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Comment by curiousDog | Hacker News Reader

Still don't understand why businesses buy this instead of scaling-out. Cost? Complexity?

I'm not saying that Oracle hardware or software is the solution, but "scaling-out" is incredibly difficult in transaction processing. I worked at a mid-size tech company with what I imagine was a fairly typical workload, and we spent a ton of money on database hardware because it would have been either incredibly complicated or slow to maintain data integrity across multiple machines.

I imagine that situation is fairly common.

Generally it's just that it's really difficult to do it right. Sometime's it's impossible. It's often loads more work (which can be hard to debug). Furthermore, it's frequently not even an advantage. Have a read of https://research.microsoft.com/pubs/163083/hotcbp12%20final.... Remember corporate workloads frequently have very different requirements than consumer.

Both, I suspect. When you consider the cost of engineer time needed to create high performance scale-out systems in a problem domain that requires consistency, scale-up can start to look fairly attractive.

Oracle is incredibly well crafted for managing a transactional database in an enterprise setting. If it's $200k more and the option is to take a massive risk what would you do ?

At some point the massive risk will become worth taking; this has happened for ETL and is on the way to happening for BI; there is a skills / tools gap but that will change.

The third and most important reason:

They have someone to blame when shit goes tits up. Response time may still be abysmal, but "dammit we payed a million dollars for this!"

and one more thing with large volume transaction processing is scaling out on I/O and ETL jobs.Oracle (and other DB vendors) offer a lot of utilities that scale out perfectly with least of development time. For e.g. it would rather be a difficult deal to work with parsers to parse a Gigabit-data file with code we have to own up , than to use features these DBs directly offer to parallel-load into a ready to use table within minutes.

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