Before delving into materialized views, let's quickly clarify what a standard view is:

• A view is a named SQL query that is stored in the database's metadata.
• It does not store any data itself. It's a "virtual table" whose content is derived on-the-fly.
• When you query a standard view, the DBMS effectively substitutes the view's definition (its underlying query) into your query and then executes the combined query. This means the underlying query is re-executed every single time the view is accessed.
• Primary Uses: Simplifying complex queries, enforcing security (by restricting access to certain rows/columns), and providing data abstraction.

A materialized view (often abbreviated as MV) takes the concept of a view a step further by physically storing the result of its defining query on disk, much like a regular table.

• When a materialized view is created, the DBMS executes its underlying query and then saves the complete result set as a table-like object in the database's storage.
• When you query a materialized view, the DBMS simply retrieves the pre-computed data from this stored result, rather than having to re-execute the potentially complex underlying query every time.

The primary reason to use materialized views is to achieve significant performance gains for specific types of queries:

1. Accelerating Complex Queries (Aggregations and Joins):
2. Queries that involve numerous joins (JOIN) over large tables, complex aggregations (SUM, COUNT, AVG, GROUP BY), or subqueries can be very resource-intensive and time-consuming to execute repeatedly.
3. A materialized view can pre-compute these results.
4. Optimizing Data Warehousing and Business Intelligence (BI):
5. Materialized views are a cornerstone of data warehouse architectures. They are extensively used to pre-summarize and aggregate vast amounts of historical data into summary tables, often organized in star schemas or snowflake schemas.
6. Reducing Network Overhead in Distributed Systems:
7. In a distributed database environment, a materialized view can store a local copy of data from a remote source.
8. Batch Processing and Reporting:
9. For reports or processes that run periodically and require consistent, pre-computed data, materialized views ensure that the data is ready and fast to access.

Since a materialized view stores a physical copy of data, its contents can become "stale" or outdated if the data in its underlying base tables changes. Therefore, materialized views must be refreshed periodically to ensure they reflect the latest information.

Common refresh mechanisms include:

1. Complete Refresh (Full Rebuild):
2. How it works: The DBMS completely drops the existing data in the materialized view and then re-executes the materialized view's defining query from scratch, populating it with the latest results.
3. Fast Refresh (Incremental Refresh / Delta Refresh):
4. How it works: The DBMS identifies only the changes (inserts, updates, deletes) that have occurred in the underlying base tables since the last refresh. It then applies these changes to the materialized view, rather than rebuilding it entirely.

While materialized views are a powerful optimization technique, their use involves important trade-offs that must be carefully considered:

1. Increased Storage Space: Storing the pre-computed results requires additional disk space.
2. Refresh Overhead and Latency:
3. Maintaining the materialized view incurs CPU and I/O costs during refresh operations.
4. Increased Complexity: Managing and monitoring materialized views adds a layer of complexity to database administration.
5. No Direct Update: You cannot directly INSERT, UPDATE, or DELETE data in a materialized view; its content is solely derived from its defining query and subsequent refreshes.