Capacity Planning for your Data Stores
In this webinar, Colin uses a ticket sales website that does “normal” events like an M2M concert, but also occasionally also sells tickets to the Harry Potter theatre show.
Selling tickets requires that you don’t sell more tickets than you actually have. You want to load balance your queries. You want to shard your data stores. You may want to split reads and writes. You need to determine where the system bottlenecks, so for that you need a baseline and know what regular traffic patterns are.
This is a perfect capacity planning example, because you don’t want buy servers that do nothing for much of the time. Examples like this are why the cloud is so popular today. While the focus of this talk is not to help plan for the application server loads and caches, the data layer is definitely a hard thing to tackle.
Beyond that, we will talk about storage capacity planning for OLTP and data warehousing uses.
From metrics collection, you can plan your requirements. Couple this with the elastic nature of clouds, and you should never have an “error establishing database connection”.
The tools covered in this talk include (but are not limited to): Box Anemometer, innotop, the slow query log, Percona Toolkit (pt-query-digest), vmstat, Facebook’s Prophet and Percona Monitoring and Management (PMM).