Optimizing Request Validation to External Systems Using Azure Services, Microservices, and Parallel Processing

In modern software architectures, handling external request validation efficiently can be challenging. By leveraging Azure services such as Azure Service Bus, Azure Cosmos DB, and parallel processing in microservices, you can build a scalable, high-performing architecture to process validations asynchronously.

In this post, we'll dive into how to implement this architecture using Azure services with .NET.

1. The Problem: External System Request Bottlenecks

When dealing with external systems (e.g., third-party APIs), validation requests can create bottlenecks due to:

• Unpredictable response times.
• Large volumes of requests.
• Synchronous processing, which blocks resources and slows down the system.

The Solution: Azure-Backed Scalable Architecture

To optimize this process, we can use a combination of:

• Microservices: Divide responsibilities into smaller services for better scalability.
• Azure Service Bus: Enable asynchronous messaging between services and offload processing.
• Azure Cosmos DB: Use a NoSQL database to store validation states and results.
• Parallel Processing: Handle multiple validation requests simultaneously to increase throughput.

2. Azure-Based Architecture Overview

The following components will help us build a scalable system:

• Azure Service Bus: Acts as a message broker for asynchronous request processing.
• Azure Functions or Azure Kubernetes Service (AKS): Hosts microservices responsible for validation logic.
• Azure Cosmos DB: Stores temporary validation states and results in a fast, scalable manner.

Architecture Flow

1. Client Request: A client sends a request to the main API.
2. Azure Service Bus Queue: The request is placed on the Azure Service Bus.
3. Microservices (AKS or Azure Functions): Microservices consume the messages, perform validations, and send results to Azure Cosmos DB.
4. Azure Cosmos DB: Stores intermediate results or final validation status.
5. Response: After the validation is completed, results are aggregated and sent back to the client.

3. Step-by-Step Implementation

Step 1: Microservices on Azure

In this architecture, you can deploy your microservices using either Azure Kubernetes Service (AKS) or Azure Functions.

Option 1: Azure Kubernetes Service (AKS)

AKS provides container orchestration for microservices, allowing them to scale automatically. Start by containerizing your microservices with Docker and deploying them to AKS.

Option 2: Azure Functions

Alternatively, you can use Azure Functions for a serverless approach. Each function can handle part of the validation process.

Example Azure Function in C#:

[FunctionName("ValidateRequestFunction")]
public static async Task Run(
    [ServiceBusTrigger("validationqueue", Connection = "AzureServiceBusConnection")] string requestMessage,
    ILogger log)
{
    log.LogInformation($"Processing request: {requestMessage}");

    // Perform validation logic
    var validationResult = await ValidateRequestAsync(requestMessage);

    // Store result in CosmosDB
    await StoreValidationResult(validationResult);
}

In this example:

• ServiceBusTrigger: Listens to messages from Azure Service Bus and triggers the function.
• ValidateRequestAsync: Simulates the external system call to validate the request.
• StoreValidationResult: Stores the validation result in Azure Cosmos DB.

Step 2: Asynchronous Messaging with Azure Service Bus

To enable asynchronous communication between services, use Azure Service Bus to handle message queuing. Instead of waiting for validation to complete synchronously, the request is placed in a queue and processed asynchronously by microservices.

Adding Messages to the Queue

When a client sends a request, it's added to the Azure Service Bus queue:

string connectionString = "<YourAzureServiceBusConnectionString>";
string queueName = "validationqueue";

var client = new ServiceBusClient(connectionString);
var sender = client.CreateSender(queueName);

ServiceBusMessage message = new ServiceBusMessage(requestData);
await sender.SendMessageAsync(message);

Consuming Messages from the Queue

Microservices or Azure Functions will consume messages from the Service Bus:

public async Task ProcessMessagesAsync()
{
    var client = new ServiceBusClient(connectionString);
    var processor = client.CreateProcessor(queueName, new ServiceBusProcessorOptions());

    processor.ProcessMessageAsync += async (args) =>
    {
        string request = args.Message.Body.ToString();
        await ValidateAndStoreResultAsync(request);
    };

    await processor.StartProcessingAsync();
}

Step 3: Parallel Processing with Task Parallel Library (TPL)

To speed up external validations, implement parallel processing to handle multiple validation requests concurrently.

In Azure Functions or AKS microservices, you can leverage the Task Parallel Library (TPL) to perform parallel validation:

public async Task ValidateRequestsInParallel(List<string> externalRequests)
{
    var tasks = externalRequests.Select(request => ValidateRequestAsync(request));
    await Task.WhenAll(tasks);
}

private async Task ValidateRequestAsync(string request)
{
    // Simulate external system validation
    await Task.Delay(1000);  // Simulate external latency
}

This allows multiple validations to be processed in parallel, reducing overall processing time.

Step 4: Storing Validation Results in Azure Cosmos DB

Use Azure Cosmos DB, a globally distributed NoSQL database, to store the validation states and results. Cosmos DB offers fast reads and writes, allowing you to store intermediate or final validation states.

var client = new CosmosClient("<YourCosmosDBConnectionString>");
var container = client.GetContainer("ValidationDatabase", "ValidationContainer");

var validationResult = new { requestId = "12345", status = "validated" };
await container.CreateItemAsync(validationResult, new PartitionKey(validationResult.requestId));

Cosmos DB is ideal for storing validation results as it can scale automatically to handle large volumes of requests, ensuring low-latency access.

Step 5: Aggregating Results and Returning to Client

Once validation is completed, the results stored in Cosmos DB can be retrieved and returned to the client. Use Cosmos DB's fast query capabilities to fetch the result based on a unique identifier:

public async Task<IActionResult> GetValidationResult(string requestId)
{
    var result = await _cosmosClient.GetItemAsync<ValidationResult>("ValidationContainer", requestId);
    if (result == null)
    {
        return NotFound();
    }
    return Ok(result);
}

This final step ensures that clients receive the validation result once the asynchronous processing is completed.

4. Benefits of Azure-Based Architecture

Scalability:

• Azure Service Bus enables horizontal scaling of message processing.
• Azure Functions or AKS can scale independently based on demand, allowing the system to handle increased loads.
• Azure Cosmos DB is designed for global distribution, making it ideal for systems requiring low-latency access.

Resilience:

• Asynchronous processing ensures that your system can recover from external system failures without affecting the entire workflow.
• Azure Service Bus offers built-in message retries and dead-lettering, improving fault tolerance.

Performance:

• Parallel processing with Azure services increases throughput.
• Cosmos DB offers low-latency data access, ensuring fast read and write operations.

5. Conclusion

By using Azure Services such as Azure Service Bus, Azure Functions (or AKS), and Azure Cosmos DB, you can build a highly scalable, resilient, and high-performance architecture for handling external request validations. This cloud-native approach ensures that your system can handle large volumes of requests while maintaining low latency and high availability.

Whether you're processing real-time requests or handling large volumes of asynchronous validations, these technologies provide the foundation for a robust, cloud-native architecture in .NET.