We launched QuestDB last summer [1, 2]. Our storage model is vector-based and append-only. This meant that all incoming data had to arrive in the correct time o

We launched QuestDB last summer [1, 2]. Our storage model is vector-based and append-only. This meant that all incoming data had to arrive in the correct time order. This worked well for some use cases but we increasingly saw real-world cases where data doesn't always land at the database in chronological order. We saw plenty of developers and users come and go specifically because of this technical limitation. So it became a priority to deal with out-of-order data. The big decision was which direction to take to tackle the problem. LSM trees seemed an obvious choice, but we chose an alternative route so we wouldn't lose the performance we spent years building. Our latest release supports out-of-order ingestion by re-ordering data on the fly. That's what this article is about. Also, we had many people asking about the differences between QuestDB and other open-source databases and why users should consider giving it a try instead of other systems. When we launched on HN, readers showed a lot of interest in side-by-side comparisons to other databases on the market. One suggestion [3] that we thought would be great to try out was to benchmark ingestion and query speeds using the Time Series Benchmark Suite (TSBS) [4] developed by TimescaleDB. We're super excited to share the results in the article. [1] https://news.ycombinator.com/item?id=23975807 https://news.ycombinator.com/item?id=23975807 [2] https://news.ycombinator.com/item?id=23616878 https://news.ycombinator.com/item?id=23616878 [3] https://news.ycombinator.com/item?id=23977183 https://news.ycombinator.com/item?id=23977183 [4] https://github.com/timescale/tsbs https://github.com/timescale/tsbs