And for a while, every single request did the same dumb thing: download the blob from storage, push the bytes to the browser. The file hadn’t changed since five seconds ago. The user’s browser literally had an identical copy already. Didn’t matter - full download from storage, full response to the client, every time. I was paying latency and moving bytes just to deliver files nobody actually needed re-delivered.

The fix was already sitting in every storage response I’d been ignoring: the ETag.

Thirty Seconds on ETags

Every blob response (and every properties call) includes an ETag header, something like "0x8DC5F3A2B1E4D70". Storage changes it whenever the blob’s content or metadata changes. It costs nothing, it’s always there.

On the HTTP side, the handshake is old and boring and great: the server sends ETag with a response; the browser saves it; next time the browser asks for the same URL it includes If-None-Match: <that etag>; if the server still has the same version, it answers 304 Not Modified with no body, and the browser uses its cached copy.

Boring. Reliable. Built into every browser since forever.

The Naive Version

My first pass was the obvious one:

var response = await blob.DownloadStreamingAsync(cancellationToken: ct);
return new FileStreamResult(response.Value.Content, contentType);

Works fine. But look at what the function actually is in this setup: a photocopier standing between storage and the browser, dutifully copying files that both sides already agree on. The browser has the file. Storage knows the file hasn’t changed. And my function in the middle is the only one who never asked.

The Trick: Be a Pipe, Not a Cache

The Azure SDK supports conditional requests natively. So instead of checking anything myself, I just hand the browser’s ETag straight to storage:

public async Task<AssetResponse?> GetAsync(string path, string? ifNoneMatch, CancellationToken ct)
{
    var blob = _container.GetBlobClient(path);
    var options = new BlobDownloadOptions();

    // Pass the client's ETag to the storage service as a conditional request.
    // Azure Blob Storage will short-circuit at the network level and return a 304,
    // meaning we never transfer the file body when the client is up to date.
    if (!string.IsNullOrEmpty(ifNoneMatch))
        options.Conditions = new BlobRequestConditions { IfNoneMatch = new ETag(ifNoneMatch) };

    try
    {
        var response = await blob.DownloadStreamingAsync(options, ct);

        if (response.GetRawResponse().Status == 304)
        {
            // Client's copy is current - signal it without a body.
            return new AssetResponse { ETag = ifNoneMatch!, IsNotModified = true };
        }

        var details = response.Value.Details;
        return new AssetResponse
        {
            ETag = details.ETag.ToString(),
            Content = response.Value.Content,
            ContentType = details.ContentType,
        };
    }
    catch (RequestFailedException ex) when (ex.Status == 404)
    {
        return null;
    }
}

What’s happening here?

1. The browser’s If-None-Match value goes into BlobRequestConditions.IfNoneMatch. That turns the download into a conditional request, the same ETag handshake, just one hop deeper.
2. The comparison happens inside Azure Storage, not in my code. On a match, storage answers 304 and the response has no body. The file bytes never even reach my function.
3. On a miss (file changed, or first visit), it’s a normal download, and DownloadStreamingAsync gives me a live stream I pass straight through without buffering the whole file in memory.
4. Missing blob → the RequestFailedException filter turns a 404 into a null, which the endpoint maps to a proper NotFound.

There is no dictionary of ETags, no memory cache, no invalidation logic. I didn’t build a cache, I connected two caches that already existed: the browser’s and storage’s own knowledge of its blobs.

Finishing the Loop Toward the Browser

The function endpoint relays the result:

req.HttpContext.Response.Headers[HeaderNames.ETag] = asset.ETag;

if (asset.IsNotModified)
    return new StatusCodeResult(StatusCodes.Status304NotModified);

req.HttpContext.Response.Headers[HeaderNames.CacheControl] = asset.IsImmutable
    ? "public, max-age=31536000, immutable"
    : "no-cache";

return new FileStreamResult(asset.Content!, asset.ContentType!);

Two details worth pausing on:

• no-cache doesn’t mean “don’t cache”. It means “cache it, but revalidate before using it”. That revalidation is exactly the If-None-Match round trip, which the pass-through just made nearly free - a header-only 304 instead of a file download. This is what index.html gets.
• Content-hashed files skip the conversation entirely. My build tool outputs filenames like app.ByJ3R0Az.js - the hash is the version. Those get max-age=31536000, immutable, so the browser never revalidates them at all. A new deploy produces a new filename, which is simply a different URL. The ETag dance is only for files whose names stay stable while their content changes.

Why I Like This Better Than a Memory Cache

My first instinct was an IMemoryCache of blob contents in the function. I’m glad I resisted:

• Nothing to size. No “how many MB of blobs do I keep in memory” question.
• Nothing to invalidate. The ETag comparison is against live storage, so a deploy is visible on the very next request. Stale-cache bugs can’t exist because there’s no cache to go stale.
• Scale-out safe. Ten function instances behave identically because none of them hold state. A per-instance memory cache would give ten different answers for 60 seconds after every deploy.

The thing that owns the data does the validation. Everyone else just forwards headers. 📮

Gotchas

• Round-trip the ETag string untouched. The quotes are part of the value. Trim them, “clean them up”, or re-wrap them and the comparison silently never matches again - everything still works, you just download every file every time and never notice.
• No ETag out, no If-None-Match back. Browsers only revalidate if your response included the ETag header in the first place. Forget it on one branch (error paths are a classic) and that file is a full download forever.
• no-cache still costs one round trip per file per load. That’s the deal: a header-only 304 instead of a body. For files that never change, don’t negotiate - content-hash the filename and go immutable.

Wrapping Up

The cheapest download is the one that never happens. Blob Storage already fingerprints every blob and already speaks conditional requests - most backends just never pass the browser’s question along. Forward If-None-Match, relay the 304, and let the two parties who actually know the answer talk to each other.

And that requirement quietly kills all the easy hosting options. Blob Storage’s static website feature? Public. A plain CDN? Public. The moment “who is asking” matters for every single file - not just the pages, the images and search index too - the files have to live somewhere private, and something with auth has to sit in front and hand them out.

My something is one Azure Function. One endpoint. It serves the entire site.

The Blob Container Is the Filesystem

There’s no clever mapping layer. I upload the build output into the container exactly as the generator produced it:

index.html
guides/onboarding/index.html
guides/expenses/index.html
assets/main.css
assets/search.js
images/office-map.png

The container layout is the URL space. Deploying a new version of the site is one CLI command:

az storage blob upload-batch --source ./public --destination site-content --overwrite

The Catch-All Function

Here’s the whole thing, trimmed to its skeleton:

[Function("Site")]
public async Task<IActionResult> Run(
    [HttpTrigger(AuthorizationLevel.Anonymous, "get", Route = "{*path}")] HttpRequest req,
    string? path,
    CancellationToken ct)
{
    // Every file goes through this gate - pages, scripts, images, all of it.
    if (!await _auth.IsSignedInAsync(req))
        return new UnauthorizedResult();

    var blobPath = MapToBlobPath(path);
    var blob = _container.GetBlobClient(blobPath);

    try
    {
        var response = await blob.DownloadStreamingAsync(cancellationToken: ct);
        var contentType = response.Value.Details.ContentType ?? ContentTypeFor(blobPath);
        return new FileStreamResult(response.Value.Content, contentType);
    }
    catch (RequestFailedException ex) when (ex.Status == 404)
    {
        return new NotFoundResult();
    }
}

private static string MapToBlobPath(string? path)
{
    if (string.IsNullOrEmpty(path))
        return "index.html";                    // "/" → the front page

    if (path.EndsWith('/') || !Path.HasExtension(path))
        return $"{path.TrimEnd('/')}/index.html"; // "/guides/onboarding/" → its index

    return path;                                 // "/assets/main.css" → as-is
}

What’s happening here?

1. Route = "{*path}" is the whole trick. The * makes it a catch-all: one route binding matches /, /guides/onboarding/, /assets/main.css, anything. One function, every file on the site.
2. The auth guard runs before anything touches storage. That’s the entire reason this setup exists - a static host can protect a site, this protects every byte. (How IsSignedInAsync works - cookies, Entra ID, whatever fits - is its own topic; the point is it’s one if at the top.)
3. MapToBlobPath does the job a web server normally does silently: default documents. / becomes index.html, extension-less routes like /guides/onboarding become guides/onboarding/index.html. Forget this and your front page is a 404.
4. DownloadStreamingAsync returns a live stream, and FileStreamResult pipes it straight to the response. The function never buffers a whole file in memory - a 4 MB image flows through, it doesn’t land here.
5. A missing blob throws RequestFailedException with status 404; the exception filter turns that into a clean NotFoundResult instead of a pre-flight existence check (which would just be a second storage call).

Content Types: The Unglamorous Part That Breaks Everything

If you serve HTML with the wrong Content-Type, the browser doesn’t render your page, it downloads it. Ask me how I know.

Best option: set correct content types on the blobs at upload time (upload-batch infers most of them). But blobs uploaded by hand or by older scripts often end up as application/octet-stream, so I keep a fallback map:

private static string ContentTypeFor(string path) => Path.GetExtension(path).ToLowerInvariant() switch
{
    ".html" => "text/html",
    ".css"  => "text/css",
    ".js"   => "text/javascript",
    ".json" => "application/json",
    ".svg"  => "image/svg+xml",
    ".png"  => "image/png",
    ".woff2" => "font/woff2",
    _ => "application/octet-stream",
};

What You Get for Free

• Auth on every file. Not just page-level protection - the org chart PNG and the search index JSON are exactly as protected as the pages.
• One deploy target. Build the site, upload-batch the folder, done. No web server to configure, nothing to restart.
• Generator-agnostic. The function doesn’t know or care if the folder came from Hugo, Astro, Docusaurus, or hand-written HTML.
• Cheap environments. Staging is just a second container and one config value. Rollback is re-uploading yesterday’s build folder.

One More Trick: Stop Re-Downloading Unchanged Files

There’s an elephant in the version above: every request downloads the full file from storage and pushes it to the browser - even when the file hasn’t changed in weeks and the browser has a perfect copy from five minutes ago.

The fix is already sitting in Blob Storage: every blob has an ETag, a version fingerprint that changes on every write. Browsers already know the game - once they’ve seen an ETag header, they send it back as If-None-Match on the next request. All the function has to do is forward that header into the SDK call:

var options = new BlobDownloadOptions();

var ifNoneMatch = req.Headers.IfNoneMatch.ToString();
if (!string.IsNullOrEmpty(ifNoneMatch))
    options.Conditions = new BlobRequestConditions { IfNoneMatch = new ETag(ifNoneMatch) };

var response = await blob.DownloadStreamingAsync(options, ct);

if (response.GetRawResponse().Status == 304)
{
    // Browser's copy is current - Azure never sent us the body at all.
    req.HttpContext.Response.Headers[HeaderNames.ETag] = ifNoneMatch;
    return new StatusCodeResult(StatusCodes.Status304NotModified);
}

req.HttpContext.Response.Headers[HeaderNames.ETag] = response.Value.Details.ETag.ToString();
var contentType = response.Value.Details.ContentType ?? ContentTypeFor(blobPath);
return new FileStreamResult(response.Value.Content, contentType);

The beautiful part: the ETag comparison happens inside Azure Storage, not in your code. On a match, storage answers 304 with no body - the file bytes never reach the function, and the function relays a bare 304 to the browser. No server-side cache, nothing to invalidate, and it works identically across scaled-out instances because nobody holds any state. The browser is the cache, Azure is the validator, and the function stays what it was: a pipe.

Two rules to make it stick: always set the ETag response header (no ETag out means the browser never asks conditionally again), and pass the value through untouched - the quotes are part of it, and “cleaning them up” silently breaks the match forever.

Gotchas

• You are the web server now. Default documents, trailing slashes, 404 pages - all the invisible things a real web server does are your job. MapToBlobPath above is the minimum, not the maximum.
• SPA? Then unknown routes need a fallback. For a client-side-routed app, a route with no matching blob should serve index.html (200, not 404) and let the router sort it out. For a docs site like mine, a real 404 is correct.
• Cold starts sit in front of your CSS. The function is in the path of every byte, so a consumption-plan cold start delays the whole page, not just an API call. For an internal tool I can live with it; know your tolerance.

Wrapping Up

One catch-all route, a private container, and a twenty-line function: a whole authenticated website with no web server to run. Add the If-None-Match pass-through and unchanged files stop moving entirely. The container is the filesystem, the function is the doorman. 🚪

For a long time, slow CSOM was just annoying. Users waited, someone made coffee, life went on. Then I sat down with a client to calculate the cost of Service Prioritization in SharePoint, where every CSOM call has an actual price tag: USD $1.00 per 1,000 calls. Suddenly all those wasted calls weren’t just slow. They were an invoice.

