What type of index can significantly improve the performance of complex Range-based queries?

A Partition Primary Index (PPI) is particularly effective for improving the performance of complex range-based queries because it organizes data in a way that allows Teradata to skip over partitions that are not relevant to the query. By dividing the table into multiple partitions based on a specific column or set of columns, the database can quickly access only the partitions that are needed based on the range specified in the query.

This can lead to significant reductions in the amount of data that needs to be scanned, which enhances performance, especially for queries that involve large datasets where only a subset of rows meets the range criteria. When Teradata can focus on relevant partitions, it reduces I/O and speeds up the execution time of the query.

The other types of indexes, while they have their own advantages, are not specifically geared towards optimizing range-based queries in the same way a PPI does. For instance, a Unique Primary Index (UPI) ensures that each row has a unique identifier but does not provide the same benefits for partitioning. Join Indexes (JI) are designed for simplifying joins and enhancing their performance, while Non-Unique Secondary Indexes (NUSI) can help with retrieval of rows based on non-primary key columns, but they do not partition