There is also the horizontal partitioning across multiple machines which enables the solution to get beyond previous in-memory limitations. It’s optimized for various common data types and has different algorithms for different data types. I don’t know exactly how much of the query performance improvement comes from this, but I suspect it’s quite a bit. Architecturally, they put more memory in the cache – another performance improvement technique.

The column-wise ordering of data allows it to perform very high selective compression because all of a column’s values are physically together. It also provides for excellent performance when you select a small subset of the columns in a table since you do not perform I/O for data that is not needed. Column-orientation greatly assists a compression strategy due to the high potential for the existence of similar values in columns of adjacent rows in the table. It’s ideal for columnar functions like SUM, COUNT, MIN, MAX, and AVG.

So, the shoe fits here when you need a specialist data mart useful when the performance of limited-column queries, especially columnar functions, is the overriding selection factor.

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