What type of index can be created to optimize complex queries without guaranteeing uniform distribution of rows?

A Join Index is designed specifically to improve performance for complex queries, particularly those involving joins and aggregations. By storing pre-joined results in a structured format, a Join Index allows Teradata to access data more efficiently, reducing the need for dynamic joins at query run time. This can significantly enhance query response times, especially for complex retrievals that involve multiple tables.

While creating a Join Index, it's essential to understand that it does not ensure uniform distribution of rows, as it is largely based on the specific join conditions and the data involved. The effectiveness of a Join Index is contingent upon the particular queries it is designed to optimize, rather than the distribution of the indexed data across the underlying tables.

In contrast, other types of indexes like the Primary Index, Unique Secondary Index, and Non-Unique Primary Index (NUPI) have distinct characteristics related to uniqueness and distribution:

• A Primary Index (PI) typically ensures a unique distribution of rows, which facilitates efficient data retrieval but may not cater to complex join scenarios as effectively as a Join Index.

• A Unique Secondary Index (USI) is primarily used for ensuring unique values in a column, which can improve access time for queries based on that column but does not necessarily address complex join performance.