Efficiently updating materialized views dblp

An incremental refresh eliminates the need to rebuild materialized views from scratch.

The complete refresh involves executing the query that defines the materialized view.

This process can be slow, especially if the database must read and process huge amounts of data.

In other words, fresh information is constantly made available in the form of of new data and updates.

Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation.

This chapter discusses how to refresh materialized views, which is a key element in maintaining good performance and consistent data when working with materialized views in a data warehousing environment.

This chapter includes the following sections: The database maintains data in materialized views by refreshing them after changes to the base tables.

If insufficient temporary space is available to rebuild the indexes, then you must explicitly drop each index or mark it About Types of Refresh for Materialized Views The refresh method can be incremental or a complete refresh.

There are two incremental refresh methods, known as log-based refresh and partition change tracking (PCT) refresh. Users can perform a complete refresh at any time after the materialized view is created.

Performing a refresh operation requires temporary space to rebuild the indexes and can require additional space for performing the refresh operation itself.

Some sites might prefer not to refresh all of their materialized views at the same time: as soon as some underlying detail data has been updated, all materialized views using this data become stale.

The PCT refresh method can be used if the modified base tables are partitioned and the modified base table partitions can be used to identify the affected partitions or portions of data in the materialized view.

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