Have you ever watched a user frantically editing a SharePoint list item? Click save, make another tiny change, click save again, then realize they made a typo and… click save once more. Meanwhile, your perfectly crafted webhook is firing off like a machine gun, triggering expensive operations for every single keystroke (okay, not literally every keystroke—but you get the idea).

This is exactly the problem I ran into while building a system where SharePoint list changes needed to trigger updates to site titles. Users would edit customer information, and I wanted the customer’s SharePoint site title to reflect those changes. But I definitely didn’t want to rename a site five times in thirty seconds just because someone couldn’t decide between “Acme Corp” and “ACME Corporation.”

The solution? Debouncing. Just like the useDebounce hook that React developers know and love, but for SharePoint webhooks.

TL;DR

SharePoint webhooks can trigger expensive operations every time a user clicks save—even for minor edits. To avoid unnecessary updates and improve efficiency, I implemented a debounce pattern using Azure Service Bus scheduled messages and Azure Table Storage. This ensures only the final change is processed after users finish editing, reducing load and preventing out-of-order operations.

The Business Problem

Let me paint you the picture. We have a UI that updates SharePoint list items every time a user makes a change. When they modify a customer’s name, we want to update the title of that customer’s SharePoint site. Simple enough, right?

Wrong.

Users don’t make one clean edit and walk away. They edit the name, realize they want to change the format, edit it again, spot a typo, fix that too, and maybe adjust the capitalization for good measure. Each of these changes triggers our SharePoint webhook, which in turn tries to update the site title.

Not only is this inefficient, but it’s also potentially problematic. Site title updates aren’t instant operations, and we definitely don’t want them queuing up and executing out of order.

What we really want is to wait until the user is done making changes, then process all their edits as one logical operation.

Enter the Debounce Pattern

The concept is borrowed straight from the front-end world. Instead of reacting to every single change immediately, we wait. If another change comes in within our debounce window, we cancel the previous operation and reset the timer.

Here’s the entry point to my implementation:

public class WebhookProcessor() {
    public async Task HandleListChangeAsync(string itemId, string listId) {
        using (DebounceScheduler debouncer = new DebounceScheduler()) {
            var message = new ServiceBusMessage() {
                ContentType = "application/json",
            };

            await debouncer.DebounceAsync(itemId, listId, message, TimeSpan.FromMinutes(5));
        }
    }
}

Simple on the surface, but there’s quite a bit happening under the hood.

The Architecture: Azure Service Bus + Table Storage

I built this debounce system using two Azure services:

Azure Service Bus for scheduled message delivery
Azure Table Storage for tracking sequence numbers

Here’s why this combination works so well:

Azure Service Bus Scheduled Messages

Service Bus has a fantastic feature called scheduled messages. You can schedule a message to be delivered at a specific time in the future, and critically, you can cancel that scheduled message if needed.

This is perfect for debouncing. When a webhook fires, I schedule a message to be processed in 5 minutes. If another webhook fires for the same item before those 5 minutes are up, I cancel the previously scheduled message and schedule a new one.

Table Storage for State Management

The tricky part is remembering which messages you’ve scheduled so you can cancel them later. That’s where Table Storage comes in.

The core logic looks something like this:

internal async Task DebounceAsync(string entityId, string listId, ServiceBusMessage message, TimeSpan delay) {
    // Get any existing scheduled message for this entity
    var oldSeq = await store.GetSequenceNumberAsync(entityId);
   
    if (oldSeq.HasValue) {
        // Cancel the previous scheduled message
        await _sender.CancelScheduledMessageAsync(oldSeq.Value);
        await store.DeleteSequenceNumberAsync(entityId);
    }

    // Schedule a new message
    var scheduledTime = DateTimeOffset.UtcNow.Add(delay);
    long newSeq = await _sender.ScheduleMessageAsync(message, scheduledTime);

    // Store the new sequence number for future cancellation
    await store.SetSequenceNumberAsync(entityId, newSeq, listId);
}

For each item being debounced, I store:

The sequence number of the scheduled Service Bus message
The list ID

When a new change comes in, I can look up the previous sequence number, cancel that message, and schedule a new one.

The Flow in Action

Let’s walk through what happens when a user edits a customer:

User changes customer name → SharePoint webhook fires
My webhook Azure Function receives the notification and queues it
The webhook queue processor calls WebhookProcessor.HandleListChangeAsync()
DebounceScheduler checks Table Storage for any existing scheduled message
If one exists, it gets cancelled via the Service Bus sequence number
A new message is scheduled for 5 minutes from now
The new sequence number is saved to Table Storage

If the user makes another change within 5 minutes, steps 4-7 repeat, effectively resetting the countdown.

Once 5 minutes pass without any changes, the scheduled message is delivered to our debounce queue, and the actual processing begins.

When SharePoint Gets Slow (Really Slow)

Here’s where things got interesting. During testing, I discovered that SharePoint webhooks aren’t always as snappy as you’d expect.

One evening, I made a change to a list item and noticed that both my webhook and a Power Automate flow (each set up for the same list) didn’t fire until 22 minutes later. The timing was unusual, especially since this happened right when the customer’s backup solution started running.

I can’t say for sure what caused the delay, and I don’t want to speculate or draw any conclusions based on this single incident. However, it does highlight an important consideration: webhook delivery isn’t guaranteed to be immediate.

Trade-offs and Considerations

Like any architectural decision, this approach comes with trade-offs:

Pros:

Dramatically reduces unnecessary processing
Handles the “fidgety user” problem elegantly
Resilient to webhook delivery delays
Scales well (Service Bus handles the heavy lifting)
Works across multiple function instances

Cons:

Adds latency (minimum 5-minute delay)
Requires additional Azure services (cost)
More complex than direct processing
Potential for message loss (though Service Bus is pretty reliable)

Alternative Approaches I Considered:

In-memory debouncing: Would work for a single instance, but doesn’t scale across multiple Azure Function instances
Redis-based debouncing: Would work, but adds another dependency and requires more custom logic
Database polling: Could work, but feels clunky and less efficient than Service Bus scheduled messages
Simple delays: Just wait X seconds before processing, but this doesn’t handle multiple rapid changes

The Service Bus + Table Storage approach won because it’s distributed by nature, leverages existing Azure services we were already using, and handles cancellation elegantly.

Wrapping Up

Debouncing SharePoint webhooks with Azure Service Bus and Table Storage has made our operations more efficient and resilient to user behavior and platform quirks.

References

Jeppe Spanggaard

A passionate software developer. I love to build software that makes a difference!

Optimize SharePoint Webhooks: Debouncing User Actions