ZFS 0.8.6-1 is not bleeding edge, there have been more than 1700 commits since and after 0.8.6, the ZFS release number jumped to 2.0. The big addition included in the 2.0 release is native encryption.
機器是在雲端上 (Azure 上)，不熟悉 Azure 的機種，但看記憶體與 CPU 的量好像不是用頂規的機器：
benchmark host
Standard D2ds_v4 instance
2 vCpu, 8GB of Ram and 75 GB of temporary storage
Debian Buster
Database host
Standard E4-2ds-v4 instance
2 vCpu, 32GB of Ram and 150GB of temporary storage
256GB SSD Premium (SSD Premium LRS P15 – 1100 IOPS (3500 burst), 125 MB/s)
Debian Buster
Percona server 8.0.22-13
Unfortunately, a similar solution does not work with Percona XtraDB Cluster 8.0.x, due to the modified way wsrep positions are kept in the storage engine, hence the trick with updating grastate.dat does not work as expected there.
看起來是 2019 年年初的時候 MySQL 5.1 出問題，後續決定安排升級，在 2019 年年中把系統升級到 MySQL 5.7 (Percona Server 版本)：
Our first major hurdle was to get current with our version of MySQL. In July, 2019 we completed the MySQL 5.1 to MySQL 5.7 (v5.7.19-17-log Percona Server to be precise) upgrade across all MySQL instances.
Not only was support for MySQL 5.1 at End-of-Life (more than 5 years ago) but our MySQL 5.1 instances on EC2/AWS had limited storage and we were scheduled to run out of space at the end of July. Our backs were up against the wall and we had to deliver!
另外在升級到 5.7 的時候，順便把本來是 INT 的 primary key 都換成 BIGINT：
As part of the cut-over to MySQL 5.7, we also took the opportunity to bake in a number of improvements. We converted all primary key columns from INT to BIGINT to prevent hitting MAX value.
In parallel with the MySQL 5.7 upgrade we also Upgraded Django to 1.6 due a behavioral change in MySQL 5.7 related to how transactions/commits were handled for SELECT statements. This behavior change was resulting in errors with older version of Python/Django running on MySQL 5.7
Eventbrite had traditionally used pt-online-schema-change (pt-osc) to ALTER MySQL tables in production. pt-osc uses MySQL triggers to move data from the original to the “duplicate” table which is a very expensive operation and can cause replication lag. Matter of fact, it had directly resulted in several outages in H1 of 2019 due to replication lag or breakage.
Next on the list was implementing improvements to MySQL high availability and automatic failover using Orchestrator. In February of 2020 we implemented a new HAProxy layer in front of all DB clusters and we released Orchestrator to production!
Orchestrator can successfully detect the primary failure and promote a new primary. The goal was to implement Orchestrator with HAProxy first and then eventually move to Orchestrator with ProxySQL.
然後最後題到了 Square 研發的 Shift，把 gh-ost 包裝起來變成有個 web UI 可以操作：
As with any database, fitting your data into RAM will allow for faster reads than from disk. MongoDB is no different. Knowing how much data MongoDB has to read in for your queries can help you determine how much RAM you should allocate to your database.
這樣的設計邏輯很奇怪啊，你不要扯其他 database 啊，你們家主力的 InnoDB 一直都沒有推薦要 Working Set < RAM 啊，反過來才是用 InnoDB 的常態吧，而且在 PostgreSQL 上也是這樣吧 XDDD
Another key thing we found was that even in the best-case with Kube-Router we see an approximate 13% decrease in database performance comparing bare metal to running within Kubernetes. This illustrates that there are still improvements to be made to the performance of container networking in Kubernetes.
