Dare Obasanjo had an excellent post When Not to Normalize your SQL Database wherein he helpfully provides a sample database schema for a generic social networking site.
Here's what it would look like if we designed it in the accepted normalized fashion: Normalization certainly delivers in terms of limiting duplication.
Their paths had crossed more than once in the past, as they had cooperated with bands such as The Fair Sex, 1am and Warm.
This made them eager to create their very own gestalt of electronic music: invigorating and contemplative, contemporary yet timeless.
select * from Users u inner join User Phone Numbers upn on u.user_id = upn.user_id inner join User Screen Names usn on u.user_id = usn.user_id inner join User Affiliations ua on u.user_id = ua.user_id inner join Affiliations a on a.affiliation_id = ua.affiliation_id inner join User Work History uwh on u.user_id = uwh.user_id inner join Affiliations wa on uwh.affiliation_id = wa.affiliation_id this isn't intended as a real query; it's only here to visually illustrate the fact that you need six joins -- or six individual queries, if that's your cup of tea -- to get all the information back about the user.) Those six joins aren't doing anything to help your system's performance, either.
Full-blown normalization isn't merely difficult to understand and hard to work with -- it can also be quite slow.
I thought that it could have been one of his smaller projects, but I was wrong.
venom in diagnostic tests, the umbrella terms “double sensitization” or “double positivity” cover patients with true clinical double allergy and those allergic to a single venom with asymptomatic sensitization to the other.
Of 635 patients, 351 (55.3%) were double sensitized to both venoms.
The overall re-exposure rate to Hymenoptera stings during and after immunotherapy was 62.4%; the relapse rate was 7.1% (6.0% in mono sensitized, 7.8% in double sensitized patients).
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One of the items we're struggling with now on Stack Overflow is how to maintain near-instantaneous performance levels in a relational database as the amount of data increases.