Recursive Data Retrieval Techniques For Large Sharepoint Document Libraries

Efficiently Loading Large SharePoint Document Libraries

As SharePoint document libraries grow to contain tens or hundreds of thousands of files, users face increasingly long load times when accessing these large libraries. The default paged loading behavior retrieves data in batches, often resulting in delays of 30 seconds or more before the library finishes populating. This leads to a painful user experience where employees must stare at the spinning refresh icon as they wait for documents to appear.

For example, an organization’s research library with over 50,000 PDFs and images can take several minutes to start loading files and thumbnails. If a user needs to browse and open documents quickly, the long load times drastically reduce productivity. Similarly, an engineering firm’s project files library with over 75,000 CAD drawings and specifications sheets will leave engineers drumming their fingers on their desks impatiently as they watch the browser struggle.

Recursive Data Retrieval Techniques

Recursive algorithms provide an efficient way to retrieve data from large datasets through the method of divide and conquer. A recursive procedure continues dividing a problem into smaller sub-problems, solving each sub-problem, and aggregating the results. In computer science, recursion manifests through functions that call themselves to repeat an operation.

A common example is the binary search algorithm, which locates an item in a sorted array by first checking the middle element and discarding half the elements depending on the comparison. It then repeats this process on the remaining half, quickly zooming in on the target item. Recursive techniques like binary search can rapidly retrieve data from large libraries without needing to linearly traverse all items.

Implementing Recursive Document Loads in SharePoint

To leverage recursive data retrieval in SharePoint document libraries, developers can create a custom library controller that recursively queries data in manageable batches. Below is sample C# code for a recursive document loader:

public IEnumerable<Document> GetDocuments(ISharePointContext context, int batchSize)  
{

  List<Document> documents = null;
  
  // Recursive function to fetch documents
  Func<int, IEnumerable<Document>> getBatch = null; 
  getBatch = (skip) =>
  {
    // Query next batch from SharePoint  
    documents = context.GetDocumentBatch(skip, batchSize);
    
    // Check if enough documents were returned
    if(documents.Count == batchSize)
    {
       // Concat batch with recursive call
       return documents.Concat(getBatch(skip + batchSize)); 
    }
    else 
    {
       // Base case: return last batch  
       return documents;
    }
  }

  return getBatch(0);

}

This approach recursively concatenates query batches, avoiding having to fetch all documents in a single expensive call. The batch size can be tuned based on the specific library size and performance goals.

Incremental Population for Seamless Scaling

While recursive data access helps query performance, the user experience still suffers from watching a large library load in batches. Incrementally showing results as they become available provides a smoother scaling interface.

As the recursive loader receives each batch, the batch can be immediately appended to the visible library, populated new rows and thumbnails on-demand. This keeps the UI responsive by incrementally constructing the view rather than making the user wait for all data. Modern UI frameworks like React can efficiently handle this incremental approach.

For example, appending 100-row batches to a library view gives the perception of continuous loading, even when traversing hundreds of thousands of documents. Perceived performance increases dramatically when results stream in rather than bulk arriving at completion.

Caching Strategies to Optimize Performance

While recursive loading reduces requests for massive libraries, caching further optimizes throughput by avoiding round trips to the database. Popular in-memory caching technologies like Redis and Velocity provide microsecond key-value access that can mitigate database bottlenecks.

// Check cache first
var documents = cache.Get<List<Document>>(libraryId); 

if(documents == null)
{
  // Cache miss, load from SharePoint
  documents = GetDocuments(context, batchSize);
  
  // Store in cache    
  cache.Set(libraryId, documents);
}
return documents;

This cache-aside strategy only hits SharePoint when the cache expires, cutting repetitive queries. The cache expiration can be set based on tolerable staleness constraints. Async refreshes can also update the cache in the background before expiration.

For security, cached data should not include sensitive fields, instead storing identifiers like IDs and names for post-fetch access control. With personalized caching and encryption, performance can scale without compromising compliance and governance.

Conclusion

In summary, recursive divide-and-conquer retrieval, incremental population, and aggressive caching enable performant and scalable data access even for massive SharePoint document libraries. Organizations can leverage these techniques to deliver snappy experiences when browsing hundreds of thousands of files. By combining recursive calls, seamless UI updates, and fast in-memory storage, SharePoint libraries can scale to any size while providing speedy user experiences.