Two common data partitioning methods (take Teradata as an example)

A basic principle for massive data performance optimization is "Partitioning" (also called "Partitioning ). The idea of partitioning is actually the principle of drawer in daily work and life: we put our items into multiple small draws according to some logic.

A basic principle for massive data performance optimization is "Partitioning" (also called "Partitioning ). The idea of partitioning is actually the principle of drawer in daily work and life: we put our items into multiple small draws according to some logic.

A basic principle for massive data performance optimization is "Partitioning" (also called "Partitioning ). Partitioning is actually the principle of drawer in daily work and life: we put our items into multiple small drawers according to some logic, which is generally better than mixing them in a large drawer; however, a small drawer is too large, or the logic is confusing, and the results may be counterproductive.

The partition Syntax of Teradata is relatively simple. Commonly Used partition by time. As long as you add it to the end of the create table statement, you can create a partition for the whole year of 2013.

Furthermore, the Hong Kong Space includes the following syntax elements:

My_field = 'A'

It can be changed to a format similar to this:

SUBSTR (my_field, 1, 1) IN ('E', 'F', 'G ')

In reality, in US space, because access data has changed from full table scan to partition scan, Hong Kong servers can achieve performance improvement of 10-times in some steps. For complex and time-consuming large jobs, the running time can always be shortened by more than half. It is very interesting that even experienced developers do not have a good grasp of Data partitions. The concept of data partitioning is to go beyond a specific database. In my nearly ten years of career, most performance problems can be solved through data partitioning.

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