K-nearest neighbor is a proximity algorithm to find data in order of distance. Typically, this data cannot be indexed in advance, as both the centroid and the data can be in constant motion.
PostgreSQL provides efficient searching algorithms for finding proximity data on the fly, including unique, high-performance indexing options.
For a deeper understanding of how K-nearest neighbor (KNN) works in PostgreSQL, 2ndQuadrant hosted a live webinar, “KNN Indexing in PostgreSQL” — presented by Kirk Roybal (Principal Consultant at 2ndQuadrant).
The following topics were covered:
- What is K-nearest neighbor?
- What can I do with it?
- What kinds of data can I search for?
- How expensive is it to the query planner?
- How do I minimize that cost?
Those who weren’t able to attend the live webinar can now view the recording here.
To stay updated on upcoming webinars by 2ndQuadrant, you can visit our Webinars page.
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