denormalization

What are the best practices for database design and normalization for high traffic websites like stackoverflow? Should one use a normalized database for record keeping or a normalized technique or a combination of both? Is it sensible to design a normalized database as the main database for record keeping to reduce redundancy and at the same time maintain another denormalized form of the database for fast searching? or Should the main database be denormalized but with normalized views at the application level for fast database operations? or some other approach? The performance hit of joining

I just read an article on Microservices and PaaS Architecture . In that article, about a third of the way down, the author states (under Denormalize like Crazy ): Refactor database schemas, and de-normalize everything, to allow complete separation and partitioning of data. That is, do not use underlying tables that serve multiple microservices. There should be no sharing of underlying tables that span multiple microservices, and no sharing of data. Instead, if several services need access to the same data, it should be shared via a service API (such as a published REST or a message service

According to my understanding - and correct me if I'm wrong - "Normalization" is the process of removing the redundant data from the database-desing However, when I was trying to learn about database optimizing/tuning for performance, I encountered that Mr. Rick James recommend against normalizing continuous values such as (INTS, FLOATS, DATETIME, ...) "Normalize, but don't over-normalize." In particular, do not normalize datetimes or floats or other "continuous" values. source Sure purists say normalize time. That is a big mistake. Generally, "continuous" values should not be normalized

I normalized data with the minimum and maximum with this R code: normalize <- function(x) { return ((x - min(x)) / (max(x) - min(x))) } mydata <- as.data.frame(lapply(mydata , normalize)) How can I denormalize the data ? Essentially, you just have to reverse the arithmetic: x1 = (x0-min)/(max-min) implies that x0 = x1*(max-min) + min . However, if you're overwriting your data, you'd better have stored the min and max values before you normalized, otherwise (as pointed out by @MrFlick in the comments) you're doomed. Set up data: dd <- data.frame(x=1:5,y=6:10) Normalize: normalize <- function(x)

I am using Ruby on Rails 3.0.7 and MySQL 5. In my application I have two database tables, say TABLE1 and TABLE2, and for performance reasons I have denormalizated some data in TABLE2 so that I have repeated values of TABLE1 in that one. Now, in TABLE1 I need to update some of those involved values and, of course, I must update properly also denormalized values in TABLE2. What I can do to update those values in a performant way? That is, if TABLE2 contains a lot of values (1.000.000 or more), what is the best way to keep update both tables (techniques, pratices, ...)? What can happen during the

Based on the information I have provided below, can you give me your opinion on whether its a good idea to denormalize separate tables into one table which holds different types of contracts?.. What are the pro's/con's?.. Has anyone attempted this before?.. Banking systems use a CIF (Customer Information File) [master] where customers may have different types of accounts, CD's, mortgages, etc. and use transaction codes[types] but do they store them in one table? I have separate tables for Loans, Purchases & Sales transactions. Rows from each of these tables are joined to their corresponding

We're about to run side-by-side testing to compare a legacy system with a new shiny version. We have an Oracle database table, A, that stores data for the legacy system, and an equivalent table, B, that stores data for the new system, so for the duration of the test, the database is denormalized. (Also, the legacy system and table A are fixed - no changes allowed) What I want to do is to allow the infrequent DML operations on A to propagate to B, and vice-versa. I started with a pair of triggers to do this, but hit the obvious problem that when the triggers run, the tables are mutating, and an