• Fri, Oct 13, 2017

    Azure functions can look at blob storage and react to things.

    But actually not really all that well.

    Excerpt from the Documentation:

    When you’re using a blob trigger on a Consumption plan, there can be up to a 10-minute delay in processing new blobs after a function app has gone idle. After the function app is running, blobs are processed immediately. To avoid this initial delay, consider one of the following options:

    Use an App Service plan with Always On enabled.

    Use another mechanism to trigger the blob processing, such as a queue message that contains the blob name. For an example, see Queue trigger with blob input binding.

    Let’s deconstruct this a bit.

    The important parts are the "Consumption Plan" vs "App Service", and how those relate to the Always On mode.

    See, Azure Functions have two methods of operation (“plans”). The “Consumption” plan executes the function only when triggered. So if nothing is calling it, the function will go to sleep. A Function runs ephemerally and you need not think of its underlying resources whatsoever, aside from paying per invocation.

    The App Service plan, on the other hand, launches a VM that will host your functions, and that VM remains running. You don’t need to directly manage it (nor can you), but you are being charged for all the minutes it’s humming away. Also, unlike the Consumption plan, you need to manage autoscaling yourself.

    Only on the App Service plan you are given the option to enable “Always On”, which will prevent your function apps from going to sleep.

    So in contrast to the probably familiar pattern of AWS Lambda being triggered by a change in S3 bucket, the Azure Blob storage doesn’t immediately trigger your function on change in blob storage, unless the function is already running. Otherwise, you are waiting for the scheduled wake-up window (feel free to correct me on Twitter if I am misunderstanding something). I personally find this behaviour to be super confusing, and inferior to what the rest of the cloud has come to to expect of the “serverless” patterns.

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