That changed how I write CSOM. I count round trips now, the way you’d count database queries in a hot loop. Over the past year I’ve written several posts about the specific techniques that came out of that habit, and this post ties them together into one playbook.

Every ExecuteQuery Is a Road Trip

Here’s the mental model that makes all four techniques click: every ExecuteQueryAsync() is a road trip to the SharePoint server. Doesn’t matter if you’re delivering one package or a hundred, the drive takes the same time. Network latency, authentication, server processing - the overhead is per trip, not per operation.

So the whole playbook boils down to four questions:

1. Can I deliver more packages per trip? (batching)
2. Can one trip cover several destinations? (CAML joins)
3. Is this trip even necessary? (change detection)
4. Can I avoid a second trip when something goes wrong? (ExceptionHandlingScope, and the taxonomy trick)

Let’s take them one at a time.

Rule 1: Batch or Suffer

The most common sin. A loop that loads items one by one:

// ❌ 50 items = 50 round trips = 3-5 seconds
foreach (var id in itemIds)
{
    var item = list.GetItemById(id);
    context.Load(item);
    await context.ExecuteQueryAsync();
    ProcessItem(item);
}

CSOM happily queues up operations until you call ExecuteQueryAsync(). Around 100 operations per batch is the reliable sweet spot:

// ✅ 50 items = 1 round trip = ~200-500ms
var items = chunk.Select(id => {
    var item = list.GetItemById(id);
    list.Context.Load(item);
    return item;
}).ToList();

await context.ExecuteQueryAsync();

In my testing, that’s a 10x+ improvement for the price of restructuring a loop. It works for reads, writes, deletes, all of it.

Full post with the reusable ProcessInChunks helper: CSOM Performance Optimization: Why You Should Batch Your SharePoint Operations.

Rule 2: Join Lists, Don’t Query Them One by One

Related data across multiple lists is where round trips multiply quietly. Customers in one list, orders in another, order items in a third, so you write three queries and merge the results in C#. Every multi-item query costs 2 resource units toward your throttling budget, so three lists cost 6 units plus three round trips plus the merging code nobody wants to maintain.

CAML supports joins, even though the SharePoint UI never hints at it. One query, one trip, 2 resource units, and the server does the merging. I use CAMLEX instead of raw CAML XML because lambda expressions are readable and the XML it generates is not:

var query = Camlex.Query()
    .Where(x => (string)x["CustomerName"] == "Contoso") // filter on a field 2 lists away!
    .LeftJoin(x => x["OrderTaskLookUp"].ForeignList(ListGuidOrderTasks))
    .LeftJoin(x => x["OrderDetailLookUp"].PrimaryList(ListGuidOrderTasks).ForeignList(ListGuidOrders))
    .LeftJoin(x => x["CustomerLookUp"].PrimaryList(ListGuidOrders).ForeignList(ListGuidCustomers))
    .ProjectedField(x => x["CustomerName"].List(ListGuidCustomers).ShowField("CustomerName"));

var camlQuery = new CamlQuery { ViewXml = query.ToString() };
var items = ordersList.GetItems(camlQuery);

That’s 66% fewer resource units than the three-query version, and you can even filter on a value several lists away. The lists must be connected via lookup columns, and ProjectedFields only supports simple field types (text, number, date - not user, choice, or managed metadata fields).

Full post with the raw CAML comparison and the field type list: Efficient Multi-List Queries in CSOM: Using CAML Joins with CAMLEX.

Rule 3: Did Anything Actually Change?

Users are save-happy. They open a form, change nothing, and click save anyway. Automated syncs are worse. When I dug into a typical day of API logs on one project, roughly 70% of our SharePoint update calls weren’t changing anything. SharePoint accepts identical data with a smile and bills you for the privilege.

The fix is change detection before the update:

var differences = GetDifferences(listItem, newValues, treatEmptyStringAsNull: true);
if (differences.Any())
{
    foreach (var (fieldName, newValue) in differences)
    {
        listItem[fieldName] = newValue;
    }
    listItem.Update();
    clientContext.ExecuteQuery();
}
// Nothing changed? Do absolutely nothing.

The hard part is the comparison itself. SharePoint field types fight you: user fields where only LookupId matters, multi-choice fields that come back in random order, taxonomy values, dates with kind mismatches. Naive oldValue == newValue doesn’t survive contact with any of them, and JSON serialization comparison turned out 3-5x slower than a proper normalizing comparer when I benchmarked both.

Full post with the complete comparison engine: SharePoint CSOM: Prevent Unnecessary Updates and API Calls.

Rule 4: Let the Server Do the Catching

This one came straight out of that Service Prioritization cost analysis. The client’s solution made about 90,000 CSOM calls per month, and 88% of them were EnsureUser calls for users who were already on the site. The classic defensive pattern - always ensure the user before setting a user field - was two round trips per assignment, and the first one was almost always pointless.

ExceptionHandlingScope lets you ship try-catch logic to the server and resolve it in one round trip:

var scope = new ExceptionHandlingScope(clientContext);
using (scope.StartScope())
{
    using (scope.StartTry())
    {
        // Optimistic: works for users already known to the site
        listItem["AssignedTo"] = FieldUserValue.FromUser("user@company.com");
        listItem.Update();
    }
    using (scope.StartCatch())
    {
        // Fallback: only runs server-side if the try failed
        var user = clientContext.Web.EnsureUser("user@company.com");
        listItem["AssignedTo"] = FieldUserValue.FromUser("user@company.com");
    }
}
clientContext.ExecuteQuery(); // one trip

Result for that client: a 75-85% reduction in monthly calls. One honest caveat, and it’s important: the scope reduces round trips, not server requests. Each operation inside still counts toward throttling. The call reduction came from the optimistic-first logic, and ExceptionHandlingScope is what made that logic affordable.

Full post with the cost breakdown: SharePoint’s Server-Side Try-Catch: ExceptionHandlingScope.

Rule 5: Keep Taxonomy Out of Your ViewFields

Taxonomy fields are the slowest thing you can put in a CAML query. On a list where items carried 5 to 50 terms across multiple managed metadata fields, loading a few hundred items took 90-120 seconds. Users literally walked away from their computers.

The fix has two parts. First, query the list without the taxonomy fields in ViewFields, which is where most of the cost hides. Then load the taxonomy data separately through FieldValuesForEdit, where SharePoint stores it as a raw string you can parse directly:

// Raw format in FieldValuesForEdit: "Label1|GUID1;Label2|GUID2"
foreach (ListItem[] chunk in items.Cast<ListItem>().Chunk(100))
{
    foreach (var item in chunk)
    {
        clientContext.Load(item, i => i.FieldValuesForEdit);
    }
    await clientContext.ExecuteQueryAsync();
    // parse Label|GUID pairs per item
}

That took the same load from 90-120 seconds down to 25-35 seconds, a 70-75% improvement. Fair warning: this parses an internal, undocumented format. It’s been reliable for me, but test it in your environment and keep the traditional approach as a fallback.

Full post with the parser: CSOM Performance: Fast Taxonomy Loading for SharePoint List Items with Many Terms.

Which One First?

If your workload is write-heavy (forms, syncs, integrations), start with change detection. It’s the only technique that eliminates entire operations instead of making them cheaper, and it’s invisible to users.

If your workload is read-heavy, start with batching. It’s the smallest code change for the biggest win, and the chunking helper is reusable everywhere. And if those reads span related lists, add joins next: they cut resource units, not just latency, which stretches your throttling budget further.

ExceptionHandlingScope is for when a specific fallback pattern (like EnsureUser) dominates your call logs. Check your logs first - I only found the 88% figure because I went looking. And the taxonomy trick is a targeted weapon: only reach for it when managed metadata is measurably your bottleneck, because it carries the undocumented-format risk.

The good news is they stack. Batched updates that skip unchanged items, with server-side fallbacks, is exactly how my current projects run.

Gotchas

• 100 operations per batch is the sweet spot. CSOM reliably handles around that many. Bigger batches mean bigger payloads and less predictable behavior across environments. Ask me how I know.
• ExceptionHandlingScope doesn’t reduce throttling pressure by itself. Round trips go down, but every operation inside the scope still counts as a request. The savings come from the optimistic-first logic it enables.
• Your numbers will differ from mine. The 70%, 75-85%, and 70-75% figures are from real projects, but latency, item complexity, and tenant load all move them. Measure before and after in your own environment.
• Test under throttling before you trust any of it. Dev Proxy can simulate 429 responses locally so you find out how your batches behave under pressure before production does.
• The taxonomy trick gives you label and GUID, nothing more. If you need full term paths or custom properties, you’re back to the taxonomy API for those items.
• Joins need lookup columns and GUIDs. CAML joins only work across lists connected by lookup columns, and referencing lists by GUID instead of title saves you from “list does not exist” surprises.

If you’re also downloading files in the same solution, the round-trip mindset applies there too - I’ve covered that in downloading multiple files from SharePoint.

Wrapping Up

Count your round trips before SharePoint counts them for you. That’s the whole playbook in one sentence: fewer trips (batching), combined trips (CAML joins), no pointless trips (change detection), no second trips (ExceptionHandlingScope), and lighter trips (taxonomy loading).

Each technique stands on its own, so grab the one that matches your bottleneck:

• Batch your SharePoint operations
• Join multiple lists in one CAML query
• Prevent unnecessary updates
• Server-side try-catch with ExceptionHandlingScope
• Fast taxonomy loading

I was working on a SharePoint solution where we had a list with items containing multiple taxonomy fields. Each item could have anywhere from 5 to 50 taxonomy terms across different fields. The client needed to load hundreds of these items regularly.

The traditional CSOM approach was… well, let’s just say coffee breaks became very popular during load times.

The loading process was taking 90-120 seconds for a few hundred items. Users were literally walking away from their computers while waiting for data to load.

That’s when I realized we needed to completely rethink how we approach taxonomy loading in CSOM.

The Traditional (Slow) Way

Here’s what most developers do, and what I was doing initially:

// The traditional approach - including taxonomy fields in ViewFields
CamlQuery query = new CamlQuery();
query.Query = @"
    <View>
        <ViewFields>
            <FieldRef Name='ID'/>
            <FieldRef Name='Title'/>
            <FieldRef Name='CategoryField'/>     <!-- Taxonomy field -->
            <FieldRef Name='TagField'/>          <!-- Taxonomy field -->
            <FieldRef Name='OtherTaxField'/>     <!-- Taxonomy field -->
        </ViewFields>
    </View>";

ListItemCollection items = list.GetItems(query);
clientContext.Load(items);
await clientContext.ExecuteQueryAsync();

// Then access taxonomy fields normally
foreach (ListItem item in items)
{
    var categoryField = item["CategoryField"] as TaxonomyFieldValueCollection;
    var tagField = item["TagField"] as TaxonomyFieldValueCollection;
    // Process taxonomy values...
}

Problems with this approach:

• Including taxonomy fields in ViewFields is extremely slow
• SharePoint has to load and process all taxonomy data upfront
• Multiple expensive taxonomy service calls during the initial load
• No control over how taxonomy data is loaded

Result: Coffee break time. Lots of it.

The Optimization Journey

After digging into SharePoint’s internals and some experimentation, I discovered two key things:

1. Including taxonomy fields in ViewFields is what kills performance. SharePoint has to load and process all taxonomy data during the initial query, which is extremely slow.

2. SharePoint stores taxonomy data in a raw format that can be accessed via FieldValuesForEdit and parsed directly. This bypasses the expensive taxonomy service calls entirely.

The breakthrough: Separate regular field loading from taxonomy field loading. Load items with only the fields you need immediately, then load taxonomy fields separately using the faster FieldValuesForEdit approach.

