Document databases handle data compression through a combination of general-purpose algorithms and schema-aware optimizations tailored to their flexible data structures. These systems typically compress data at rest to reduce storage costs and improve read/write performance. Common techniques include using algorithms like Snappy or zlib for per-document or block-level compression, as well as structural optimizations like field name deduplication. For example, MongoDB’s WiredTiger storage engine compresses entire data blocks (containing multiple documents) using Snappy by default, trading minimal CPU overhead for moderate space savings. Developers can also opt for zlib, which provides higher compression ratios at the cost of increased computational effort.
Specific implementations vary across databases but often leverage the repetitive nature of document structures. Since documents in a collection often share similar field names (e.g., username, email), databases like Couchbase use dictionary encoding to replace these strings with shorter tokens. This reduces redundancy without altering the data itself. Additionally, some systems optimize binary representations: MongoDB’s BSON format encodes integers and dates more efficiently than plain JSON, while Amazon DocumentDB applies compression to both data and indexes. These optimizations are particularly effective for large datasets with homogenous document structures, where patterns like repeated nested objects or arrays can be compressed more effectively.
Developers configuring compression must balance performance, storage efficiency, and hardware constraints. For instance, Snappy’s faster compression/decompression suits real-time applications, while zlib is better for archival data. Some databases also support tiered compression, where older data is compressed more aggressively. However, excessive compression can increase CPU usage and latency, especially for write-heavy workloads. Tools like MongoDB’s compact command allow manual tuning, letting developers reclaim space or adjust compression settings post-deployment. Understanding these trade-offs ensures optimal resource usage without compromising application requirements.