Redshift can apply specific and appropriate compression on each block increasing the amount of data being processed within the same disk and memory space. This means the data type within each block is always the same. A query selecting 5 columns out of 100 column table only has to access 5% of the data block space.Įach block of data contains values from a single column. This means the query performance is inversely correlated with the amount of data being accessed and the number of columns in a table does not factor into disk I/O cost. This presents multiple advantages for Redshift.ĭisk I/O is reduced significantly as only the necessary data are accessed. Without going into details, data is stored by columns rather than rows. The majority of analytical queries will utilise a small number of columns from a table for any aggregations. In addition to the architecture and design for query efficiency, the data itself is stored in a columnar format. One key feature of Redshift that influences the compute is the columnar storage of data. Colocate data and compute minimizing data transfer and increasing join efficiency across nodes.Distribute data and compute evenly across all compute nodes.

This system of slices and nodes achieves two objectives: Each slice stores multiple tables in 1MB blocks.

Redshift data types