AlloyDB AI’s ScaNN index improves search on all kinds of data

Post-filtering means performing the vector search first, leveraging an Approximate Nearest Neighbors (ANN) vector index such as ScaNN on the text_embedding column to quickly identify a set of candidate results. Only after retrieving these initial candidates — the top 100 based on vector similarity — is the filter condition, WHERE color=”blue”, applied.

If your filter had high selectivity, there’s a risk this approach would yield very few candidates meeting your filter criteria. However because the condition WHERE color=”blue” has low selectivity, you would likely obtain the approximate top-100 results. In the unlikely case you do not retrieve 100 results, the vector search would need to perform additional scans on the vector index to retrieve more candidates until the desired limit was reached. While effective for filters with low selectivity, post-filtering can become less efficient with highly selective filters, as the vector index might need to scan through many non-matching candidates.

Medium selectivity

When a filter has medium selectivity, the AlloyDB query planner may choose to apply either a pre-filter or a post-filter. However in cases of medium selectivity that range from 0.5-10% selectivity (such as, say, the color “purple”), AlloyDB supports a method called inline filtering, or in-filtering. Inline filtering applies the filter conditions in tandem with the vector search. With in-line filtering, AlloyDB leverages a bitmap from a B-tree index to select candidates matching the filter condition in tandem with the vector search in one pass. 

So in this example, while the plan evaluates which candidates are purple, AlloyDB is simultaneously searching for the approximate neighbors of the search query against items in the data catalog. This approach balances the benefits of reducing the search space, as pre-filtering does, without the risk of returning too few results, a potential issue with post-filtering when combined with a highly selective filter. 

Adaptive filtration

While the cases detailed above seem to clearly partition the search space across three different kinds of filtering, in practice it’s not so simple. At times the query planner may misjudge the selectivity of a filter due to outdated statistics, resulting in the vector search and filtering conditions being applied in a suboptimal order, for less high-quality results. This is where AlloyDB ScaNN’s latest innovation, adaptive filtration, comes in. With adaptive filtration, AlloyDB learns the selectivity of your filters at query time based on actual observed statistics and can adaptively change its execution plan. This results in more optimal ordering of filters and vector search, and greatly mitigates cases of planner misestimations. 

In summary, real-world workloads are complex, and distinct filtering conditions have different selectivities that may change over time as your data and workloads grow. That’s where an intelligent database engine powering your vector search can make a difference — by optimizing and adapting filtering for your workload, helping to ensure consistently high-quality and performant search results as your data evolves.

Get started today

Get started with vector search leveraging AlloyDB’s ScaNN index today. Then, learn how you can use AlloyDB AI’s latest features to power multimodal vector search. Adaptive filtration is available in preview; get started by turning on the feature flag. 

You can also sign up for a 30-day AlloyDB free trial.