Document databases typically handle large binary data through a combination of built-in features designed for storing and managing binary objects. A common approach is to use a concept called Binary Large Objects (BLOBs), where the database can store binary data directly within the document structure as a field. For instance, in a MongoDB document, you can use the BinData type to store files like images or videos as part of a document. This allows developers to group related data together, making it easier to retrieve both metadata and binary content in a single database call.
However, for very large files, storing data directly in the document might not be practical due to size limitations and performance concerns. In such cases, many document databases provide a separate storage mechanism, often referred to as “file storage” or “attachment storage.” For example, MongoDB has GridFS, which breaks large files into smaller chunks and stores them as individual documents. This design gives developers the ability to store files larger than the standard document size limit while still allowing for straightforward retrieval of those files using the original identifiers.
Managing the performance implications of binary data is important as well. Developers need to consider indexing strategies and caching mechanisms to ensure that accessing both document data and large binaries is efficient. Some document databases, like Couchbase, leverage built-in object storage capabilities to help with this, while others may require implementing additional infrastructure to optimize responsiveness and latency. By understanding these strategies, developers can effectively work with large binary data while ensuring their applications remain performant.