A few thousand requests per minute is a big load, and the only way to do it right, is by controlling and limiting the maximum number of threads which could be running at any one time.

As there's not much information posted as to how you've implemented this. I'm going to cover a few possible circumstances.

Time to experiment...

The constants:

• Items to process:
  • 50 per second, or in other words...
  • 3,000 per minute, and one more way to look at it...
  • 180,000 per hour

The variables:

• Data transfer rates:

  • How much data you can transfer per second is going to play a role no matter what we do, and this will vary through out the day depending on the time of day.

    The only thing we can do is fire off more requests from different cpu's to distribute the weight of traffic we're sending back n forth.

• Processing power:

  • I'm assuming you have this in a WebJob as opposed to having this coded inside the MVC site it's self. It's highly inefficient and not fit for the purpose that you're trying to achieve. By using a WebJob we can queue work items to be processed by other WebJobs. The queue in question is the Azure Queue Storage.

    Azure Queue storage is a service for storing large numbers of messages that can be accessed from anywhere in the world via authenticated calls using HTTP or HTTPS. A single queue message can be up to 64 KB in size, and a queue can contain millions of messages, up to the total capacity limit of a storage account. A storage account can contain up to 200 TB of blob, queue, and table data. See Azure Storage Scalability and Performance Targets for details about storage account capacity.

    Common uses of Queue storage include:

    • Creating a backlog of work to process asynchronously
    • Passing messages from an Azure Web role to an Azure Worker role

The issues:

• We're attempting to complete 50 transactions per second, so each transaction should be done in under 1 second if we were utilising 50 threads. Our 45 second time out serves no purpose at this point.
• We're expecting 50 threads to run concurrently, and all complete in under a second, every second, on a single cpu. (I'm exaggerating a point here, just to make a point... but imagine downloading 50 text files every single second. Processing it, then trying to shoot it back over to a colleague in the hopes they'll even be ready to catch it)
• We need to have a retry logic in place, if after 3 attempts the item isn't processed, they need to be placed back in to the queue. Ideally we should be providing more time to the server to respond than just one second with each failure, lets say that we gave it a 2 second break on first failure, then 4 seconds, then 10, this will greatly increase the odds of us persisting / retrieving the data that we needed.
• We're assuming that our MongoDb can handle this number of requests per second. If you haven't already, start looking at ways to scale it out, the issue isn't in the fact that it's a MongoDb, the data layer could have been anything, it's the fact that we're making this number of requests from a single source that is going to be the most likely cause of your issues.

The solution:

1. Set up a WebJob and name it EnqueueJob. This WebJob will have one sole purpose, to queue items of work to be process in the Queue Storage.
2. Create a Queue Storage Container named WorkItemQueue, this queue will act as a trigger to the next step and kick off our scaling out operations.
3. Create another WebJob named DequeueJob. This WebJob will also have one sole purpose, to dequeue the work items from the WorkItemQueue and fire out the requests to your data store.
4. Configure the DequeueJob to spin up once an item has been placed inside the WorkItemQueue, start 5 separate threads on each and while the queue is not empty, dequeue work items for each thread and attempt to execute the dequeued job.
   1. Attempt 1, if fail, wait & retry.
   2. Attempt 2, if fail, wait & retry.
   3. Attempt 3, if fail, enqueue item back to WorkItemQueue
5. Configure your website to autoscale out to x amount of cpu's (note that your website and web jobs share the same resources)

Here's a short 10 minute video that gives an overview on how to utilise queue storages and web jobs.

Edit:

Another reason you may be getting those errors could be because of two other factors as well, again caused by it being in an MVC app...

If you're compiling the application with the DEBUG attribute applied but pushing the RELEASE version instead, you could be running into issues due to the settings in your web.config, without the DEBUG attribute, an ASP.NET web application will run a request for a maximum of 90 seconds, if the request takes longer than this, it will dispose of the request.

To increase the timeout to longer than 90 seconds you will need to change the [httpRuntime][3] property in your web.config...

<!-- Increase timeout to five minutes --><httpRuntime executionTimeout="300" />

The other thing that you need to be aware of is the request timeout settings of your browser > web app, I'd say that if you insist on keeping the code in MVC as opposed to extracting it and putting it into a WebJob, then you can use the following code to fire a request off to your web app and offset the timeout of the request.

string html = string.Empty;string uri = "http://google.com";HttpWebRequest request = (HttpWebRequest)WebRequest.Create(uri);request.Timeout = TimeSpan.FromMinutes(5);using (HttpWebResponse response = (HttpWebResonse)request.GetResponse())using (Stream stream = response.GetResponseStream())using (StreamReader reader = new StreamReader(stream)){    html = reader.ReadToEnd();}

Are you using mongoDB in a VM? It seems to be a network problem. This kind of transient faults should occur, so the best you can do is implement a retry pattern or use a lib such as Polly to do that:

Policy    .Handle<IOException>()    .Retry(3, (exception, retryCount) =>    {        // do something     });

https://github.com/michael-wolfenden/Polly