HashiCorp 內 scale 的方法

去日本前在 Hacker News 上看到「Squeeze the hell out of the system you have」這篇,用作者的名字翻了一下 LinkedIn,看起來講的是 HashiCorpSRE 事情:「Dan Slimmon」。

看的時候可以注意一下,文章裡面的觀點未必要認同,大多是他自己的看法或是想法,但裡面提到很多發生的事情,可以知道 HashiCorp 內目前搞了什麼東西。

從 LinkedIn 的資料可以看到他從 2019 就加入 HashiCorp 了,所以文章一開頭這邊講的同事應該就是 HashiCorp 的同事:

About a year ago, I raised a red flag with colleagues and managers about Postgres performance.

往下看可以看到他們有遇到 PostgreSQL 的效能問題,然後每次都是以 scale up (加大機器) 的方式解決,考慮到 HashiCorp 的產品線,我會猜應該是 Terraform Cloud 這個產品線遇到的狀況。

然後在後面提到的解法則是提到了 codebase 是 Rails,他們花了三個月的時候不斷的重複 profiling + optimizing,包括 SQL 與 PostgreSQL 的設定:

Two engineers (me and my colleague Ted – but mostly Ted) spent about 3 months working primarily on database performance issues. There was no silver bullet. We used our telemetry to identify heavy queries, dug into the (Rails) codebase to understand where they were coming from, and optimized or eliminated them. We also tuned a lot of Postgres settings.

另外一組人則是弄了 read-only replication server,把 loading 拆出去:

Two more engineers cut a path through the codebase to run certain expensive read-only queries on a replica DB. This effort bore fruit around the same time as (1), when we offloaded our single most frequent query (a SELECT triggered by polling web clients).

這兩個方法大幅降低了資料庫的 peak loading,從 90% 降到 30%:

These two efforts together reduced the maximum weekly CPU usage on the database from 90% to 30%.

可以看到都還沒用到 sharding 的技巧,目前硬體的暴力程度可以撐很久 (而且看起來是在沒有投入太多資源在 DB-related tuning 上面),快撞到的時候也還可以先用 $$ 換效能,然後投入人力開始 profiling 找問題...

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