Key Insights:

1. Exclude taxonomy fields from ViewFields - This is the biggest performance gain. Load regular fields first, taxonomy fields separately.
2. FieldValuesForEdit contains raw taxonomy data in the format: Label|GUID;Label|GUID - much faster to parse than TaxonomyFieldValue API
3. Batch loading is crucial - load all items’ FieldValuesForEdit in one call per chunk
4. Chunking prevents timeouts - process items in manageable batches
5. Dictionary lookups are fast - map terms back to items efficiently

The Optimized Solution

Here’s the approach that cut our load times by 70-75%:

Main Loading Logic

public async Task<List<MyItemDTO?>?> GetItemsByOptions(string[] options)
{
    // Step 1: Load list items WITHOUT taxonomy fields in ViewFields
    // This is much faster than including taxonomy fields in the initial query
    List list = clientContext.Web.Lists.GetByTitle("YourListName");
    
    CamlQuery query = new CamlQuery();
    query.Query = @"
        <View>
            <ViewFields>
                <FieldRef Name='ID'/>
                <FieldRef Name='Title'/>
                <FieldRef Name='OtherRegularFields'/>
                <!-- Notice: NO taxonomy fields here -->
            </ViewFields>
            <Query>
                <!-- Your where clause here -->
            </Query>
        </View>";
    
    ListItemCollection items = list.GetItems(query);
    clientContext.Load(items);
    await clientContext.ExecuteQueryAsync();

    if (items == null || items.Count == 0)
        return null;

    // Step 2: Process in chunks and load taxonomy fields separately
    List<MyItemDTO> result = new List<MyItemDTO>();
    
    foreach (ListItem[] chunk in items.Cast<ListItem>().Chunk(100))
    {
        // Load taxonomy fields separately using FieldValuesForEdit
        var categoryTerms = await LoadTaxonomyField(chunk, "CategoryField");
        var tagTerms = await LoadTaxonomyField(chunk, "TagField");

        // Step 3: Map everything together
        foreach (var item in chunk)
        {
            var dto = new MyItemDTO
            {
                Id = item.Id,
                Title = item["Title"]?.ToString(),
                // Map regular fields normally
            };

            // Add taxonomy terms from our separate loading
            if (categoryTerms.TryGetValue(item.Id.ToString(), out var terms))
                dto.CategoryTerms = terms;
                
            if (tagTerms.TryGetValue(item.Id.ToString(), out var tags))
                dto.TagTerms = tags;

            result.Add(dto);
        }
    }

    return result;
}

Batch Taxonomy Loading

private async Task<Dictionary<string, List<TaxonomyTerm>>> LoadTaxonomyField(
    IEnumerable<ListItem> items, 
    string taxonomyFieldInternalName)
{
    // Load FieldValuesForEdit for all items in one batch
    foreach (var item in items)
    {
        clientContext.Load(item, i => i.FieldValuesForEdit);
    }
    await clientContext.ExecuteQueryRetryAsync();

    // Get the field ID for internal format matching
    var list = clientContext.Web.Lists.GetById(listGuid);
    var field = list.Fields.GetFieldByInternalName(taxonomyFieldInternalName);
    clientContext.Load(field);
    await clientContext.ExecuteQueryRetryAsync();

    string fieldId = field.Id.ToString();

    // Parse taxonomy data from each item's FieldValuesForEdit
    return items.ToDictionary(
        item => item.Id.ToString(),
        item => ParseTaxonomyFromEditValues(item, fieldId)
    );
}

private List<TaxonomyTerm> ParseTaxonomyFromEditValues(ListItem item, string fieldId)
{
    // Find the correct field key (SharePoint uses shortened GUIDs)
    var fieldValues = item.FieldValuesForEdit.FieldValues
        .Where(x => x.Key.EndsWith(fieldId.Replace("-", "").Substring(4)));
    
    if (!fieldValues.Any())
        return new List<TaxonomyTerm>();

    string actualFieldKey = fieldValues.First().Key;
    
    if (!item.FieldValuesForEdit.FieldValues.ContainsKey(actualFieldKey))
        return new List<TaxonomyTerm>();

    // Parse the raw taxonomy string
    string rawTaxonomyData = item.FieldValuesForEdit[actualFieldKey];
    return ParseTaxonomyEditString(rawTaxonomyData);
}

Raw Format Parser

private List<TaxonomyTerm> ParseTaxonomyEditString(string editString)
{
    if (string.IsNullOrWhiteSpace(editString))
        return new List<TaxonomyTerm>();

    // Format: "Label1|GUID1;Label2|GUID2;Label3|GUID3"
    return editString
        .Split(new[] { ';' }, StringSplitOptions.RemoveEmptyEntries)
        .Select(entry => 
        {
            var parts = entry.Split('|');
            if (parts.Length == 2 && Guid.TryParse(parts[1], out Guid guid))
            {
                return new TaxonomyTerm 
                { 
                    Label = parts[0].Trim(), 
                    TermGuid = guid 
                };
            }
            return null;
        })
        .Where(t => t != null)
        .Select(t => t!)
        .ToList();
}

public class TaxonomyTerm
{
    public string Label { get; set; }
    public Guid TermGuid { get; set; }
}

Performance Impact

The results were dramatic:

Before optimization:

• 90-120 seconds for a few hundred items with multiple taxonomy fields
• Multiple ExecuteQueryRetryAsync() calls per item
• Heavy taxonomy service usage

After optimization:

• 25-35 seconds for the same dataset
• ~70-75% performance improvement
• Minimal API calls (2 per chunk: one for items, one for field metadata)

The exact savings depend on the number of items and terms, but the pattern held consistently across different datasets.

Should You Try This Approach?

If you’re experiencing slow loading times with taxonomy fields, this might be worth testing.

I can’t give you specific numbers for when this optimization makes sense - every environment and scenario is different. What I can tell you is that in my case, it made a dramatic difference.

My recommendation:

• If your current taxonomy loading is painfully slow, try this approach in a test environment
• Measure the before and after performance in your specific scenario
• Keep the complexity vs. benefit trade-off in mind
• Always have a fallback to the traditional approach

Remember the limitations:

• This is an undocumented approach that works with SharePoint’s internal formats
• You only get basic term information (label + GUID)
• You’ll need to test thoroughly in your environment

The traditional CSOM approach works fine for many scenarios. But if you’re hitting performance walls with taxonomy fields, this optimization might be exactly what you need.

Important Disclaimers

⚠️ This is an undocumented approach that relies on SharePoint’s internal storage format for taxonomy fields. While it has worked reliably in my experience, it’s not officially supported by Microsoft.

Key Takeaways

1. Batch operations are crucial - Load multiple items’ data in single API calls
2. Chunking prevents timeouts - Process large datasets in manageable pieces
3. Raw formats can be faster - Sometimes bypassing official APIs improves performance
4. Know when to optimize - Don’t add complexity unless you really need the performance
5. Document your risks - Be transparent about using undocumented approaches

The traditional CSOM taxonomy approach works fine for simple scenarios. But when you’re dealing with lots of items and lots of terms, sometimes you need to think outside the box.

Fast taxonomy loading is one of five techniques in The SharePoint CSOM Performance Playbook, which is where I’d start if taxonomy isn’t the only thing slowing you down.

I demoed this approach on the Microsoft 365 & Power Platform community demos call in January 2026, under the same title as this post. Those calls are worth an hour of your month if you build on this stack.

So there I was, building what I thought was a pretty straightforward API endpoint. Users fill out a form, click save, and boom—SharePoint list item gets updated. Simple, right?

Wrong.

It was during one of those routine maintenance windows when I decided to review some API logs that I noticed something odd. The numbers just didn’t add up. We were making way more SharePoint API calls than seemed necessary for the amount of actual data changes happening in the system.

Every operation was hitting SharePoint. Even when nothing had actually changed.

That’s when it hit me. Our application was treating every potential update as an actual update, regardless of whether the data was different from what SharePoint already had. Users could open a form, change nothing, click save, and we’d still fire off an API call to “update” the item with identical values.

But SharePoint doesn’t care if you’re setting a field to the exact same value it already has. It still counts that as an API call. It still hits your throttling limits. And if you’re paying for “Service Prioritization in SharePoint”? It still costs you money.

The “Why Did I Even Build This?” Moment

I spent some time digging deeper into the patterns, and the numbers were… enlightening.

This wasn’t just a user behavior issue. It was happening everywhere:

• Users frequently save forms without making actual changes
• Automated processes periodically “sync” data that’s already current
• Applications send complete object states rather than just changed fields
• Integration systems perform routine updates as part of larger workflows

When I looked at a typical day’s worth of operations, I found that roughly 70% of our SharePoint update calls weren’t actually changing anything. We were essentially paying SharePoint to accept identical data and politely say “thanks for the update!”

The more I thought about it, the more I realized this pattern was probably happening in systems everywhere. How many developers have built the same “just update everything” approach without stopping to ask whether anything actually changed?

SharePoint’s “Sure, I’ll Take Your Money” Attitude

Here’s the thing about SharePoint’s CSOM that nobody really talks about: it doesn’t care if you’re being wasteful. You want to set a field to the exact same value it already has? “No problem!” says SharePoint. “That’ll be one API call, please.”

And if you’re using Service Prioritization in SharePoint? That’s real money walking out the door.

Let me show you what I mean with some concrete numbers from a client I worked with recently:

The Head-Scratching Cases

// These should be considered equal, but .NET says "nope"
currentValue: "John Doe"
newValue: "John Doe"  // Easy case, no problem here

currentValue: null
newValue: ""  // Is empty string the same as null? Your call!

currentValue: 42
newValue: "42"  // Different types, same value. Fun times.

The SharePoint Special Cases

// User fields (oh boy...)
currentValue: FieldUserValue { LookupId = 15, LookupValue = "John Doe" }
newValue: FieldUserValue { LookupId = 15, LookupValue = "John Doe" }

// Multi-choice fields that hate you
currentValue: ["Option A", "Option C", "Option B"]
newValue: ["Option B", "Option A", "Option C"]  // Same options, different order

// Taxonomy fields (because why not make it complicated?)
currentValue: TaxonomyFieldValue { TermGuid = "abc-123", Label = "Technology" }
newValue: TaxonomyFieldValue { TermGuid = "abc-123", Label = "Technology" }

After an hour of testing different scenarios, I realized I needed something more sophisticated than oldValue == newValue.

Building the “Actually Smart” Comparison Engine

Alright, time to get serious. I needed a comparison function that could handle all of SharePoint’s quirky field types and still be fast enough to not slow down my API.

Here’s what I ended up building (and yes, it’s a bit of a beast):

internal static List<(string InternalName, object? NewValue)> GetDifferences(
    ListItem item,
    IDictionary<string, object?> newValues,
    bool treatEmptyStringAsNull)
{
    var diffs = new List<(string, object?)>();

    foreach (var kvp in newValues)
    {
        var name = kvp.Key;
        var newVal = kvp.Value;

        object? currentVal = null;
        if (item.FieldValues != null && item.FieldValues.TryGetValue(name, out var cv))
        {
            currentVal = cv;
        }

        if (!ValuesEqual(currentVal, newVal, treatEmptyStringAsNull))
        {
            diffs.Add((name, newVal));
        }
    }

    return diffs;
}

private static bool ValuesEqual(object? a, object? b, bool treatEmptyStringAsNull)
{
    if (ReferenceEquals(a, b))
    {
        return true;
    }

    if (a is null || b is null)
    {
        if (treatEmptyStringAsNull)
        {
            if ((a is null && IsEmptyStringLike(b)) || (b is null && IsEmptyStringLike(a)))
            {
                return true;
            }
        }
        return false;
    }

    a = Normalize(a, treatEmptyStringAsNull);
    b = Normalize(b, treatEmptyStringAsNull);

    if (a is IStructuralEquatable seA && b is IStructuralEquatable seB)
    {
        return StructuralComparisons.StructuralEqualityComparer.Equals(seA, seB);
    }

    return Equals(a, b);
}

private static bool IsEmptyStringLike(object? x)
{
    return x is string s && string.IsNullOrWhiteSpace(s);
}

private static object? Normalize(object? v, bool treatEmptyStringAsNull)
{
    if (v == null)
    {
        return null;
    }

    switch (v)
    {
        case string s:
            return (treatEmptyStringAsNull && string.IsNullOrWhiteSpace(s)) ? null : s;

        case bool b:
            return b;

        case byte or sbyte or short or ushort or int or uint or long or ulong or float or double or decimal:
            return Convert.ToDecimal(v, CultureInfo.InvariantCulture);

        case DateTime dt:
            var utc = (dt.Kind == DateTimeKind.Utc ? dt : DateTime.SpecifyKind(dt, DateTimeKind.Unspecified)).ToUniversalTime();
            return new DateTime(utc.Year, utc.Month, utc.Day, utc.Hour, utc.Minute, utc.Second, DateTimeKind.Utc);

        case FieldUserValue u:
            return u.LookupId;

        case FieldLookupValue l:
            return l.LookupId;

        case IEnumerable<FieldLookupValue> multiLookup:
            return multiLookup.Select(x => x?.LookupId ?? 0).OrderBy(x => x).ToArray();

        case TaxonomyFieldValue tx:
            return tx.TermGuid?.Trim().ToLowerInvariant();

        case TaxonomyFieldValueCollection txc:
            return txc
                .Where(x => x != null && !string.IsNullOrEmpty(x.TermGuid))
                .Select(x => x.TermGuid.Trim().ToLowerInvariant())
                .OrderBy(g => g)
                .ToArray();

        case FieldGeolocationValue geo:
            return new ValueTuple<double, double, double, double>(
                Math.Round(geo.Latitude, 6),
                Math.Round(geo.Longitude, 6),
                Math.Round(geo.Altitude, 2),
                Math.Round(geo.Measure, 2));

        case string[] ss:
            return ss
                .Select(x => treatEmptyStringAsNull && string.IsNullOrWhiteSpace(x) ? null : x)
                .OrderBy(x => x, StringComparer.Ordinal)
                .ToArray();

        case IEnumerable enumerable when v is not string:
        {
            var list = new List<object?>();
            foreach (var e in enumerable)
            {
                list.Add(Normalize(e, treatEmptyStringAsNull));
            }
            return list.ToArray();
        }

        default:
            return v;
    }
}

The Magic Behind the Scenes

1. The Normalization Dance

The Normalize method is where the real magic happens. It takes SharePoint’s various field types and converts them into something we can actually compare:

• Numbers: Everything becomes a decimal because SharePoint loves to mix integers and strings
• DateTime: Converted to UTC and truncated to seconds (because who needs millisecond precision for list items?)
• User/Lookup Fields: We only care about the LookupId, not the display text that might change
• Collections: Sorted alphabetically because SharePoint doesn’t guarantee order
• Strings: Can optionally treat empty/whitespace as null (trust me, you want this)

2. The “It’s Not You, It’s SharePoint” Handling

For arrays and complex objects, I use IStructuralEquatable to do deep comparisons. Because yes, SharePoint will absolutely give you arrays that contain the same items but in different orders.

