I didn't say they were the same, but instead was replying to the statement "RDBMSes typically can't do that, but document stores can.", but of course an RDBMS can store an "array of cities you've ever lived in".
I encourage you to think about the number of disk seeks required for recovering the full collection in both cases.
Ignoring XML, array types, or materialized views, for the RDBMS it is generally zero on virtually any modern platform, as the common case now is a database that is 100% in memory. A $79/m OVH server has 32GB. A low-end Dell server can have 256GB for $3000 (there are extremely few databases where the hot data surpasses even a GB). And the best thing about normalization is that it, like LZ, is a form of compression, yielding a more likely scenario that your database fits in memory.
That's all a side topic, however, and is neither here nor there on the long running NoSQL/SQL debate.
I didn't say they were the same, but instead was replying to the statement "RDBMSes typically can't do that, but document stores can.", but of course an RDBMS can store an "array of cities you've ever lived in".
I encourage you to think about the number of disk seeks required for recovering the full collection in both cases.
Ignoring XML, array types, or materialized views, for the RDBMS it is generally zero on virtually any modern platform, as the common case now is a database that is 100% in memory. A $79/m OVH server has 32GB. A low-end Dell server can have 256GB for $3000 (there are extremely few databases where the hot data surpasses even a GB). And the best thing about normalization is that it, like LZ, is a form of compression, yielding a more likely scenario that your database fits in memory.
That's all a side topic, however, and is neither here nor there on the long running NoSQL/SQL debate.