提高Pinterest上Goku时间序列数据库的效率（第2部分）

Monil Mukesh Sanghavi | Software Engineer, Real Time Analytics Team; Xiao Li | Software Engineer, Real Time Analytics Team; Ming-May Hu | Software Engineer, Real Time Analytics Team; Zhenxiao Luo | Software Engineer, Real Time Analytics Team; Kapil Bajaj | Manager, Real Time Analytics Team

man’s hands holding a stopwatch

At Pinterest, one of the pillars of the observability stack provides internal engineering teams (our users) the opportunity to monitor their services using metrics data and set up alerting on it. Goku is our in-house time series database providing cost efficient and low latency storage for metrics data. Underneath, Goku is not a single cluster but a collection of sub-service components including:

在Pinterest，可观察性堆栈的支柱之一为内部工程团队（我们的用户）提供了使用指标数据监控其服务并设置警报的机会。Goku是我们内部的时间序列数据库，为指标数据提供了成本效益高且延迟低的存储。在底层，Goku不是一个单一的集群，而是由多个子服务组件组成，包括：

Goku Short Term (in-memory storage for the last 24 hours of data, referred to as GokuS)
Goku Short Term（内存中存储最近 24 小时数据的存储，称为 GokuS）
Goku Long Term (ssd and hdd based storage for older data, referred to as GokuL)
Goku长期存储（用于存储较旧数据的SSD和HDD，称为GokuL）
Goku Compactor (time series data aggregation and conversion engine)
Goku Compactor（时间序列数据聚合和转换引擎）
Goku Root (smart query routing)
Goku Root（智能查询路由）

You can read more about these components in the blog posts on GokuS Storage, GokuL (long term) storage, and Cost Savings on Goku, but a lot has changed in Goku since those were written. We have implemented multiple features that increased the efficiency of Goku and improved the user experience. In this 3 part blog post series, we will cover the efficiency improvements in 3 major aspects:

您可以在GokuS存储、GokuL（长期）存储和Goku的成本节约的博客文章中了解更多关于这些组件的信息，但自那些文章以来，Goku发生了很多变化。我们已经实现了多个功能，提高了Goku的效率并改善了用户体验。在这个由3部分组成的博客系列中，我们将涵盖3个主要方面的效率改进：

We’ll also share some learnings and takeaways from using Goku for storing metrics at Pinterest.

我们还将分享在Pinterest存储指标时使用Goku的一些经验和心得。

This 2nd blog post focuses o...