’s code to manage the relationships between documents. In use-cases where you often need to fetch linked documents, the number of read operations could increase, potentially leading to increased costs and lower performance.
Conclusion
When dealing with Cosmos DB or any NoSQL database, getting your data modeling right is crucial for the performance and scalability of your application. Leveraging cross-document references in your data model can be a powerful tool, but it requires careful thought and planning. Given the above, it’s apparent that developers sitting for the DP-420 Designing and Implementing Native Applications using Microsoft Azure Cosmos DB exam should be well versed in the concept and application of document referencing between documents in Cosmos DB.
Practice Test
Microsoft Azure Cosmos DB facilitates global data distribution.
- True
- False
Answer: True
Explanation: This is a unique feature of Microsoft Azure Cosmos DB, allowing transparent and automatic global data distribution.
Azure Cosmos DB is a relational database.
- True
- False
Answer: False
Explanation: Azure Cosmos DB is a globally distributed, multi-model database service. It supports NoSQL.
Azure Cosmos DB APIs currently drop support for MongoDB.
- True
- False
Answer: False
Explanation: Azure Cosmos DB offers compatibility for different APIs including MongoDB, SQL, Gremlin (graph), and Table Storage.
To develop a design by referencing between documents, you only need to consider the primary key in Azure Cosmos DB.
- True
- False
Answer: False
Explanation: Azure Cosmos DB uses primary key for partitioning the data, but there are other factors too to consider such as the choice of API, data model, partition key, and indexing policy.
The throughput of Azure Cosmos DB can be scaled anytime without affecting the application availability.
- True
- False
Answer: True
Explanation: Azure Cosmos DB is designed to provide seamless global scalability across various dimensions such as storage, throughput, and geographical distribution.
Which of the following factors need to be considered while designing and implementing native applications using Microsoft Azure Cosmos DB? (Multiple Select)
- Choice of API
- Partition Key
- Schema and secondary indexes
- All of the above
Answer: All of the above
Explanation: The design of a native application requires consideration of several factors including choice of API, partition key, and indexing along with schema and secondary indexes.
Azure Cosmos DB allows you to store and process unstructured data.
- True
- False
Answer: True
Explanation: Azure Cosmos DB is a NoSQL database service, so it supports storing and processing unstructured and semi-structured data.
High availability in Azure Cosmos DB is maintained using Replicated Writes.
- True
- False
Answer: True
Explanation: Azure Cosmos DB maintains high availability by automatically replicating all your data across all regions associated.
The Cosmos DB SQL API is built to support SQL (Structured Query Language).
- True
- False
Answer: True
Explanation: Despite being a NoSQL database, Cosmos DB supports a SQL API for querying the data.
Turnkey global distribution is a feature of Azure Cosmos DB.
- True
- False
Answer: True
Explanation: Azure Cosmos DB provides tunkey global distribution, which is one of its unique selling points.
Interview Questions
What is the primary goal of referencing between documents in Cosmos DB design?
The main goal is to enhance and optimize performance by allowing related data to be retrieved in a single, atomic operation, which reduces the number of requests to the server.
How do partition keys help in the implementation of native applications using Microsoft Azure Cosmos DB?
Partition keys are used to distribute data across multiple physical partitions. It enables the database to scale and perform effectively by ensuring that the data is evenly distributed across the partitions.
What is the concept of embedding in Cosmos DB and why is it necessary when designing native applications?
Embedding refers to storing related data within a single document, rather than in a separate reference document. It allows applications to retrieve and manipulate related data in a single atomic operation, increasing efficiency and performance.
How does the use of Indexing in Cosmos DB improve the performance of native applications?
Indexing in Cosmos DB allows for quick and efficient data retrieval. The automatic indexing feature allows all document properties to be indexed without needing any defined schema, which significantly boosts the performance of queries.
What factors should be considered when choosing a partition key when implementing Cosmos DB?
The crucial factors include the size of the data, the need for balanced distribution of data across partitions, and the query requirements of the application. Choosing the correct partition key can optimize the performance of the native application on Cosmos DB.
How does Azure Cosmos DB ensure data consistency across all global replicas?
Cosmos DB provides five consistency levels: Strong, Bounded staleness, Session, Consistent prefix, and Eventual, and it provides the flexibility to choose the appropriate consistency level based on the requirements of the application.
Can you explain the use of Change Feed in designing an application using Cosmos DB?
Change Feed in Cosmos DB enables the applications to listen to data changes. It is a sorted list of documents within a collection ordered by the modification time, making it ideal for creating applications that require real-time processing and analytics.
How does the optimistic concurrency control feature in Cosmos DB help in maintaining data consistency?
In the case of concurrent updates to a document, Optimistic Concurrency Control uses the ‘AccessCondition’ header in the request to prevent accidental overwrites, thus preserving data consistency.
What role does a Request Unit (RU) play in designing and implementing Cosmos DB?
Request Units are the measure of throughput in Cosmos DB. These are rate-based and help in managing and scaling the throughput of databases based on the application’s needs, impacting the performance and cost efficiency of the application.
Why is it important to denormalize data when designing native applications on Azure Cosmos DB?
Denormalizing data can help reduce the need for complex joins, subqueries, or multiple round trips to retrieve related data. This can greatly improve read performance, making it particularly useful in distributed databases like Cosmos DB where such operations can be expensive.
How does TTL (Time to Live) property in Cosmos DB aid in efficient data management?
The TTL property determines the lifespan of a document in Cosmos DB. Once the specified TTL value is reached, the document is automatically deleted, which helps manage storage efficiently and can help reduce costs.
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