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Materialized view

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In computing, a materialized view is a database object that contains the results of a query. For example, it may be a local copy of data located remotely, or may be a subset of the rows and/or columns of a table or join result, or may be a summary using an aggregate function.

The process of setting up a materialized view is sometimes called materialization.[1] This is a form of caching the results of a query, similar to memoization of the value of a function in functional languages, and it is sometimes described as a form of precomputation.[2][3] As with other forms of precomputation, database users typically use materialized views for performance reasons, i.e. as a form of optimization.[4]

Materialized views that store data based on remote tables were also known as snapshots[5] (deprecated Oracle terminology).

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. 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. This enables much more efficient access, at the cost of extra storage and of some data being potentially out-of-date. Materialized views find use especially in data warehousing scenarios, where frequent queries of the actual base tables can be expensive.[citation needed]

In a materialized view, indexes can be built on any column. In contrast, in a normal view, it's typically only possible to exploit indexes on columns that come directly from (or have a mapping to) indexed columns in the base tables; often this functionality is not offered at all.



Materialized views were implemented first by the Oracle Database: the Query rewrite feature was added from version 8i.[6]

Example syntax to create a materialized view in Oracle:

     AS SELECT * FROM <table_name>;


In PostgreSQL, version 9.3 and newer natively support materialized views.[7] In version 9.3, a materialized view is not auto-refreshed, and is populated only at time of creation (unless WITH NO DATA is used). It may be refreshed later manually using REFRESH MATERIALIZED VIEW.[8] In version 9.4, the refresh may be concurrent with selects on the materialized view if CONCURRENTLY is used.[9]

Example syntax to create a materialized view in PostgreSQL:

 [ WITH (storage_parameter [= value] [, ... ]) ]
    [ TABLESPACE tablespace_name ]
     AS SELECT * FROM <table_name>;

SQL Server[edit]

Microsoft SQL Server differs from other RDBMS by the way of implementing materialized view via a concept known as "Indexed Views". The main difference is that such views do not require a refresh because they are in fact always synchronized to the original data of the tables that compound the view. To achieve this, it is necessary that the lines of origin and destination are "deterministic" in their mapping, which limits the types of possible queries to do this. This mechanism has been realised since the 2000 version of SQL Server.

Example syntax to create a materialized view in SQL Server:

FROM <table_name>

Stream processing frameworks[edit]

Apache Kafka (since v0.10.2), Apache Spark (since v2.0), Apache Flink, Kinetica DB,[10] Materialize,[11] and RisingWave[12] all support materialized views on streams of data.


Materialized views are also supported in Sybase SQL Anywhere.[13] In IBM Db2, they are called "materialized query tables".[14] ClickHouse supports materialized views that automatically refresh on merges. [15] MySQL doesn't support materialized views natively, but workarounds can be implemented by using triggers or stored procedures [16] or by using the open-source application Flexviews.[17] Materialized views can be implemented in Amazon DynamoDB using data modification events captured by DynamoDB Streams. Google announced in 8 April 2020[18] the availability of materialized views for BigQuery[19] as a beta release.


  1. ^ Compare: C.J. Date (28 August 2006). The Relational Database Dictionary: A Comprehensive Glossary of Relational Terms and Concepts, with Illustrative Examples. "O'Reilly Media, Inc.". p. 59. ISBN 978-1-4493-9115-7. Retrieved 26 October 2016. materialization[:] A somewhat unsophisticated technique for implementing operations on views according to which (a) the relational expression that defines the view is evaluated at the time the operation is invoked, (b) the view is thereby materialized, and (c) the operation in question is then executed against the relation so materialized.
  2. ^ Karen Morton; Kerry Osborne; Robyn Sands; Riyaj Shamsudeen; Jared Still (28 October 2013). Pro Oracle SQL. Apress. p. 48. ISBN 978-1-4302-6220-6.
  3. ^ Marie-Aude Aufaure; Esteban Zimányi (16 January 2012). Business Intelligence: First European Summer School, EBISS 2011, Paris, France, July 3-8, 2011, Tutorial Lectures. Springer Science & Business Media. p. 43. ISBN 978-3-642-27357-5.
  4. ^ Michael L. Gonzales (25 February 2003). IBM Data Warehousing: with IBM Business Intelligence Tools. John Wiley & Sons. p. 214. ISBN 978-0-471-45736-7.
  5. ^ C.J. Date (28 August 2006). The Relational Database Dictionary: A Comprehensive Glossary of Relational Terms and Concepts, with Illustrative Examples. "O'Reilly Media, Inc.". p. 59. ISBN 978-1-4493-9115-7. Retrieved 26 October 2016. materialized view[:] Deprecated term for a snapshot. [...] The problem is [...] that (as the definition indicates) snapshots have come to be known, at least in some circles, not as snapshots at all but as materialized views. But snapshots aren't views; views are virtual and snapshots aren't, and 'materialized view' is a contradiction in terms (at least as far as the model is concerned). Worse yet, the unqualified term view is often taken to mean a materialized view specifically, and thus we're in danger of no longer having a good term for a view in the original sense.
  6. ^ Oracle8i Tuning Release 8.1.5. Ecst.csuchico.edu. Retrieved on 2012-02-09.
  7. ^ "Materialized Views - PostgreSQL wiki". wiki.postgresql.org. Retrieved 29 November 2022.
  8. ^ "CREATE MATERIALIZED VIEW". PostgreSQL Documentation. 10 November 2022. Retrieved 29 November 2022.
  9. ^ "REFRESH MATERIALIZED VIEW". PostgreSQL Documentation. 13 February 2020. Retrieved 29 November 2022.
  10. ^ "Materialized Views". Retrieved 28 December 2022.
  11. ^ "CMU DB Talk: Building Materialize". Retrieved 30 March 2022.
  12. ^ "Is RisingWave the Next Apache Flink?". www.singularity-data.com. 28 April 2022. Retrieved 30 June 2022.
  13. ^ Materialized Views – Sybase SQL Anywhere Archived 2009-12-14 at the Wayback Machine. Ianywhere.com. Retrieved on 2012-02-09.
  14. ^ Improving Performance with SQL Server 2005 Indexed Views. Microsoft.com. Retrieved on 2012-02-09.
  15. ^ ClickHouse Documentation MaterializedView. Clickhouse.yandex. Retrieved on 2019-09-05.
  16. ^ Implementing materialized views in MySQL. Shinguz.ch (2006-11-06). Retrieved on 2012-02-09.
  17. ^ Flexviews for MySQL – incrementally refreshable materialized views w/ MySQL. Flexviews.sourceforge.net. Retrieved on 2012-02-09.
  18. ^ "Release notes". Google.com. 8 April 2020. Retrieved 21 July 2021.
  19. ^ Google BigQuery Materialized Views documentation Google.com Retrieved on 2020-05-20.

External links[edit]