浅谈时序数据库内核：如何用单机扛住亿级数据写入

版本	日期	备注
1.0	2021.10.19	文章首发

0. 背景

标题来源于InfluxDB对于它们的存储引擎诞生的背景介绍：

The workload of time series data is quite different from normal database workloads. There are a number of factors that conspire to make it very difficult to get it to scale and perform well:
- Billions of individual data points
- High write throughput
- High read throughput
- Large deletes to free up disk space
- Mostly an insert/append workload, very few updates

The first and most obvious problem is one of scale. In DevOps, for instance, you can collect hundreds of millions or billions of unique data points every day.

To prove out the numbers, let’s say we have 200 VMs or servers running, with each server collecting an average of 100 measurements every 10 seconds. Given there are 86,400 seconds in a day, a single measurement will gener