3. The Business Logic Escape Hatch

The treatEmptyStringAsNull parameter saved my sanity. Different parts of your application might handle empty values differently, and this lets you define your own rules for what counts as “unchanged.”

But Wait, Why Not Just Use JSON Comparison?

Before you ask (because I know you’re thinking it): “Why build this elaborate comparison engine when you could just serialize both objects to JSON and compare the strings?”

Great question. I actually thought the same thing initially. How hard could it be, right? Just JsonSerializer.Serialize() both the old and new values, compare the resulting strings, and call it a day.

Here’s what the “simple” JSON approach looks like:

internal static List<(string InternalName, object? NewValue)> GetDifferencesJson(
    IDictionary<string, object?> oldValues,
    IDictionary<string, object?> newValues,
    bool treatEmptyStringAsNull)
{
    var diffs = new List<(string, object?)>();

    foreach (var kvp in newValues)
    {
        var name = kvp.Key;
        var newVal = kvp.Value;

        object? currentVal = null;
        oldValues?.TryGetValue(name, out currentVal);

        var newJson = JsonSerializer.Serialize(newVal);
        var oldJson = JsonSerializer.Serialize(currentVal);

        if (newJson != oldJson)
        {
            diffs.Add((name, newVal));
        }
    }

    return diffs;
}

Looks clean and simple, right? That’s what I thought too. So I built both approaches and put them head-to-head with BenchmarkDotNet. The results were… eye-opening.

The Performance Reality Check

Here’s what I found when comparing the performance of my custom approach versus JSON string comparison:

Single List Item Comparison:

• Custom Dictionary Approach: 749,625 operations/second (1.334 μs per operation)
• JSON String Approach: 201,109 operations/second (4.972 μs per operation)
• Performance difference: 3.7x faster with the custom approach

Batch Operations (300 items):

• Custom Dictionary Approach: 3,705 operations/second (269.91 μs per batch)
• JSON String Approach: 712 operations/second (1,404.06 μs per batch)
• Performance difference: 5.2x faster with the custom approach

Why JSON Comparison Falls Short

The numbers tell the story, but here’s what’s actually happening under the hood:

1. Serialization Overhead: Converting SharePoint field values to JSON strings requires multiple allocations and string operations
2. Memory Pressure: JSON approach allocated nearly 50% more memory (4.67KB vs 3.17KB per operation)
3. Type Conversion Issues: JSON serialization doesn’t handle SharePoint’s quirky field types the way we need
4. Garbage Collection: More allocations = more GC pressure = slower overall performance

When you’re processing hundreds or thousands of list items in batch operations, that 3-5x performance difference really adds up. Plus, the custom approach gives us complete control over how different field types are compared, which JSON comparison simply can’t provide.

Putting It All Together: The Real-World Implementation

Here’s how I actually use this in my APIs:

public async Task<bool> UpdateListItemAsync(int itemId, Dictionary<string, object?> newValues)
{
    var clientContext = GetClientContext();
    var list = clientContext.Web.Lists.GetByTitle("YourList");
    var listItem = list.GetItemById(itemId);
    
    clientContext.Load(listItem);
    clientContext.ExecuteQuery();

    // The moment of truth - what actually changed?
    var differences = GetDifferences(listItem, newValues, treatEmptyStringAsNull: true);
    
    if (!differences.Any())
    {
        // Nothing changed! Skip the update entirely
        return false; // "Thanks for playing, but you didn't actually change anything"
    }

    // Only update the fields that actually changed
    foreach (var (fieldName, newValue) in differences)
    {
        listItem[fieldName] = newValue;
    }

    listItem.Update();
    clientContext.ExecuteQuery();
    
    return true; // "Something actually happened!"
}

The Numbers Don’t Lie (And They’re Pretty Great)

After implementing this change detection across our applications, here’s what we typically see:

Before implementing change detection:

• Applications making thousands of update requests per day
• Every single one triggered a SharePoint ExecuteQuery()
• High API call volumes and occasional throttling issues

After implementing change detection:

• Same number of update requests from users/systems
• 60-80% contained no actual changes and were skipped
• Dramatically reduced SharePoint API consumption
• Much lower throttling risk

The typical impact:

• Significant reduction in monthly API calls
• Cost savings with Service Prioritization in SharePoint (varies by usage)
• Reduced throttling risk during busy periods
• Faster API responses because skipped operations are basically instant

But here’s what really matters: the performance improvement isn’t just about the numbers. Users notice when applications feel more responsive, and developers notice when they stop getting throttling alerts.

The Gotchas (Because There Are Always Gotchas)

When This Approach Might Not Be For You

1. Audit Requirements: If you need to log every single “save” action regardless of whether anything changed, this might not work for your use case.

2. Always Update Timestamps: Some business requirements dictate that “LastModified” should always be updated when a user clicks save, even if the content is identical.

3. Super Simple Scenarios: If you’re only updating one field and it’s a simple string comparison, the overhead of this elaborate comparison might not be worth it.

The SharePoint Field Types That Made Me Question My Life Choices

Some field types need special handling:

// Calculated fields - don't even try to update these
if (field.ReadOnlyField || field.FieldTypeKind == FieldType.Calculated)
{
    continue; // SharePoint will just ignore you anyway
}

// Attachment fields - these need completely different logic
if (field.FieldTypeKind == FieldType.Attachments)
{
    // Handle separately - attachments are their own special nightmare
    continue;
}

Wrapping Up (And Why This Matters More Than You Think)

Look, I know this might seem like over-engineering for a simple “update SharePoint item” operation. But here’s the thing: those small inefficiencies add up fast.

In my case, this one change:

• Improved user experience with faster response times
• Reduced throttling headaches during busy periods
• Made me feel like a responsible developer (this one’s important too!)

The pattern I’ve shown you isn’t just about SharePoint or just about API optimization. It’s about asking the fundamental question: “Is this operation actually necessary?”

That question has made me a better developer across all the platforms I work with, not just SharePoint.

So next time you’re building any kind of update operation—whether it’s SharePoint, a database, or that JSON file you’re definitely not using as a database—pause for a moment and ask: “Did anything actually change?”

Your future self (and your API bills) will thank you.

Pro tip: Start implementing this pattern on your highest-traffic endpoints first. That’s where you’ll see the biggest impact, and it’ll give you the confidence to roll it out everywhere else.

Now go forth and eliminate those unnecessary API calls. Your SharePoint environment will purr like a happy cat.

Change detection is one of five techniques in The SharePoint CSOM Performance Playbook. If you want the whole picture of where CSOM calls leak, start there.

Recently, I was working with a client to calculate the cost of enabling Service Prioritization in SharePoint on one of their app registrations. The pricing model is straightforward: USD $0.50 per 1,000 Graph calls and USD $1.00 per 1,000 SP REST/CSOM calls.

During the analysis, I discovered something that caught my attention: their solution was making approximately 90,000 calls per month, and 88% of all their CSOM calls were EnsureUser operations.

This was particularly interesting because the users being “ensured” were already associated with the site where the data was being created. It seemed like there should be a more efficient approach - similar to what’s possible with Graph API.

The Problem with User Field Assignment

If you’ve worked with SharePoint user fields, you’re familiar with this common pattern:

// The standard approach
var user = clientContext.Web.EnsureUser("user@company.com");
clientContext.Load(user);
clientContext.ExecuteQuery(); // Network call #1

listItem["AssignedTo"] = new FieldUserValue()
{
    LookupId = user.Id
};
listItem.Update();
clientContext.ExecuteQuery(); // Network call #2

This requires two network calls for each user assignment. Microsoft provides FieldUserValue.FromUser("user@company.com") as an alternative, but it throws an exception if the user isn’t already “known” to the site. This leads to either making two calls to be safe, or handling exceptions with additional calls.

Discovering ExceptionHandlingScope

While researching optimization approaches, I came across a section in Microsoft’s documentation for “Complete basic operations using SharePoint client library code” about ExceptionHandlingScope.

ExceptionHandlingScope allows you to implement try-catch logic that executes on the SharePoint server rather than requiring multiple round trips from your client application. Instead of this painful dance:

1. Try operation → Network call
2. Handle exception → Network call
3. Retry operation → Network call

You get this beautiful symphony:

1. Send try-catch logic to server → Network call
2. Server handles everything internally
3. Get result → You’re done

Here’s the magic in action:

var scope = new ExceptionHandlingScope(clientContext);

using (scope.StartScope())
{
    using (scope.StartTry())
    {
        // Try the optimistic approach first
        listItem["CaseResponsible"] = FieldUserValue.FromUser("user@company.com");
        listItem.Update();
    }
    
    using (scope.StartCatch())
    {
        // If that fails, ensure the user exists
        var user = clientContext.Web.EnsureUser("user@company.com");
        listItem["CaseResponsible"] = FieldUserValue.FromUser("user@company.com");
    }
}

// Execute once - the server handles all the logic
clientContext.ExecuteQuery();

One network call. That’s it.

The server receives your entire try-catch block, attempts the optimistic operation first, and only falls back to EnsureUser if needed. No round trips. No guessing. No waste.

Real-World Impact: Significant Call Reduction

Looking back at the client scenario with ExceptionHandlingScope:

• Before: ~79,200 EnsureUser calls + ~10,800 actual operations = 90,000 total calls
• After: ~10,800-21,600 operations (depending on how many users need ensuring) = substantial reduction

This represents a potential 75-85% reduction in API calls, with corresponding improvements in both performance and Service Prioritization in SharePoint costs.

The Cost Breakdown

Let’s translate this into actual Service Prioritization in SharePoint costs:

Before optimization:

• 90,000 CSOM calls per month
• At USD $1.00 per 1,000 calls
• Monthly cost: $90

After ExceptionHandlingScope optimization:

• Best case: 10,800 calls (if most users are already known) = $10.80/month
• Worst case: 21,600 calls (if many users need ensuring) = $21.60/month
• Monthly savings: $68.40 - $79.20

Annual savings: $820 - $950

These savings become even more significant at scale. For organizations with multiple solutions or higher call volumes, the cost difference can easily reach thousands of dollars annually.

More importantly, the performance improvements—reduced network latency, faster operations, and lower throttling risk—often provide value that far exceeds the direct cost savings.

Throttling: The Hidden Performance Killer

Beyond cost savings, ExceptionHandlingScope provides significant throttling benefits that are often more impactful than the financial savings.

Understanding SharePoint Throttling

SharePoint applies throttling limits to prevent abuse and ensure service stability. When you exceed these limits, you’ll encounter:

• HTTP 429 (Too Many Requests) responses
• Exponential backoff delays (sometimes minutes)
• User experience degradation as operations slow down
• Potential service interruptions during peak usage

The Throttling Math

In our client scenario:

Before ExceptionHandlingScope:

• 90,000 calls per month = ~3,000 calls per day
• During peak hours, this could easily trigger throttling
• Each throttled request requires retry with exponential backoff
• A single throttled operation can delay your entire batch

After ExceptionHandlingScope:

• 10,800-21,600 calls per month = ~360-720 calls per day
• 10x reduction in throttling risk
• Smoother operation during peak business hours
• More predictable performance for end users

Real-World Throttling Impact

Consider a typical business day where multiple users are creating items simultaneously:

• Without optimization: 88% of your throttling budget consumed by redundant EnsureUser calls
• With ExceptionHandlingScope: 88% of your throttling budget available for actual business operations

The beauty of ExceptionHandlingScope is that it doesn’t just reduce the number of calls—it makes your remaining calls more valuable and less likely to be throttled.

