Unlike a simple VIEW the result of a Materialized View is stored somewhere, generally in a table.

Materialized Views are used when immediate response is needed and the query where the Materialized View bases on would take to long to produce a result.

A materialized view takes a different approach: the query result is cached as a concrete ("materialized") table (rather than a view as such) that may be updated from the original base tables from time to time.

Secondary indexes are suited for low cardinality data.

Queries of high cardinality columns on secondary indexes require Cassandra to access all nodes in a cluster, causing high read latency.

This paper presents the algorithm to incrementally update the materialized views with inner join, focusing on one with aggregate functions, and building of a program that automatically generates codes in PL/pg SQL for triggers, which can undertake synchronous incremental updates of the materialized views in Postgre SQL.

refreshes where only the individual materialized view rows affected by base table changes are updated. Earlier in this tutorial we saw how the rowids for row in a materialized view changed after a complete refresh.

The process of setting up a materialized view is sometimes called materialization.) In any database management system following the relational model, a view is a virtual table representing the result of a database query.

Whenever a query or an update addresses an ordinary view's virtual table, the DBMS converts these into queries or updates against the underlying base tables.

This technology can significantly improve the performance of database systems.

Although the idea of materialized views came up in the 1980s, only three database management systems, i.e.

Materialized views are suited for high cardinality data.