Capacity Planning for your Data Stores
Imagine a ticket sales website that does normal events like an M2M concert but also occasionally sells tickets to the very popular play Harry Potter and the Cursed Child. This is a perfect capacity planning example. Selling tickets requires that you never 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 you need a baseline for your regular traffic. The website must be able to handle increased load for extremely popular performances, but you don’t want to buy servers that aren’t doing anything for much of the time. (This is also why the cloud is so popular today.)
Colin Charles explores storage capacity planning for OLTP and data warehousing uses and explains how metrics collection helps you plan your requirements. Coupled with the elastic nature of clouds, you should never have an error establishing database connection. Along the way, Colin also covers tools such as Box Anemometer, innotop, the slow query log, Percona Toolkit (pt-query-digest), vmstat, Facebook’s Prophet, and Percona Monitoring and Management (PMM).