Important: ExceptionHandlingScope Is NOT a Magic Bullet

Critical Disclaimer: ExceptionHandlingScope itself does NOT reduce the number of requests to SharePoint. Each operation within the scope still counts as a separate request for throttling purposes.

The reduction in our scenario comes from changing the approach, not from using ExceptionHandlingScope:

What Actually Reduces Calls

• Before: Always calling EnsureUser + setting the field = 2 calls per user
• After: Using FieldUserValue.FromUser() directly, falling back to EnsureUser only when needed

What ExceptionHandlingScope Actually Does

ExceptionHandlingScope reduces network round trips, not server requests:

• Network benefit: 1 round trip instead of potentially 2-3
• Throttling impact: Each operation still counts toward throttling limits
• Performance gain: Reduced latency, not reduced server load

The Real Magic

The 75-85% reduction in our client scenario comes from this logic change:

// This approach reduces actual requests
// Most users are already known to the site
// So most operations only need 1 request instead of 2
using (scope.StartTry())
{
    // This succeeds for ~80% of users (1 request)
    listItem["Field"] = FieldUserValue.FromUser("user@company.com");
}
using (scope.StartCatch())
{
    // This only runs for ~20% of users (1 request)
    var user = clientContext.Web.EnsureUser("user@company.com");
    listItem["Field"] = FieldUserValue.FromUser("user@company.com");
}

ExceptionHandlingScope simply makes this pattern efficient by handling the try-catch logic server-side instead of requiring multiple network round trips to determine which approach to use.

Bottom line: ExceptionHandlingScope optimizes network efficiency, but the request reduction comes from smarter business logic, not from the scope itself.

The Broader Lesson

This experience highlighted an important principle: before optimizing an existing approach, it’s worth questioning whether there’s a fundamentally different way to solve the problem. In this case, server-side exception handling provided a cleaner solution than client-side optimization or caching strategies.

ExceptionHandlingScope has been available since SharePoint 2013, but it’s not widely discussed in the SharePoint development community. It’s a good reminder to periodically review Microsoft’s documentation for features that might address current challenges in new ways.

Your Next Steps

Before you write your next SharePoint operation that involves potential exceptions:

1. Pause and calculate: How many network calls will your approach generate?
2. Question the pattern: Could server-side logic handle the complexity?
3. Explore ExceptionHandlingScope: Can your try-catch logic run on the server?
4. Measure the impact: Compare network calls before and after implementation

Sometimes the most powerful optimizations come from using the tools that were there all along. ExceptionHandlingScope isn’t just a performance optimization—it’s a reminder that the platform often provides solutions we didn’t know we were looking for.

Next time you’re facing a SharePoint performance challenge, remember: the answer might not be in the latest framework or cutting-edge technique. Sometimes, it’s hiding in plain sight in the documentation you scrolled past.

ExceptionHandlingScope is one of five techniques in The SharePoint CSOM Performance Playbook, alongside batching, CAML joins, change detection and fast taxonomy loading.

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.

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:

1. Azure Service Bus for scheduled message delivery
2. 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:

1. User changes customer name → SharePoint webhook fires
2. My webhook Azure Function receives the notification and queues it
3. The webhook queue processor calls WebhookProcessor.HandleListChangeAsync()
4. DebounceScheduler checks Table Storage for any existing scheduled message
5. If one exists, it gets cancelled via the Service Bus sequence number
6. A new message is scheduled for 5 minutes from now
7. 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:

1. In-memory debouncing: Would work for a single instance, but doesn’t scale across multiple Azure Function instances
2. Redis-based debouncing: Would work, but adds another dependency and requires more custom logic
3. Database polling: Could work, but feels clunky and less efficient than Service Bus scheduled messages
4. 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.

Picture this: It’s 2 AM, you’re on your third cup of coffee, and you’re watching your perfectly crafted Microsoft Graph batch request fail spectacularly. Again.

You’ve got 25 files to download from SharePoint. Your batch processes 24 of them perfectly, then one lonely file decides to throw a throttling tantrum. What does your retry logic do? It throws away all 24 successful downloads and starts over. From scratch. Like a digital Groundhog Day, but less amusing and more soul-crushing.

Sound familiar? Welcome to the “retry everything” club – where perfectly good API calls go to die unnecessarily. 😅

The Grocery Cart Problem (Or: Why We’re Doing This Wrong)

Let me paint you a picture. You’re at the grocery store with a cart full of 20 items. You get to checkout, and the cashier says, “Sorry, we’re out of milk.”

What would you do?

• Option A: Put back everything, go home, and come back later to shop for all 20 items again
• Option B: Buy the 19 items you can get, then come back just for the milk

If you picked Option A, congratulations – you think like most API retry logic! If you picked Option B, you’re ready to learn about smart retries.

The “retry everything” approach is like Option A, and here’s why it’s bonkers:

• 🔄 Wasted effort: You’re re-requesting stuff that already worked perfectly
• 🐌 Slower performance: Users wait longer while you redo successful work
• 📈 Throttling amplification: You’re actually making the problem worse by hitting successful endpoints again
• 🔍 Poor debugging: Can’t easily identify which specific requests are the real troublemakers

I learned this the hard way when I watched a simple file sync turn into an API call avalanche. 20 requests became 40, then 80, then… well, let’s just say Microsoft’s throttling system got very acquainted with my application.

The “Aha!” Moment (It’s Simpler Than You Think)

The solution hit me during one of those 2 AM debugging sessions: What if we only retry the stuff that actually failed?

Revolutionary, right? 😏

Here’s the beautiful thing – this isn’t some PhD-level computer science. It’s just common sense applied to code. Keep the winners, retry the losers. Simple.

But (there’s always a “but”), there’s one sneaky technical challenge that makes this trickier than it sounds. Microsoft’s Graph SDK has a helpful method called NewBatchWithFailedRequests(), but it has a quirk: it generates brand new request IDs. This breaks your ability to map responses back to your original data.

Think of it like this: You order pizza for table 5, but when they bring the replacement slice, they call it table 23. Good luck figuring out who ordered what!

If you’re new to Graph batching or request mapping, I’d recommend checking out my post on Graph Batching for File Content: Mapping Requests to Responses first. It’s like the prequel to this story – explains how to keep track of what’s what when dealing with batch responses.

Quick Win Summary (For the Impatient Developers)

The Problem: Your retry logic is like that friend who starts the entire conversation over when they missed one word. Inefficient and annoying.

The Solution: A drop-in extension method that only retries the actual failures while keeping successful responses safe and sound.

The Payoff:

• ⚡ Faster operations (no more re-downloading working files)
• 📉 Fewer API calls (your rate limits will thank you)
• 🎯 Less throttling (stop beating dead endpoints)
• 😌 Happier users (and happier you at 2 AM)

The Catch: You need to understand request-to-response mapping. Don’t worry, it’s not rocket science, and I’ve got a whole post about it.

Time Investment: About 5 minutes to implement, countless hours of frustration saved.

Ready for the nitty-gritty? Let’s dive in! 👇

The Hero of Our Story: The Smart Retry Extension

Okay, here’s where we get our hands dirty. The main challenge isn’t just filtering out successful requests – it’s that pesky NewBatchWithFailedRequests method that scrambles your request IDs like eggs at Sunday brunch.

Here’s the extension method that saves the day:

internal static class GraphServiceClientExtensions 
{
    internal static async Task<(IReadOnlyDictionary<string, HttpStatusCode> Statuses, Dictionary<string, HttpResponseMessage> BatchResponse)> 
        PostBatchWithFailedDependencyRetriesAsync(this GraphServiceClient graphClient, BatchRequestContentCollection originalBatch) 
    {
        const int maxRetries = 5;
        TimeSpan delay = TimeSpan.FromSeconds(1);

        Dictionary<string, HttpResponseMessage> allResponses = new Dictionary<string, HttpResponseMessage>();
        Dictionary<string, HttpStatusCode> allStatuses = new Dictionary<string, HttpStatusCode>();

        BatchRequestContentCollection batchToSend = originalBatch;

        for (int attempt = 1; attempt <= maxRetries; attempt++) 
        {
            BatchResponseContentCollection batchResponse = await graphClient.Batch.PostAsync(batchToSend);
            Dictionary<string, HttpStatusCode> responses = await batchResponse.GetResponsesStatusCodesAsync();

            // Filter out failures (excluding redirects which are normal for file content)
            Dictionary<string, HttpStatusCode> failedRequests = responses
                .Where(kvp => !BatchResponseContent.IsSuccessStatusCode(kvp.Value) && kvp.Value != HttpStatusCode.Found)
                .ToDictionary(kvp => kvp.Key, kvp => kvp.Value);

            // Collect all responses from this attempt
            foreach (var kvp in responses) 
            {
                var response = await batchResponse.GetResponseByIdAsync(kvp.Key);
                allResponses[kvp.Key] = response;
                allStatuses[kvp.Key] = kvp.Value;
            }

            if (failedRequests.Count == 0 || attempt == maxRetries) 
            {
                break;
            }

            await Task.Delay(delay);
            delay = TimeSpan.FromSeconds(delay.TotalSeconds * 2);

            // The key problem: NewBatchWithFailedRequests creates new request IDs!
            batchToSend = batchToSend.NewBatchWithFailedRequests(responses);
            
            // This is why we need this method - restore the original request IDs
            await RestoreOriginalRequestIdsAsync(batchToSend, originalBatch);
        }

        return (allStatuses, allResponses);
    }

    private static async Task RestoreOriginalRequestIdsAsync(
        BatchRequestContentCollection newBatch, 
        BatchRequestContentCollection originalBatch) 
    {
        var stepsSnapshot = newBatch.BatchRequestSteps.ToArray();

        foreach (var kvp in stepsSnapshot) 
        {
            var oldStepId = kvp.Key;
            var step = kvp.Value;
            var requestPath = step.Request.RequestUri!.AbsolutePath;

            // Find the original request ID by matching the request path
            var matchingOriginal = originalBatch.BatchRequestSteps
                .First(x => x.Value.Request.RequestUri!.AbsolutePath == requestPath);

            var originalStepId = matchingOriginal.Key;

            if (oldStepId != originalStepId) 
            {
                newBatch.RemoveBatchRequestStepWithId(oldStepId);

                var newStep = new BatchRequestStep(
                    requestId: originalStepId,
                    httpRequestMessage: step.Request,
                    dependsOn: step.DependsOn);

                newBatch.AddBatchRequestStep(newStep);
            }
        }
    }
}

What’s happening here? Think of it as a diplomatic negotiator for your API calls:

1. The ID Shuffle Problem: NewBatchWithFailedRequests gives failed requests shiny new IDs, like witness protection for HTTP requests
2. The Detective Work: RestoreOriginalRequestIdsAsync plays detective, matching requests by their paths to find their original identities
3. The Happy Reunion: Failed requests get their original IDs back, so your mapping dictionary doesn’t break down in tears

It’s like having a really good wedding planner who makes sure everyone sits at the right table, even after the venue changes.

Showtime: Watching Smart Retries in Action

Now let’s see our smart retry logic work its magic in a real-world scenario. Imagine you’re building a document sync tool and need to download 25 files from SharePoint. Some will work perfectly, others might throw tantrums due to throttling or network hiccups.

