In this talk, we share our journey in making QuestDB, an Apache 2.0-licensed open-source time-series database, a significantly faster analytical database. Over the course of just one year, we achieved query performance gains of up to 6x by implementing specialised data structures, SIMD-based optimisations, scalable aggregation algorithms, and parallel execution pipelines.

QuestDB is designed for high-performance ingestion—processing millions of rows per second—and efficient queries over billions of rows. While it excelled in time-based queries, we found that certain generic analytical queries were slower than expected. In this session, we’ll walk through how we identified opportunities for improvement, the key changes we implemented, and how those changes delivered dramatic performance improvements in a relatively short timeframe.

We’ll demonstrate before-and-after queries to showcase the impact of these optimisations. All the code is freely available in QuestDB's GitHub repository for anyone to explore or contribute to.