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TFA talks about the fact that SQL simply doesn't scale to volume as big as the ones that companies like Amazon need to deal with. It's not the first time I've

TFA talks about the fact that SQL simply doesn't scale to volume as big as the ones that companies like Amazon need to deal with. It's not the first time I've read this: Google also has similar problems and fixed it with a DB (or DBs ?) of their own. In Google's case, AFAICT, it's also a gigantic key/value store on which monstrous map/reduce are done. My question is simple: up to which amount of data and for which use cases do SQL still work? In other words, at which point do you need something else than SQL simply because SQL ain't cutting it anymore?

EwanToo 14y ago
You can run SQL on TB's of data if you throw fast enough nodes at it, it's not purely a data size issue, but more of a scale-out issue. Things like joins and foreign key constraints can be very slow on widely distributed relational databases, so you end up dropping more and more functionality from your SQL to keep the performance to a maximum. Eventually you pull out so much of the functionality from your relational database, that you end up running individual very narrow queries, and very short transactions, that you more closely mirror a key/value store than a traditional RDBMS like Oracle. Of course, when you're someone like Amazon, then TB's of data is considered fairly small.