Here’s how the new approach handles it like a champ:

// Let's say you need to download content from 25 SharePoint files
var batch = new BatchRequestContentCollection(graphClient);
var fileMapping = new Dictionary<string, FileContentRequest>();

// Build the batch for file content downloads
var filesToDownload = await GetFilesToProcess(); // Your method to get file list

foreach (var file in filesToDownload) 
{
    var requestInfo = graphClient.Sites[siteId]
                                .Drives[driveId]
                                .Items[file.DriveItemId]
                                .Content
                                .ToGetRequestInformation();
    
    var requestId = await batch.AddBatchRequestStepAsync(requestInfo);
    
    // Map the request ID to your file info (same pattern as previous post)
    fileMapping[requestId] = new FileContentRequest 
    { 
        DriveItemId = file.DriveItemId,
        FileName = file.Name,
        ExpectedSize = file.Size,
        DownloadStartTime = DateTime.Now
    };
}

// 🎯 Here's where the magic happens - just one line change!
var (statuses, responses) = await graphClient.PostBatchWithFailedDependencyRetriesAsync(batch);

// Process results - this is where the retry really shines
var successfulDownloads = new List<FileDownloadResult>();
var failedDownloads = new List<string>();

foreach (var kvp in fileMapping) 
{
    var requestId = kvp.Key;
    var fileRequest = kvp.Value;
    
    if (responses.TryGetValue(requestId, out var response)) 
    {
        var statusCode = statuses[requestId];
        
        if (BatchResponseContent.IsSuccessStatusCode(statusCode)) 
        {
            // Success! Handle the file content
            var contentBytes = await response.Content.ReadAsByteArrayAsync();
            
            // Save to your desired location
            var localPath = Path.Combine(downloadFolder, fileRequest.FileName);
            await File.WriteAllBytesAsync(localPath, contentBytes);
            
            successfulDownloads.Add(new FileDownloadResult 
            { 
                FileName = fileRequest.FileName,
                LocalPath = localPath,
                ActualSize = contentBytes.Length,
                ExpectedSize = fileRequest.ExpectedSize,
                DownloadTime = DateTime.Now - fileRequest.DownloadStartTime
            });
        } 
        else 
        {
            // Even after smart retries, this file failed
            failedDownloads.Add($"{fileRequest.FileName} ({statusCode})");
            
            // Log the specific failure for debugging
            Console.WriteLine($"Failed to download {fileRequest.FileName}: {statusCode}");
        }
        
        // Always clean up the response
        response.Dispose();
    }
}

Console.WriteLine($"Successfully downloaded: {successfulDownloads.Count} files");
if (failedDownloads.Any())
{
    Console.WriteLine($"Failed downloads: {string.Join(", ", failedDownloads)}");
}

The beautiful part? Look at that line with the magic emoji 🎯. That’s literally the only change you need to make to your existing batch processing code. Everything else stays exactly the same.

Here’s what’s happening behind the scenes:

1. First batch attempt: Say 20 files succeed, 5 fail due to throttling
2. Smart filtering: Keep those 20 successful responses safe
3. Targeted retry: Build a new batch with just the 5 failures
4. ID preservation: Make sure those 5 retries still map to your original file info
5. Rinse and repeat: Maybe 4 of the 5 succeed on retry, leaving just 1 persistent troublemaker

The result? Instead of making 250 API calls (25 files × 5 retry attempts for the unlucky ones), you might only make 35 total calls. Your throttling problems become manageable, and files download way faster.

// Supporting classes for the example above
public class FileContentRequest 
{
    public string DriveItemId { get; set; } = string.Empty;
    public string FileName { get; set; } = string.Empty;
    public long ExpectedSize { get; set; }
    public DateTime DownloadStartTime { get; set; }
}

public class FileDownloadResult 
{
    public string FileName { get; set; } = string.Empty;
    public string LocalPath { get; set; } = string.Empty;
    public long ActualSize { get; set; }
    public long ExpectedSize { get; set; }
    public TimeSpan DownloadTime { get; set; }
}

The Method to the Madness (What’s Really Happening)

I know that extension method looks intimidating – like trying to read assembly instructions in a foreign language. But once you break it down, it’s actually pretty logical. Let me walk you through the step-by-step dance:

Step 1: The First Attempt “Let’s try everything once and see what happens”

Sends your original batch of 25 files and carefully captures every single response and status code. No throwing anything away yet.

Step 2: The Great Sorting “Okay, who succeeded and who’s being difficult?”

Separates the winners from the losers, but (and this is important) ignores redirect responses. Why? Because when you’re downloading large files, redirects are totally normal – SharePoint often redirects you to the actual storage location.

Step 3: The Preservation Society “Keep the good stuff safe while we deal with the troublemakers”

All successful responses get stored in a safe place while we build a new, smaller batch containing only the failed requests. It’s like having a really good filing system for your API responses.

Step 4: The Identity Crisis Resolution “Wait, who are you again? Let me check your original ID…”

This is the tricky bit! The NewBatchWithFailedRequests method gives everyone new IDs, like a witness protection program for HTTP requests. Our RestoreOriginalRequestIdsAsync method plays detective, matching requests by their URL paths to restore their original identities.

Step 5: The Polite Wait “Let’s not be pushy – maybe try again in a second?”

Implements exponential backoff – starts with a 1-second wait, then 2 seconds, then 4 seconds, etc. This prevents your app from being that annoying person who keeps knocking on the door every second.

Step 6: The Safety Net “Okay, we tried 5 times. Some files just aren’t meant to be downloaded today.”

Gives up after 5 attempts to prevent infinite retry loops. Because sometimes you need to know when to walk away from the poker table.

Why This Actually Works (The Science Behind the Magic)

Here’s what makes this approach so much better than the “retry everything” strategy:

🎯 Surgical Precision Only retry what actually failed – it’s like having a really good therapist who focuses on the actual problems instead of rehashing everything from childhood.

No wasted API calls on requests that already succeeded. If 24 out of 25 files downloaded perfectly, why punish them with another round trip?

⚡ Speed Demon Successful requests don’t get repeated, so everything finishes faster – sometimes dramatically faster.

Users see their successful downloads immediately while you quietly retry the problematic ones in the background.

🤝 Throttling-Friendly Fewer total requests means you’re less likely to hit Microsoft’s rate limits, and when you do, recovery is faster.

Instead of amplifying throttling issues, you’re actually helping to resolve them by reducing load on the endpoints that are already struggling.

🔄 Drop-in Simplicity Change literally one line of code and you’re done. No architectural rewrites, no complex state management – just swap out the method call.

Your existing error handling, logging, and business logic all stay exactly the same.

🔍 Debug Paradise Easy to see exactly which requests are consistently failing, making troubleshooting a breeze instead of a nightmare.

When file “ImportantDocument.pdf” fails on every retry attempt, you know there’s something specific about that file, not your entire batch logic.

The “Before and After” Moment

Let me paint you a picture of how this changes your life:

Before Smart Retries:

• 25 file batch fails on 3 files due to throttling
• Retry all 25 files → now 5 files fail due to increased throttling
• Retry all 25 files again → now 8 files fail
• You’re now in the throttling spiral of doom
• Users are staring at loading spinners
• You’re questioning your career choices

After Smart Retries:

• 25 file batch fails on 3 files due to throttling
• Keep the 22 successful files, retry only the 3 failures
• Maybe 2 of the 3 succeed on retry, leaving 1 stubborn file
• Final retry gets the last file, or you log it as a persistent issue
• Users get 24/25 files quickly, you sleep better at night

It’s the difference between being stuck in traffic because one lane is blocked (and everyone keeps switching to that lane), versus just using the open lanes and going around the problem.

The Bottom Line (And Why Your Future Self Will Thank You)

I’ll be real with you – when I first started working with Microsoft Graph batching, I thought the built-in retry policies were enough. “How hard could it be?” I thought. “APIs fail sometimes, just retry them!”

Then I built my first real-world document sync application. Suddenly, I was dealing with users uploading hundreds of files, enterprise throttling limits, and the occasional network hiccup that would bring the whole operation to a screeching halt.

That’s when I learned the hard way that “retry everything” is like using a sledgehammer to hang a picture frame. Sure, it might work, but you’re probably going to break some stuff in the process.

This selective retry approach has been a game-changer. Not just for performance (though users definitely notice when their bulk operations actually complete), but for debugging too. When you can see that ImportantReport_v23_FINAL_REALLY_FINAL.docx is the file that keeps failing, you can actually do something about it.

The best part? Once you have this extension method in your toolkit, it becomes muscle memory. You’re not adding complexity to your day-to-day development – you’re just swapping out one method call for a smarter one. It’s like upgrading from a flip phone to a smartphone – you wonder how you ever lived without it.

Pro tip: After implementing this, keep an eye on your application logs. You’ll start to notice patterns in failures that you never saw before. Maybe certain file types are more prone to issues, or maybe there’s a specific time of day when throttling gets worse. This kind of insight is pure gold for optimization.

The moral of the story? Sometimes the biggest performance improvements come not from doing things faster, but from doing fewer unnecessary things. And sometimes, the best debugging tool is just… not breaking the working stuff while you fix the broken stuff.

Your 2 AM debugging sessions will never be the same. 😌

Want to Learn More? (The Reading List)

• Microsoft Graph JSON batching - The official documentation (surprisingly readable!)
• Graph Batching for File Content: Mapping Requests to Responses - My previous post that sets up the foundation for this one
• Microsoft Graph throttling guidance - Understanding what makes Microsoft’s APIs cranky
• Graph Explorer - Test your batch requests interactively (great for experimenting)
• Exponential Backoff Pattern - The polite way to retry things

Now go forth and batch smarter, not harder! 🚀

Picture this: you need to download 50 files from SharePoint using Microsoft Graph. Being a good developer, you decide to use batching instead of making 50 individual API calls (because nobody wants to wait that long, and Microsoft’s throttling limits aren’t going anywhere).

You set up your batch request, send it off, and get your responses back. Great! Except… now you’re staring at a bunch of file content with absolutely no way to tell which file is which. 😅

Unlike other Graph operations that return nice JSON objects with IDs and metadata, file content responses are just raw bytes. No file name, no path, no ID - nothing to help you figure out which response belongs to which original request.

I learned this the hard way when I first tried Graph batching for file downloads. Spent way too much time trying to correlate responses by file size or content patterns before realizing there was a much cleaner solution.

The Mapping Solution (It’s Simpler Than You Think)

The trick is surprisingly straightforward: use the batch request ID as your bridge between the original file info and the response content. Every batch request gets a unique ID, and that same ID comes back with the response.

Here’s the game plan:

1. Create a dictionary mapping request IDs to your original file information
2. Build your batch requests and store the mappings
3. Process responses using the request ID to look up the original file info

Let me show you exactly how this works.

Setting Up Your File Information

First, let’s create a simple model to hold our file details:

public class FileInfoDTO {
    public string? Path { get; set; }
    public string? Name { get; set; }
    public string? RelativePath { get; set; }
    public string? UniqueFileName { get; set; }
}

Nothing fancy here - just the basics we need to identify and process each file.

The Complete Batch Download Method

Here’s the full implementation. Don’t worry, I’ll break down the important parts afterward:

private async Task<Dictionary<string, byte[]>> DownloadFilesBatchAsync(
    FileInfoDTO[] fileInfos, 
    string siteId, 
    string driveId) {
    
    using BatchRequestContent batchRequestContent = new BatchRequestContent();
    Dictionary<string, FileInfoDTO> requestMapping = new Dictionary<string, FileInfoDTO>();
    Dictionary<string, byte[]> results = new Dictionary<string, byte[]>();

    // Build batch requests with mapping
    foreach (FileInfoDTO fileInfo in fileInfos) {
        if (string.IsNullOrEmpty(fileInfo.RelativePath) || string.IsNullOrEmpty(fileInfo.UniqueFileName))
            continue;

        string requestId = batchRequestContent.AddBatchRequestStep(
            GraphClient.Sites[siteId]
                      .Drives[driveId]
                      .Root
                      .ItemWithPath(fileInfo.RelativePath)
                      .Content
                      .Request());

        // This is the magic - storing the mapping!
        requestMapping[requestId] = fileInfo;
    }

    // Execute batch request
    BatchResponseContent batchResponse = await GraphClient.Batch.Request().PostAsync(batchRequestContent);
    Dictionary<string, HttpResponseMessage> responses = await batchResponse.GetResponsesAsync();

    // Process responses using our mapping
    foreach ((string requestId, HttpResponseMessage response) in responses) {
        FileInfoDTO originalFile = requestMapping[requestId]; // Look up the original file info
        
        try {
            switch (response.StatusCode) {
                case HttpStatusCode.OK:
                    byte[] content = await response.Content.ReadAsByteArrayAsync();
                    results[originalFile.UniqueFileName!] = content;
                    break;
                    
                case HttpStatusCode.Redirect:
                    // Handle redirect for large files (more on this below)
                    byte[] redirectContent = await DownloadFromRedirectAsync(response.Headers.Location);
                    results[originalFile.UniqueFileName!] = redirectContent;
                    break;
                    
                case HttpStatusCode.TooManyRequests:
                    // Handle throttling
                    throw new Exception($"Throttled request for {originalFile.Name}");
                    
                default:
                    throw new Exception($"Failed to download {originalFile.Name}: {response.ReasonPhrase}");
            }
        }
        finally {
            // Always dispose - learned this one the hard way after some memory leak hunting
            response.Content.Dispose();
        }
    }

    return results;
}

The Key Parts Explained

The Mapping Dictionary

Dictionary<string, FileInfoDTO> requestMapping = new Dictionary<string, FileInfoDTO>();

This is your lifeline. For every request you add to the batch, you store the request ID and link it to your original file information. When responses come back, you can instantly look up which file each response belongs to.

