> It turns out you can accomplish quite a lot with 4,709 lines of Go code! How about a full time-series database implementation, robust enough to be run in production for a year where it stored 2.1 trillion data points, and supporting 119M queries per second (53M inserts per second) in a four-node cluster? Statistical queries over the data complete in 100-250 ms while summarizing up to 4 billion points. It’s pretty space-efficient too, with a 2.9x compression ratio. At the heart of these impressive results, is a data structure supporting a novel abstraction for time-series data: a time partitioning, copy-on-write, version-annotated, k-ary tree.
> It turns out you can accomplish quite a lot with 4,709 lines of Go code! How about a full time-series database implementation, robust enough to be run in production for a year where it stored 2.1 trillion data points, and supporting 119M queries per second (53M inserts per second) in a four-node cluster? Statistical queries over the data complete in 100-250 ms while summarizing up to 4 billion points. It’s pretty space-efficient too, with a 2.9x compression ratio. At the heart of these impressive results, is a data structure supporting a novel abstraction for time-series data: a time partitioning, copy-on-write, version-annotated, k-ary tree.