Making 768 servers look like 1
This technical guide explains the necessity of database sharding for scaling applications that handle millions of queries. It details the progression from single-node databases to complex architectures spanning hundreds of servers to overcome CPU and I/O bottlenecks.
Why it matters
Understanding database scalability is critical for engineers building high-traffic digital infrastructure and modern web applications.
Ben Dicken [ @ BenjDicken ] | July 15, 2026
To some, that looks like a lot of computers. To those managing the infrastructure for apps with millions of customers, executing millions of queries per second, pretty normal. Products at this scale frequently require thousands of servers working in unison.
The most difficult infrastructure component to scale is almost always the database. A single database server cannot handle such demand, so we must spread the queries and data out across many servers with database sharding .
Database sharding is the best way to scale a Postgres or MySQL database for anything beyond a few terabytes of data. Let's look at how we go from a small single-node database, to one with a few terabytes spread across four shards, all the way up to one that is sharded across 768 servers and storing a petabyte of data.
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in