Building Requests with Mapping

string requestId = batchRequestContent.AddBatchRequestStep(...);
requestMapping[requestId] = fileInfo;

The AddBatchRequestStep method returns a unique request ID. Store this immediately - you’ll need it to match responses later.

Handling the Redirect Curveball

Here’s something that caught me off guard initially: large files don’t return content directly. Instead, Graph gives you a redirect to an Azure Blob Storage URL where the actual file lives. Microsoft doesn’t specify the exact file size threshold, but in practice, I’ve observed this happening with files larger than a few MB.

private async Task<byte[]> DownloadFromRedirectAsync(Uri? redirectUri) {
    if (redirectUri == null)
        throw new ArgumentException("Redirect URI is null");

    using HttpClient httpClient = new HttpClient();
    return await httpClient.GetByteArrayAsync(redirectUri);
}

This happens because Microsoft doesn’t want to push huge files through the Graph API unnecessarily. The redirect URL is temporary and works great - just make sure you handle it properly.

Note: The exact file size that triggers a redirect isn’t officially documented by Microsoft, so always handle both direct content (200 OK) and redirect (302) responses in your code.

Error Handling That Actually Helps

When things go wrong (and they will), you want meaningful error messages. Here’s how to handle the common scenarios:

private async Task ProcessBatchResponseAsync(
    string requestId, 
    HttpResponseMessage response, 
    FileInfoDTO originalFile,
    Dictionary<string, byte[]> results) {
    
    try {
        switch (response.StatusCode) {
            case HttpStatusCode.OK:
                byte[] content = await response.Content.ReadAsByteArrayAsync();
                results[originalFile.UniqueFileName!] = content;
                break;
                
            case HttpStatusCode.Redirect:
            case HttpStatusCode.Found:
                byte[] redirectContent = await DownloadFromRedirectAsync(response.Headers.Location);
                results[originalFile.UniqueFileName!] = redirectContent;
                break;
                
            case HttpStatusCode.NotFound:
                Console.WriteLine($"File not found: {originalFile.Name} at {originalFile.RelativePath}");
                // Maybe the file was moved or deleted
                break;
                
            case HttpStatusCode.TooManyRequests:
                Console.WriteLine($"Throttled request for: {originalFile.Name}");
                // This is where retry logic would go (coming in the next post!)
                break;
                
            case HttpStatusCode.Forbidden:
                Console.WriteLine($"Access denied for: {originalFile.Name}");
                // Check your permissions
                break;
                
            default:
                Console.WriteLine($"Unexpected error downloading {originalFile.Name}: {response.StatusCode} - {response.ReasonPhrase}");
                break;
        }
    }
    finally {
        response.Content?.Dispose(); // Don't leak memory!
    }
}

Important Things to Remember

Batch Size Limits

Graph batching has a hard limit of 20 requests per batch. If you have more files, you’ll need to chunk them:

const int BATCH_SIZE = 20;
for (int i = 0; i < fileInfos.Length; i += BATCH_SIZE) {
    var batch = fileInfos.Skip(i).Take(BATCH_SIZE).ToArray();
    var batchResults = await DownloadFilesBatchAsync(batch, siteId, driveId);
    // Merge results...
}

Memory Management

Always dispose of HttpResponseMessage.Content. File downloads can be large, and forgetting to dispose will cause memory leaks that are painful to debug.

Request ID Uniqueness

Request IDs are unique within a single batch, but not across different batches. Don’t try to reuse mappings between different batch operations.

Why This Pattern Works So Well

This approach has several advantages that make it my go-to solution:

• Dead Simple: No complex logic, just a straightforward mapping pattern
• Reliable: Works consistently regardless of file sizes or response order
• Memory Efficient: Proper cleanup prevents memory leaks
• Debuggable: Easy to trace issues when something goes wrong
• Extensible: Perfect foundation for adding retry logic later

The beauty is in its simplicity. You’re not trying to guess which response belongs to which request - you know exactly because you mapped it from the start.

What’s Next?

This mapping pattern solves the core problem of correlating Graph batch responses with your original requests. But what happens when some requests fail due to throttling or temporary errors?

In my next post, I’ll show you how to build retry logic on top of this foundation that automatically handles failed requests without losing track of which files still need to be downloaded.

Have you run into this mapping challenge before? Let me know in the comments how you solved it - I’m always curious about different approaches! 🚀

Following up on my previous post about DevProxy and throttling testing, there’s another critical performance issue I see regularly in SharePoint CSOM code: executing operations one by one instead of batching them.

Consider this common pattern that I see everywhere:

// ❌ This approach is slow and inefficient
foreach (var id in itemIds)
{
    var item = list.GetItemById(id);
    context.Load(item);
    await context.ExecuteQueryAsync(); // Executing for EACH item!
    
    // Process the item
    ProcessItem(item);
}

This code makes a server round-trip for every single item. If you’re processing 50 items, that’s 50 separate calls to SharePoint. Each call has network latency and server processing time.

The Solution: Batch Operations

SharePoint CSOM reliably handles batches of around 100 operations before calling ExecuteQueryAsync(). This means you can dramatically reduce the number of server round-trips.

Microsoft’s official documentation also emphasizes this pattern in their performance guidelines, showing how grouping data retrieval operations significantly improves performance.

Here’s the pattern I use in most of my SharePoint projects:

The Helper Methods

public static async Task<List<T>> GetItemsByIds<T>(this List list, IEnumerable<int> ids) 
    where T : new()
{
    if (ids == null || !ids.Any())
    {
        return new List<T>();
    }

    var listItems = await CSOMHelpers.ProcessInChunks(ids.ToList(), 100, async chunk => {
        var items = chunk.Select(id => {
            var item = list.GetItemById(id);
            list.Context.Load(item);
            return item;
        }).ToList();

        await ExecuteQuery(list.Context, () => { /* Load calls already done above */ });
        
        return items;
    });

    return listItems.ToList();
}

internal static async Task<List<TOut>> ProcessInChunks<TIn, TOut>(List<TIn> source, int chunkSize, Func<List<TIn>, Task<List<TOut>>> action)
{
    var result = new List<TOut>();
    foreach (var chunk in source.Chunk(chunkSize))
    {
        result.AddRange(await action(chunk.ToList()));
    }
    return result;
}

Performance Impact: The Numbers

Let me show you the difference with a real-world example. Loading 50 SharePoint list items:

Before (One-by-One)

• 50 server calls
• Network latency: 50ms × 50 = 2.5 seconds
• Actual total time is usually higher due to server-side processing, hence: ~3-5 seconds

After (Batched)

• 1 server call (all 50 items in one batch)
• Network latency: 50ms × 1 = 50ms
• Total time: ~200-500ms

⚠️ Performance Disclaimer

The numbers shown above are from a specific example scenario and are meant to illustrate the concept of batching benefits. Your actual performance gains will vary significantly based on:

• Network latency and bandwidth
• SharePoint server load and location
• Item complexity and field count
• Query complexity and filtering
• Authentication overhead
• Time of day and concurrent users

While batching almost always improves performance, the exact improvement factor can range from marginal gains to dramatic improvements. The key takeaway is the pattern and approach, not the specific numbers.

Applying the Pattern to CRUD Operations

This pattern works for all CSOM operations, not just reading, but for all the CRUD operations.

Why 100 Items?

SharePoint has practical limits on how many operations you can batch:

• CSOM limit: Around 100 operations per batch
• REST API limit: Different limits depending on operation type
• Network payload: Larger batches mean bigger HTTP requests

I’ve found 100 items to be the sweet spot that:

• Maximizes performance gains
• Stays well within SharePoint limits
• Keeps HTTP payloads manageable
• Works reliably across different SharePoint environments

Combining with Throttling Protection

When you combine this batching approach with the throttling protection patterns from my DevProxy post, you get robust, high-performance SharePoint applications.

Best Practices

1. Batch whenever you can - even for small numbers of items, batching usually helps.
2. Use 100 as your chunk size for most scenarios
3. Combine with retry logic for production resilience
4. Test with DevProxy to ensure your batching works under throttling conditions
5. Monitor performance before and after implementing batching

The Bottom Line

Batching CSOM operations is one of the easiest wins for SharePoint performance optimization. The code pattern is straightforward to implement and reuse, but the performance impact is dramatic.

Stop executing SharePoint operations one by one. Your users (and SharePoint servers) will thank you.

Batching is the first of five techniques I use to keep CSOM cheap. The rest, and the order I reach for them in, are in The SharePoint CSOM Performance Playbook.

Resources

• SharePoint CSOM Best Practices
• DevProxy for Testing Throttling
• SharePoint Performance Guidelines

If you’ve ever optimized your C# API calls with parallel programming, you might know this story.

Your client wants faster performance. You run multiple API requests in parallel. Locally, everything flies — especially late at night when traffic is low. But the next morning, on the final pre-deployment test, disaster strikes.

Random errors. Requests delayed for 45 seconds.
Microsoft Graph or SharePoint has slammed you with throttling and rate limits. Your blazing-fast local solution crumbles under production-like conditions.

How CRUD Operations Can Secretly Burn Through Your Limits — and How to Catch Them with DevProxy

Here’s the thing:
SharePoint Online charges “Resource Units” (RUs) for every request you make. Think of RUs as an invisible currency — every API call you send deducts from your allowance. Run out too fast, and throttling kicks in.

And those “simple” operations? They’re not as cheap as you think.

From Microsoft’s RU table:

Source: Microsoft’s official SharePoint throttling documentation

Permission expansions are just one type of “expensive” call. Another common scenario that can impact RU consumption is making multiple separate queries instead of using CAML joins — something I explored in my CAML join post.
While joining multiple lists in a single query is actually more efficient than separate calls, poorly structured queries or retrieving unnecessarily large datasets can still consume RUs quickly if not optimized properly.

Why This Sneaks Up on You

When coding locally, it’s easy to run a handful of queries without noticing.
But in production, with real concurrency and volume, these calls pile up in seconds.

Imagine:

• 10 parallel updates (2 RUs each) = 20 RUs in one burst
• Add a few joins/expansions or large list queries, and you’re suddenly burning through limits 5x faster.

How DevProxy Can Show You the Pain Before Production

Here’s where DevProxy shines.
If you configure it to simulate throttling based on these RU-heavy calls, you’ll see the impact locally:

1. Enable throttling plugins in your devproxy.json (as shown earlier).
2. Point urlsToWatch at your SharePoint endpoints.
3. Run your CRUD-heavy code.

DevProxy will start throwing 429s (Too Many Requests) once your “fake” RU budget is exhausted — just like SharePoint would in production.

The beautiful part?
You can crank the limits down during testing to make expensive patterns obvious. Even a single large query or unbatched Update will light up your logs.

Example DevProxy log when hitting a throttling simulation:

[Warning] Throttling triggered: 2 parallel Create calls exceeded RU limit (RateLimitingPlugin)
Retry after: 30 seconds

Pro tip:
Use DevProxy as a budget meter for your API calls. Treat every 2-RU and 5-RU operation as “spending big” — and redesign those spots before they become a production outage.

What is DevProxy?

DevProxy is an open-source HTTP/HTTPS proxy server that can simulate real-world network issues, including:

• Rate limiting
• Throttling
• Network delays
• Intermittent errors

It’s free, open source, and designed for developers who want to catch performance bottlenecks before they reach production.

Installing DevProxy

Follow the official setup guide here:
DevProxy Installation Documentation

Basic Usage

Run DevProxy with the default configuration:

devproxy

It will begin intercepting all HTTP/HTTPS requests.

Tip: Start DevProxy before running your own code — otherwise, it won’t capture the traffic.

Simulating Microsoft Graph Throttling

Here’s how to configure DevProxy to reproduce Microsoft Graph and SharePoint throttling issues locally.

