"Columnar databases aren’t new, so why are we building yet another one? We weren’t able to find one in open source that was optimized for time series."
This is the direction that QuestDB (www.questdb.io) has taken: columnar database, partitions by time and open source (apache 2.0). It is written is zero-GC java and c++, leveraging SIMD instructions. The live demo has been shown to HN recently, with sub second queries for 1.6 billion rows: https://news.ycombinator.com/item?id=23616878
There is more mature ClickHouse database [1]. This is column-based OLAP database, which provides outstanding query performance (it can scan billions of rows per second per CPU core) and outstanding on-disk data compression (up to 100x if proper table configs are used). ClickHouse also scales horizontally to multiple nodes. Comparing to QuestDB, ClickHouse consistently shows high performance on a wide range of query types from production. It also provides many SQL extensions optimized for analytical workloads.
BTW, VictoriaMetrics [2] is a time series database built on top of ClickHouse architecture ideas [3], so it inherits high performance and scalability from ClickHouse, while providing simpler configuration and operation for typical production time series workloads.
This is the direction that QuestDB (www.questdb.io) has taken: columnar database, partitions by time and open source (apache 2.0). It is written is zero-GC java and c++, leveraging SIMD instructions. The live demo has been shown to HN recently, with sub second queries for 1.6 billion rows: https://news.ycombinator.com/item?id=23616878
NB: I am a co-founder of questdb.