> To make everything scale, it's super distributed, but having everything so distributed has a huge cost
Its more then a huge cost, its often insane... We are not talking 10x but easily 100x to a 1000x. Its like when i see some known database makers that scale, write how they can do a million writes per second. Ignoring that they rented a 1000 servers for that, each costing $500 per month. That same software is also 10x to 100x more slower, then a single postgresql database in reads.
So you ask yourself, how many companies do a million writes per second. Few ... How much of those writes may have been reduced by using smarter caching / batching? Probably a factor of 10 to 100x...
The thing i like about scalable solution, is that its way easier to just add a few nodes, vs needing to deal with postgres replication / master setup, when the master node got moved / need to upgraded.
For fun, i wrote my own ART database, and a LSM database using LLMs ... Things do 400 a 500k inserts / second on basic cheap hardware. So wait, why are some companies advertising that they do a million inserts/s, on 500k/month hardware? Some companies may need this ability to scale, as they will not run a 1000 server but maybe 10.000, or more. But 99% of the companies will never even smell close to a 100k inserts/second, let alone a million.
People forget that network latency is a huge thing, but the moment you want consistency and need something like raft, that means now your doing not just 1x the network latency of a write but 4x (send write, verify receive, send commit, verify commit, confirm).
Even something as basic like sqlite vs postgres on the same server, can mean a difference of 3x performance, simply because of then network overhead vs in-function. And that network overhead is just local on the same machine.
Its more then a huge cost, its often insane... We are not talking 10x but easily 100x to a 1000x. Its like when i see some known database makers that scale, write how they can do a million writes per second. Ignoring that they rented a 1000 servers for that, each costing $500 per month. That same software is also 10x to 100x more slower, then a single postgresql database in reads.
So you ask yourself, how many companies do a million writes per second. Few ... How much of those writes may have been reduced by using smarter caching / batching? Probably a factor of 10 to 100x...
The thing i like about scalable solution, is that its way easier to just add a few nodes, vs needing to deal with postgres replication / master setup, when the master node got moved / need to upgraded.
For fun, i wrote my own ART database, and a LSM database using LLMs ... Things do 400 a 500k inserts / second on basic cheap hardware. So wait, why are some companies advertising that they do a million inserts/s, on 500k/month hardware? Some companies may need this ability to scale, as they will not run a 1000 server but maybe 10.000, or more. But 99% of the companies will never even smell close to a 100k inserts/second, let alone a million.
People forget that network latency is a huge thing, but the moment you want consistency and need something like raft, that means now your doing not just 1x the network latency of a write but 4x (send write, verify receive, send commit, verify commit, confirm).
Even something as basic like sqlite vs postgres on the same server, can mean a difference of 3x performance, simply because of then network overhead vs in-function. And that network overhead is just local on the same machine.