99 | Memory bandwidth and vectorized processing

For data warehouse queries that need to scan over millions of rows, a big bottleneck is the bandwidth for getting data from disk into memory. However, that is not the only bottleneck. Developers of analytical databases also worry about efficiently using the bandwidth from main memory into the CPU cache, avoiding branch mispredictions and bubbles in the CPU instruction processing pipeline, and making use of single-instruction-multi-data (SIMD) instructions in modern CPUs. 
Besides reducing the volume of data that needs to be loaded from disk, columnoriented storage layouts are also good for making efficient use of CPU cycles. For example, the query engine can take a chunk of compressed column data that fits comfortably in the CPU’s L1 cache and iterate through it in a tight loop (that is, with no function calls). A CPU can execute such a loop much faster than code that requires a lot of function calls and conditions for each record that is processed. Column compression allows more rows from a column to fit in the same amount of L1 cache. Operators, such as the bitwise AND and OR described previously, can be designed to operate on such chunks of compressed column data directly. This technique is known as vectorized processing.