Delete and update operations in vector databases can lead to fragmented storage over time if not managed properly. When a vector is deleted, the database typically marks its storage space as unused rather than immediately freeing it. Similarly, updates often write a new version of the vector while invalidating the old one. This leaves gaps in the storage layout, increasing fragmentation. Over time, these gaps accumulate, causing the database to use more storage than needed for the active data. For example, if a 10GB dataset has 30% of its vectors deleted or replaced, the storage might still appear as 10GB until the gaps are addressed.
Many vector databases use compaction processes to reclaim unused space. Compaction reorganizes data by copying active vectors into a new contiguous storage block and discarding unused regions. For instance, Milvus implements automatic segment compaction: when segments (storage units) reach a threshold of deleted vectors, they are merged with adjacent segments, freeing space. Similarly, systems like RocksDB (used in some vector databases) employ background compaction to merge and clean up storage layers. This process reduces fragmentation and ensures efficient disk usage but can temporarily increase CPU and I/O load during reorganization.
Developers should check whether their database handles compaction automatically or requires manual configuration. For example, in Weaviate, deleted vectors are flagged as inactive, and a manual compaction API call is needed to reclaim space. In contrast, Qdrant automatically triggers compaction when certain thresholds are met. Understanding these mechanisms is critical for balancing storage efficiency with performance. Without compaction, storage costs grow unnecessarily, and query performance may degrade due to fragmented data access patterns.