Delete operations and updates in vector databases impact storage usage by creating gaps or outdated data that isn’t immediately removed. When you delete a vector, many databases don’t instantly free up the physical storage space. Instead, they mark the vector as invalid or tombstone it, leaving the original data in place until a maintenance process cleans it up. Similarly, updating a vector often involves writing a new version while leaving the old one in storage until it’s cleared. Over time, these operations can fragment storage and increase unused space, especially in systems optimized for write-heavy workloads. Without intervention, this can lead to inefficient storage use and slower query performance as the database scans more data blocks.
To address this, many vector databases use compaction processes to reclaim space. Compaction consolidates fragmented data by rewriting valid vectors into new storage blocks and discarding deleted or outdated ones. For example, databases like Apache Cassandra or time-series systems apply similar logic: they merge smaller data files into larger ones, eliminating redundant or obsolete entries. In vector databases like Milvus or Pinecone, compaction might run automatically in the background or be triggered manually. During compaction, the system rebuilds indexes if necessary, ensuring that queries remain efficient. This process reduces storage overhead and improves read performance but can temporarily increase CPU and I/O usage while it runs. The specifics depend on the database’s design—some prioritize immediate space reclamation, while others batch operations for efficiency.
The exact behavior varies by implementation. For instance, FAISS (a library for vector search) doesn’t natively handle deletions, so developers often layer a separate system to track invalid vectors and exclude them during searches. In contrast, databases like Weaviate or Qdrant handle deletions and updates internally, using strategies like versioning or copy-on-write to manage changes. Developers should check their database’s documentation to understand how storage reclamation works. If compaction isn’t automatic, manual cleanup may be required to prevent storage bloat. For example, in Elasticsearch’s vector search features, optimizing indices manually forces a compaction-like process. Understanding these mechanisms is critical for maintaining performance and cost efficiency, especially in large-scale applications where storage costs and query latency matter.