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Catbench Vector Search Demo Has Postgres SQL Throughput, Latency Monitoring Now
CatBench is a Postgres + PgVector demo application that uses 25k pet photos from a Kaggle dataset for demonstrating how the similarity search features work together with the rest of your application code & schema in the backend (HammerDB Order Entry). The latest version of CatBench has Postgres instance-wide throughput and average query execution latency charts built in now. Previously you were able to navigate around the recommendation engine for Cat Purchases UI, click on cat photos and find product recommendations, based on what other similar cats had purchased. - Linux, Oracle, SQL performance tuning and troubleshooting - consulting & training.
The latest version of CatBench has Postgres instance-wide throughput and average query execution latency charts built in now. Here’s a screenshot from a test where I gradually increased the number of cat & dog similarity search query loops from 4 to 16 on a freshly restarted Postgres instance (and the OS pagecache had been cleared too): This also enables the next step: Add a “recall” quality monitoring for the “cat fraud detection” reverse lookup queries - this allows us to start playing with different vector index types and build configuration settings and have nice performance and recall quality charts to compare.
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