tl;dr version: his recommendation algorithm stored an amount of data that was Θ(n^2) + Θ(n*m) for # of users, beers. He optimized that to put a constant limit on the storage space used.
The clearest takeaway is: if you want to reduce the disk or memory footprint of your DB, you need to figure out what tables/rows/columns are consuming the lion's share of the space (there's almost always one column that's way worse than the rest) and figure out how to change your app logic to store much less data in that column.
The OP's lessons learned apply no matter which DB you're using!
Comments
tl;dr version: his recommendation algorithm stored an amount of data that was Θ(n^2) + Θ(n*m) for # of users, beers. He optimized that to put a constant limit on the storage space used.
The clearest takeaway is: if you want to reduce the disk or memory footprint of your DB, you need to figure out what tables/rows/columns are consuming the lion's share of the space (there's almost always one column that's way worse than the rest) and figure out how to change your app logic to store much less data in that column.
The OP's lessons learned apply no matter which DB you're using!