Storing raw vectors, compressed representations, or references to vectors involves trade-offs between retrieval speed and storage efficiency. Raw vectors prioritize fast retrieval but require significant storage, while compressed or referenced data reduces storage costs at the expense of added latency or complexity during retrieval. The choice depends on whether the system prioritizes speed, storage savings, or a balance of both.
Raw Vectors provide the fastest retrieval speed because the data is immediately available without decompression or external lookups. For example, a 1024-dimensional vector stored as 32-bit floats occupies 4KB of memory. This direct storage allows algorithms like k-nearest neighbors (k-NN) to compute distances in real time. However, raw vectors consume substantial storage, especially at scale: 1 million vectors would require ~4GB of memory. Systems with strict latency requirements, such as real-time recommendation engines, often use raw vectors to avoid decompression overhead.
Compressed Representations (e.g., using product quantization or binary hashing) reduce storage by encoding vectors into smaller formats. A 1024-dimensional vector compressed via product quantization might shrink from 4KB to 32 bytes, cutting storage by ~99%. However, retrieval speed depends on the compression method. Lossy techniques introduce approximation errors, requiring post-processing to refine results, while decompression adds computational steps. For instance, searching compressed vectors in FAISS (a common vector database) trades slight accuracy loss for faster searches compared to brute-force methods. Compression is ideal when storage costs outweigh minor latency increases, such as in large-scale image retrieval systems.
References to Vectors (e.g., UUIDs pointing to external storage) minimize local storage by offloading vector data to databases or distributed systems. This approach saves memory but introduces retrieval latency from network calls or disk I/O. For example, a system storing references in Redis might fetch vectors from an object store like S3, adding milliseconds to each query. Reliability also becomes a concern: network delays or outages directly impact performance. References work best when vectors are infrequently accessed or when storage constraints are extreme, such as edge devices with limited memory. However, caching referenced vectors locally can mitigate latency, blending storage savings with acceptable retrieval speed.