PolyScale.ai – PolyScale.ai – Matt Calder

Matt Calder

Matt focuses on optimizing the caching algorithms and creating AI solutions for PolyScale.

One Hit Expectation

Calling attention to an interesting paper with small contribution.

PolyScale a Bigger Boat for Pgvector

A look at pgvector and how PolyScale can improve query performance.

Approaching Cache Invalidation with PolyScale.ai

A deep dive into the complexities of cache invalidation and how PolyScale.ai approaches these problems.

PolyScale Metrics with ClickHouse Materialized Views

The PolyScale Observability Interface visualizes and summarizes statistics on query traffic, cache performance, and database performance. Those statistics are based on a massive amount of metrics data. Accessing that data efficiently is achieved with the use of ClickHouse materialized views.

PolyScale Database Observability Walkthrough

PolyScale caching improves user experiences by reducing latency and improves data throughput by reducing database server loads. In addition, through its Observability interface, PolyScale improves developer’s situational awareness. Awareness of the structure of the database load, and how the cache is performing.

PolyScale AI - Closing the Configuration Loop

In this article the details of how PolyScale's cache is configured are described. The parameters of this configuration are then mapped to the KPIs of cache performance using a simple but powerful statistical model.