How do document databases handle large binary data?

Document databases handle large binary data through two primary approaches: storing binaries directly within documents or using references to external storage systems. Most document databases like MongoDB or Couchbase support binary data types (e.g., BinData in BSON) that allow embedding smaller binaries (e.g., thumbnails, PDFs) directly into JSON/BSON documents. This works well for files under the database’s size limits (e.g., MongoDB’s default 16MB document limit). However, storing large binaries (videos, high-res images) directly can bloat documents, slowing queries and increasing storage costs. To avoid this, databases often provide mechanisms to split large binaries into chunks. For example, MongoDB’s GridFS specification automatically divides files into smaller parts (e.g., 255KB chunks) stored as separate documents, enabling efficient storage and retrieval without hitting document size limits.

For extremely large or frequently accessed binaries, document databases often integrate with external object storage services. Instead of storing the binary itself, the document holds a reference (e.g., a URL or file path) pointing to the data in systems like Amazon S3, Azure Blob Storage, or a distributed file system. This approach keeps the database lightweight and leverages optimized storage solutions for large files. For instance, a user profile document might include an avatar_url field pointing to an image in S3. This separation simplifies scalability, as object storage handles bandwidth-intensive operations, while the database manages structured metadata. Developers must ensure consistency between the database and external storage, often using transactions or cleanup processes to avoid orphaned files.

Document databases also optimize binary handling through features like compression, streaming, and metadata management. Compression reduces storage overhead, especially for formats like images or videos. Streaming APIs allow applications to read or write binaries in parts, avoiding memory overload. For example, GridFS enables parallel chunk downloads for faster access. Metadata (e.g., file type, size, checksum) is often stored alongside binaries or references, enabling queries like “find all documents with videos over 100MB.” While document databases offer flexibility, the choice between embedding, chunking, or external storage depends on use cases: small, frequently accessed files work well embedded, while large or static files benefit from external references. Proper indexing and caching (e.g., CDNs for external assets) further enhance performance.