Process Azure Storage Blob Data with Azure Function App | Exam AZ-204 Solution
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You develop an HTTP triggered Azure Function app to process Azure Storage blob data.
The app is triggered using an output binding on the blob.
The app continues to time out after four minutes.
The app must process the blob data.
You need to ensure the app does not time out and processes the blob data.
Solution: Pass the HTTP trigger payload into an Azure Service Bus queue to be processed by a queue trigger function and return an immediate HTTP success response.
Does the solution meet the goal?
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A. B.A.
Large, long-running functions can cause unexpected timeout issues.
General best practices include: Whenever possible, refactor large functions into smaller function sets that work together and return responses fast.
For example, a webhook or HTTP trigger function might require an acknowledgment response within a certain time limit; it's common for webhooks to require an immediate response.
You can pass the HTTP trigger payload into a queue to be processed by a queue trigger function.
This approach lets you defer the actual work and return an immediate response.
https://docs.microsoft.com/en-us/azure/azure-functions/functions-best-practicesThe proposed solution is to pass the HTTP trigger payload into an Azure Service Bus queue to be processed by a queue trigger function and return an immediate HTTP success response.
This solution can potentially meet the goal of ensuring that the app does not time out and processes the blob data. By using a queue-triggered function, the app can offload the processing of the blob data to a separate function that runs independently of the HTTP trigger. This can help to avoid the four-minute time-out limit associated with HTTP-triggered functions.
Additionally, by returning an immediate HTTP success response, the user is informed that the request has been accepted and is being processed. This can provide a better user experience, as the user is not left waiting for a response while the function processes the blob data.
However, it is important to note that there may be other factors that could impact the overall performance and efficiency of the solution. For example, if the queue becomes backlogged with requests, this could potentially slow down the processing of the blob data. Additionally, there may be other optimizations or modifications that could be made to improve the overall performance and scalability of the application.
