Dynamic Indexing: Switching algorithms on the fly
Dynamic indexing means changing the index type or parameters (e.g. from flat to IVF to HNSW) without a full application restart, so the system can adapt to data size, query pattern, or SLA changes.
Summary
- Dynamic indexing means changing the index type or parameters (e.g. flat → IVF → HNSW) without a full restart, so the system can adapt to data size, query pattern, or SLA changes. See index build time and real-time vs. offline.
- Use cases: data growth (trigger HNSW/IVF-PQ when dataset crosses a threshold); workload shift (rebuild with higher M/efConstruction, tune efSearch); A/B or tiering (different indexes per tenant or query type). Implementation: serve from current index while building new one; atomic switch; requires index persistence and versioning, control plane or admin API.
- Pipeline: build new index in background → validate → atomic switch. Practical tip: define a size or latency threshold to trigger a rebuild; keep the previous index as fallback until the new one is validated.
Use cases
Use cases: (1) Data growth—start with flat or a small IVF for tiny collections; when the dataset crosses a threshold, trigger a background build of HNSW or IVF-PQ and swap the index at a cutover. (2) Workload shift—if latency becomes critical, rebuild with higher M/efConstruction for better recall and then tune efSearch. (3) A/B or tiering—different indexes (e.g. fast approximate vs. high-recall) and route traffic per tenant or query type.
Implementation
Implementation: the VDB keeps the current index serving traffic while a new index is built from the same data (or from a snapshot). After build and validation, the system atomically switches reads (and optionally writes) to the new index; the old one can be dropped or kept as fallback. This requires index persistence and versioning, and often a control plane or admin API to trigger and monitor the switch.
Pipeline: build new index in background → validate → atomic switch. Practical tip: define a size or latency threshold to trigger a rebuild; keep the previous index as fallback until the new one is validated.
Frequently Asked Questions
What is dynamic indexing?
Changing the index type or parameters (e.g. from flat to IVF to HNSW) without a full application restart, so the system can adapt to data size, query pattern, or SLA changes. See real-time vs. offline indexing and index build time.
When is dynamic indexing useful?
Data growth: start with flat or small IVF; when the dataset crosses a threshold, trigger a background build of HNSW or IVF-PQ and swap. Workload shift: if latency becomes critical, rebuild with higher M/efConstruction and tune efSearch. A/B or tiering: different indexes (e.g. fast approximate vs. high-recall) per tenant or query type. See recall–latency trade-off.
How does the switch work?
The VDB keeps the current index serving traffic while a new index is built from the same data (or from a snapshot). After build and validation, the system atomically switches reads (and optionally writes) to the new index; the old one can be dropped or kept as fallback. Requires index persistence and versioning. See distributed index building.
What infrastructure is needed?
Index persistence and versioning; often a control plane or admin API to trigger and monitor the switch. See Kubernetes, HA, and streaming ingestion.