1. Create a devproxy.json configuration file

This is the exact configuration I used when testing my problematic parallel code:

{
  "$schema": "https://raw.githubusercontent.com/dotnet/dev-proxy/main/schemas/v0.27.0/rc.schema.json",
  "rate": 25,
  "plugins": [
    {
      "name": "RetryAfterPlugin",
      "enabled": true,
      "pluginPath": "~appFolder/plugins/dev-proxy-plugins.dll"
    },
    {
      "name": "RateLimitingPlugin",
      "enabled": true,
      "pluginPath": "~appFolder/plugins/dev-proxy-plugins.dll",
      "configSection": "rateLimitingPlugin"
    },
    {
      "name": "GraphRandomErrorPlugin",
      "enabled": true,
      "pluginPath": "~appFolder/plugins/dev-proxy-plugins.dll",
      "configSection": "graphRandomErrorPlugin"
    }
  ],
  "urlsToWatch": [
    "https://graph.microsoft.com/v1.0/*",
    "https://graph.microsoft.com/beta/*",
    "https://graph.microsoft.us/v1.0/*",
    "https://graph.microsoft.us/beta/*",
    "https://dod-graph.microsoft.us/v1.0/*",
    "https://dod-graph.microsoft.us/beta/*",
    "https://microsoftgraph.chinacloudapi.cn/v1.0/*",
    "https://microsoftgraph.chinacloudapi.cn/beta/*",
    "https://*.sharepoint.*/*_api/web/GetClientSideComponents",
    "https://*.sharepoint.*/*_api/*",
    "https://*.sharepoint.*/*_vti_bin/*",
    "https://*.sharepoint-df.*/*_api/*",
    "https://*.sharepoint-df.*/*_vti_bin/*"
  ],
  "graphRandomErrorPlugin": {
    "$schema": "https://raw.githubusercontent.com/dotnet/dev-proxy/main/schemas/v0.27.0/graphrandomerrorplugin.schema.json",
    "allowedErrors": [
      429,
      503,
      504
    ]
  },
  "rateLimitingPlugin": {
    "$schema": "https://raw.githubusercontent.com/dotnet/dev-proxy/main/schemas/v0.27.0/ratelimitingplugin.schema.json",
    "costPerRequest": 2,
    "rateLimit": 120
  },
  "logLevel": "information",
  "newVersionNotification": "stable",
  "showSkipMessages": true,
  "showTimestamps": true
}

2. Start DevProxy with your configuration

devproxy --config-file devproxy.json

Example of Problematic Code

Here’s the snippet that caused my throttling nightmare. It uses Parallel.ForEachAsync to query SharePoint in bulk:

// ❌ This will likely trigger throttling in production
var customers = new ConcurrentBag<CustomerDTO>();

await Parallel.ForEachAsync(departmentIds, async (departmentId, token) => 
{
    var clonedContext = _clientContext.Clone(_clientContext.Url);

    var query = Camlex.Query()
        .ViewFields(new CustomerDTO().ViewFields().ToArray().Append("DepartmentId"))
        .LeftJoin(x => x["DepartmentLookup"].ForeignList(DEPARTMENT_LIST_GUID))
        .ProjectedField(x => x["DepartmentId"].List(DEPARTMENT_LIST_GUID).ShowField("ID"))
        .Where(x => x["DepartmentId"] == departmentId);

    var departmentCustomers = await SharePointService.GetItemsFromListByQuery<CustomerDTO>(
        CUSTOMER_LIST_GUID,
        clonedContext,
        query);

    if (departmentCustomers?.Any() == true)
    {
        foreach (var customer in departmentCustomers)
        {
            customers.Add(customer);
        }
    }
});

Why This Code Fails in Production

1. No throttling safeguards — Multiple parallel requests overwhelm SharePoint.
2. No retry logic — Requests fail instead of recovering.
3. No request limiting — All departments are processed simultaneously.
4. Silent failures — Errors are ignored without logging or fallback.

The Better Way

Instead of hard-coding fixes here, I recommend Bert Jansen’s detailed guide on throttling and rate limit handling:
Throttling & Rate Limit Handling Patterns

Key principles:

• Limit concurrency with SemaphoreSlim.
• Use exponential backoff for retries.
• Monitor rate limit headers to adjust requests dynamically.
• Handle errors explicitly to prevent silent failures.

Conclusion

DevProxy has become an essential tool in my Microsoft 365 development workflow. It helps me:

• Catch throttling issues before production.
• Test error handling and retry logic locally.
• Deliver applications that can survive real-world API limits.

If I’d used DevProxy from the start, I could have avoided the last-minute throttling meltdown entirely.

💡 Pro tip: Make DevProxy part of your early development process, not your emergency toolkit.

Additional Resources

• DevProxy GitHub Repository
• Microsoft Graph Throttling Guidelines
• DevProxy Documentation

When working with related data across multiple SharePoint lists, developers often fall into the trap of making multiple individual queries:

// ❌ Bad approach - Multiple API calls
var customers = context.Web.Lists.GetByTitle("Customers");
var orders = context.Web.Lists.GetByTitle("Orders");
var orderItems = context.Web.Lists.GetByTitle("OrderItems");

// Each query consumes 2 resource units
var customerItems = customers.GetItems(camlQuery1);  // 2 units
var orderItems = orders.GetItems(camlQuery2);        // 2 units  
var itemDetails = orderItems.GetItems(camlQuery3);   // 2 units
// Total: 6 resource units + processing overhead

This approach has several issues:

• Higher resource consumption: Each query consumes 2 resource units per multi-item request
• Increased throttling risk: More API calls mean hitting limits faster
• Network overhead: Multiple round trips to SharePoint
• Complex data merging: Manual joining of results in C#

How to fix it?

I have worked alot with MSSQL where it is pretty easy to just join a table on a table on a table. But I did not know it was possible in CSOM. Eg. in the UI of SharePoint you can only expand one table - NOT multiple. But one day I deep dived into CAML and joins, and found out it was possible!

Resource Unit Comparison:

Let’s break down the actual cost difference. According to Microsoft’s throttling documentation, each multi-item query consumes 2 resource units.

Multiple separate queries (❌ Bad approach):

// Query 1: Get order items from main list
var orderItems = ordersList.GetItems(query1);        // 2 resource units

// Query 2: Get customer details  
var customers = customersList.GetItems(query2);      // 2 resource units

// Query 3: Get order details
var orderDetails = orderDetailsList.GetItems(query3); // 2 resource units

// Total: 6 resource units + network overhead + manual C# joining

Single join query (✅ Good approach):

// One query with joins gets ALL the data
var items = ordersList.GetItems(joinQuery);          // 2 resource units total

The math:

• Multiple queries: 2 units × 3 lists = 6 units
• Single join query: 2 units total
• Savings: 66% fewer resource units!

This becomes even more significant when you consider that SharePoint throttling limits are measured in resource units per time window. With joins, you can query 3x more data within the same throttling limits.

The Solution: CAML Joins

CAML actually supports joining multiple lists in a single query! This means you can get data from related lists without multiple API calls.

First, install CAMLEX via NuGet:

Install-Package Camlex.Client.dll

Then build your join query:

var list = context.Web.Lists.GetByTitle("YourMainList");

CamlexNET.Interfaces.IQuery query = Camlex.Query();

query = query
.LeftJoin(x => x["OrderTaskLookUp"].ForeignList(ListGuidOrderTasks))
.LeftJoin(x => x["OrderDetailLookUp"].PrimaryList(ListGuidOrderTasks).ForeignList(ListGuidOrders))
.LeftJoin(x => x["CustomerLookUp"].PrimaryList(ListGuidOrders).ForeignList(ListGuidCustomers))
.ProjectedField(x => x["CustomerNo"].List(ListGuidCustomers).ShowField("CustomerNo"))
.ProjectedField(x => x["CustomerName"].List(ListGuidCustomers).ShowField("CustomerName"));

var camlQuery = new CamlQuery();
camlQuery.ViewXml = query.ToString();

var items = list.GetItems(camlQuery);
context.Load(items);
context.ExecuteQuery();

Important: Your lists need to be connected via lookup columns for joins to work!

Why CAMLEX Instead of Raw CAML?

I don’t write raw CAML myself - it’s verbose and error-prone. Instead, I use CAMLEX because:

• Cleaner syntax: C# lambda expressions instead of XML
• IntelliSense support: Catch errors at compile time
• Easier for the next developer: Self-documenting code
• Less mistakes: No more XML typos or missing tags

What else?

One thing is to join multiple lists, but to filter on a value 4 lists away - That is neat!

For example, filtering orders by the customer’s name, which is 2 lists away:

CamlexNET.Interfaces.IQuery query = Camlex.Query();

query = query
.Where(x => (string)x["CustomerName"] == "Contoso") // <---- Filtering on join field
.LeftJoin(x => x["OrderTaskLookUp"].ForeignList(ListGuidOrderTasks))
.LeftJoin(x => x["OrderDetailLookUp"].PrimaryList(ListGuidOrderTasks).ForeignList(ListGuidOrders))
.LeftJoin(x => x["CustomerLookUp"].PrimaryList(ListGuidOrders).ForeignList(ListGuidCustomers))
.ProjectedField(x => x["CustomerNo"].List(ListGuidCustomers).ShowField("CustomerNo"))
.ProjectedField(x => x["CustomerName"].List(ListGuidCustomers).ShowField("CustomerName"));

var camlQuery = new CamlQuery();
camlQuery.ViewXml = query.ToString();
var items = ordersList.GetItems(camlQuery);

This CAMLEX query automatically generates the following CAML XML - notice how complex the raw XML is compared to the clean C# syntax above:

<View>
  <Query>
    <Where>
      <Geq>
        <FieldRef Name="customerName" />
        <Value Type="Text">Contoso</Value>
      </Geq>
    </Where>
  </Query>
  <ViewFields>
    <FieldRef Name="Id" />
    <FieldRef Name="customerNo" />
    <FieldRef Name="customerName" />
  </ViewFields>
  <Joins>
    <Join Type="LEFT" ListAlias="0f0cfc71-1c6e-4fd4-b6f2-279d0e3862f4">
      <Eq>
        <FieldRef Name="OrderTaskLookUp" RefType="Id" />
        <FieldRef List="0f0cfc71-1c6e-4fd4-b6f2-279d0e3862f4" Name="Id" />
      </Eq>
    </Join>
    <Join Type="LEFT" ListAlias="ce57b9a2-5052-482c-a8c8-150c7d59ced3">
      <Eq>
        <FieldRef List="0f0cfc71-1c6e-4fd4-b6f2-279d0e3862f4" Name="OrderDetailLookUp" RefType="Id" />
        <FieldRef List="ce57b9a2-5052-482c-a8c8-150c7d59ced3" Name="Id" />
      </Eq>
    </Join>
    <Join Type="LEFT" ListAlias="e204ed0f-7c25-432f-9228-3eb438c527e2">
      <Eq>
        <FieldRef List="ce57b9a2-5052-482c-a8c8-150c7d59ced3" Name="CustomerLookUp" RefType="Id" />
        <FieldRef List="e204ed0f-7c25-432f-9228-3eb438c527e2" Name="Id" />
      </Eq>
    </Join>
  </Joins>
  <ProjectedFields>
    <Field 
      Name="customerNo" 
      Type="Lookup" 
      List="e204ed0f-7c25-432f-9228-3eb438c527e2" 
      ShowField="customerNo" 
    />
    <Field 
      Name="customerName" 
      Type="Lookup" 
      List="e204ed0f-7c25-432f-9228-3eb438c527e2" 
      ShowField="customerName"
    />
  </ProjectedFields>
</View>

Imagine having to write and maintain that XML manually! This is exactly why CAMLEX is so valuable - you get all the power of CAML joins with readable C# syntax.

Performance Benefits

Before (Multiple queries):

• 🔴 6+ resource units
• 🔴 Multiple network calls
• 🔴 Complex C# merging logic

After (Single join):

• ✅ 2 resource units only
• ✅ One network call
• ✅ Server-side joining

Key Takeaways

• Use joins instead of multiple queries to reduce resource consumption
• CAMLEX makes CAML readable for you and the next developer
• You can filter on joined data even multiple lists away
• SharePoint UI limitations ≠ API limitations - joins work even if the UI doesn’t show it

Common Issues & Solutions

❌ “List does not exist” error

// Use list GUIDs instead of names for reliability
.ForeignList(new Guid("12345678-1234-1234-1234-123456789012"))

❌ “Field not found” error

// Use internal field names, not display names
.ShowField("Title")        // ✅ Internal name
.ShowField("Customer Name") // ❌ Display name

❌ Join returns no data

• Verify lookup columns exist and are properly configured
• Check that you’re joining on the correct fields
• Ensure the lookup field contains valid IDs

❌ “Cannot project this field type” error

Only specific field types can be included in ProjectedFields:

✅ Supported ProjectedFields types:

• Calculated (treated as plain text)
• ContentTypeId
• Counter
• Currency
• DateTime
• Guid
• Integer
• Note (one-line only)
• Number
• Text

❌ NOT supported in ProjectedFields:

• Multi-line text fields
• Rich text fields
• Choice fields
• Lookup fields (use joins instead)
• User/Person fields
• Managed metadata fields

💡 Pro tip: If you need data from unsupported field types, query them separately after getting your joined results.

CAML joins are one of five techniques in The SharePoint CSOM Performance Playbook, which covers when a join is the right answer and when batching or change detection would serve you better.