Concepts
Denormalization is a crucial technique when designing and implementing native applications using Microsoft Azure Cosmos DB. It helps improve read performance and reduces the complexity of querying data by duplicating certain data across multiple documents or collections. This article will explore how to implement denormalization using a change feed in Azure Cosmos DB.
Understanding Denormalization
In a traditional relational database model, data is often distributed across multiple tables, and join operations are required to retrieve the desired information. However, in a NoSQL database like Azure Cosmos DB, denormalization allows us to store related data together, reducing the need for complex joins and increasing read performance.
Using the Change Feed
Azure Cosmos DB provides a change feed feature that allows us to react to changes in the database in near real-time. By leveraging the change feed, we can implement denormalization by keeping related documents updated automatically whenever changes are made to the source data.
To begin, let’s consider a scenario where we have two collections in our Azure Cosmos DB database: “Orders” and “Customers”. Each order document in the “Orders” collection references the customer it belongs to using a customer ID. Our goal is to denormalize the customer information within the order document.
To achieve this, we can use a change feed processor to monitor the “Customers” collection for any updates or inserts. Whenever a change occurs, we can update the corresponding order documents with the latest customer information.
Here’s an example code snippet that demonstrates how to implement this using the Azure Cosmos DB SDK for .NET:
using Microsoft.Azure.Documents;
using Microsoft.Azure.Documents.ChangeFeedProcessor;
using Newtonsoft.Json.Linq;
using System;
using System.Threading;
using System.Threading.Tasks;
class Program
{
private const string EndpointUri = "Your_Cosmos_DB_Endpoint";
private const string AuthKey = "Your_Auth_Key";
private const string DatabaseName = "Your_Database_Name";
private const string OrdersCollectionName = "Your_Orders_Collection_Name";
private const string CustomersCollectionName = "Your_Customers_Collection_Name";
static async Task Main(string[] args)
{
var processorHost = new ChangeFeedProcessorHost(
nameof(DenormalizationHost),
new Uri(EndpointUri),
AuthKey,
new ChangeFeedProcessorOptions());
await processorHost.RegisterObserverFactoryAsync(
OrdersCollectionName,
CustomersCollectionName,
new DenormalizationObserverFactory());
await processorHost.StartAsync();
Console.WriteLine("Denormalization host started. Press any key to stop...");
Console.ReadKey();
await processorHost.StopAsync();
await processorHost.UnregisterObserversAsync();
}
public class DenormalizationObserverFactory : IChangeFeedObserverFactory
{
public IChangeFeedObserver CreateObserver()
{
return new DenormalizationObserver();
}
}
public class DenormalizationObserver : IChangeFeedObserver
{
public Task OpenAsync(IChangeFeedObserverContext context)
{
Console.WriteLine("Denormalization observer opened.");
return Task.CompletedTask;
}
public Task CloseAsync(IChangeFeedObserverContext context, ChangeFeedObserverCloseReason reason)
{
Console.WriteLine("Denormalization observer closed. Reason: " + reason);
return Task.CompletedTask;
}
public Task ProcessChangesAsync(IChangeFeedObserverContext context, IReadOnlyList docs, CancellationToken cancellationToken)
{
foreach (var doc in docs)
{
var customerId = doc.GetPropertyValue("id");
var customerName = doc.GetPropertyValue("name");
var orderQuery = new SqlQuerySpec("SELECT * FROM c WHERE c.customerId = @customerId",
new SqlParameterCollection { new SqlParameter("@customerId", customerId) });
var orderDocuments = context.CreateDocumentQuery(OrdersCollectionName, orderQuery, new FeedOptions { EnableCrossPartitionQuery = true })
.ToList();
foreach (var orderDocument in orderDocuments)
{
orderDocument.SetPropertyValue("customerName", customerName);
await context.ReplaceDocumentAsync(orderDocument);
}
}
return Task.CompletedTask;
}
}
}
In this code snippet, we initialize a ChangeFeedProcessorHost, passing in the Cosmos DB endpoint, authentication key, and database and collection names. Next, we register an observer factory that creates instances of our custom DenormalizationObserver. The observer is responsible for processing the changes and updating the order documents with the latest customer information.
Within the DenormalizationObserver, the ProcessChangesAsync method extracts the customer ID and name from the changed customer document. It then performs a query to find all order documents with a matching customer ID and updates them by setting the customerName property.
Finally, we replace the updated order documents using the context.ReplaceDocumentAsync method. This ensures that the denormalized data remains up to date.
Conclusion
Implementing denormalization with a change feed in Azure Cosmos DB allows us to achieve better read performance and simplified querying. By automatically updating related documents, we can reduce the need for costly joins and improve response times. Leveraging the Azure Cosmos DB SDK for .NET, we can easily monitor changes and keep our data denormalized in near real-time.
Answer the Questions in Comment Section
True/False: Denormalization is the process of combining multiple entities into a single entity to improve query performance in Azure Cosmos DB.
Answer: False
True/False: By using a change feed in Azure Cosmos DB, you can implement denormalization by automatically capturing and processing the changes made to the data.
Answer: True
Single select: Which feature of Azure Cosmos DB allows you to continuously track the changes made to the data and perform denormalization?
a) Change feed
b) Optimistic concurrency
c) Partition key
d) TTL (time to live)
Answer: a) Change feed
Multiple select: When implementing denormalization using a change feed in Azure Cosmos DB, what advantages do you gain? (Select all that apply)
a) Improved query performance
b) Reduced network latency
c) Simplified data model
d) Automatic indexing of all attributes
Answer: a) Improved query performance, c) Simplified data model
True/False: The change feed in Azure Cosmos DB is an event-driven mechanism that allows you to build reactive and scalable applications.
Answer: True
Single select: Which programming models are supported for consuming the change feed in Azure Cosmos DB?
a) Java only
b) JavaScript/Node.js only
c) .NET/C# only
d) Java, JavaScript/Node.js, and .NET/C#
Answer: d) Java, JavaScript/Node.js, and .NET/C#
True/False: The change feed in Azure Cosmos DB guarantees exactly once delivery of events, ensuring that every change is processed exactly one time.
Answer: False
Single select: What is the maximum retention period for change feed events in Azure Cosmos DB?
a) 7 days
b) 14 days
c) 30 days
d) 90 days
Answer: c) 30 days
Multiple select: When consuming the change feed in Azure Cosmos DB, which of the following actions can you perform? (Select all that apply)
a) Read the changes in chronological order
b) Apply business logic and update the data
c) Filter the changes based on specific criteria
d) Subscribe to real-time notifications for changes
Answer: a) Read the changes in chronological order, b) Apply business logic and update the data, c) Filter the changes based on specific criteria
True/False: Denormalization using a change feed in Azure Cosmos DB allows you to achieve a fine-grained data consistency model.
Answer: False
Great post! Implementing denormalization using change feed in Azure Cosmos DB is a game-changer for performance.
Fantastic explanation of the change feed integration. Thanks!
Can someone tell me which SDKs support change feed processing?
I’m curious about the write throughput impact when using change feed. Any info on that?
Awesome guide, thanks for sharing!
Change feed is such a powerful feature. We implemented it and saw significant performance improvements.
How is change feed different from triggers in Cosmos DB?
Thanks for the detailed post!