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Materialized Views in SQL
Example of Creating a Materialized View:
What are the benefits of materialized views in Sql?
What are the use cases of SQL materialized views?
How do materialized views work?
Differences between Views and Materialized Views in SQL
What are the challenges with materialized views in SQL?
Frequently Asked Questions
Do materialized views exist in SQL Server?
Why do we use materialized views?
What is the difference between view and materialized view in Nosql?
What is the difference between materialized view and normal view in SQL?
Last Updated: Mar 27, 2024

Materialized View in SQL

Author Riya Singh
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A materialized view is a consolidated table created by combining data from multiple tables to accelerate data retrieval. For instance, in a retail app, it may merge customer and product data for improved query performance.

Materialized view in sql

Materialized Views in SQL

Materialized views in SQL provide a mechanism for storing the result of a query physically and updating it periodically, rather than recalculating the result each time the query is executed. This can lead to significant performance improvements in scenarios where the underlying data changes infrequently compared to the query frequency.

Example of Creating a Materialized View:

Since we do not have the syntax of materialized views in SQL, we can still use materialized views as below: 

  • MySQL


CREATE Table CustomerSummary AS

SELECT CustomerID, COUNT(orderID) AS NumberOfOrders

FROM Sales

GROUP BY CustomerID;

This materialized view CustomerSummary stores the count of orders for each customer. To retrieve data from it, you would use the same SELECT statement as you would with a view:

SELECT * FROM CustomerSummary;



However, the data is retrieved much faster because it is stored and does not require re-running the underlying query each time.

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What are the benefits of materialized views in Sql?

Materialized views in SQL offer several benefits, contributing to improved performance, simplified query execution, and efficient data analysis:

  • Query Performance: Materialized views store precomputed results, reducing the time needed to execute complex or resource-intensive queries. Queries using materialized views often result in faster response times compared to recalculating results on-the-fly.
  • Data Aggregation and Transformation: Materialized views are especially useful for aggregating data, allowing you to store precomputed summaries or aggregates of large datasets. They can store the results of complex transformations or calculations, simplifying subsequent querying.
  • Offline Analysis: Materialized views facilitate offline analysis by providing a snapshot of data at a specific point in time, even if the underlying data changes.
  • Reduced Load on Source Tables: By storing precomputed results, materialized views reduce the load on the source tables and databases, as queries are executed against the materialized view instead of the raw data.
  • Optimized Query Execution Plans: Materialized views can lead to optimized execution plans, as the database optimizer may leverage the precomputed results for more efficient query processing.

What are the use cases of SQL materialized views?

Materialized views in SQL are valuable in various scenarios where the benefits of precomputed results and periodic refreshes outweigh the cost of maintaining the materialized view. Some common use cases include:

1. Aggregations and Summarizations:

  • Use Case: Storing aggregated or summarized data.
  • Example: Calculating and storing the total sales per region from a large sales transactions table.

2. Complex Query Results:

  • Use Case: Storing the results of complex queries.
  • Example: Storing the result of a query involving multiple joins, filters, and calculations, providing faster access to the computed data.

3. Data Warehousing:

  • Use Case: Supporting data warehousing and reporting.
  • Example: Creating materialized views for commonly used reports in a data warehouse to improve query performance.

4. Historical Snapshots:

  • Use Case: Maintaining historical snapshots.
  • Example: Storing periodic snapshots of inventory levels for historical analysis, allowing users to analyze changes over time.

5. Offline Analysis:

  • Use Case: Enabling offline or periodic analysis.
  • Example: Storing a snapshot of data for offline analysis or in scenarios where real-time data is not critical.

6. Frequently Accessed Data:

  • Use Case: Accelerating access to frequently accessed data.
  • Example: Creating materialized views for lookup tables or reference data frequently used in queries.

How do materialized views work?

Materialized views work by storing the results of a query as a physical table or indexed view in a database. Unlike regular views, which are virtual and execute the underlying query each time they are accessed, materialized views persistently store the computed results. Here's a step-by-step overview of how materialized views work:

1. Creation of Materialized View: A materialized view is created by specifying a query that defines the data to be stored in the view. This query can include aggregations, joins, filters, or any other SQL operations.

CREATE MATERIALIZED VIEW my_materialized_view AS
SELECT column1, column2, COUNT(*)
FROM my_table
GROUP BY column1, column2;


2. Initial Data Population: The materialized view is initially populated with the result set of the specified query. This process involves executing the query and storing the computed data in the materialized view.

3. Periodic Refresh: Periodically, the materialized view needs to be refreshed to reflect changes in the underlying tables. This refresh can be triggered by a scheduled job or event.

REFRESH MATERIALIZED VIEW my_materialized_view;


4. Incremental or Full Refresh: The refresh operation can be incremental, where only the modified or new data is updated, or a full refresh, where the entire materialized view is recomputed.

5. Querying the Materialized View: Users or applications can query the materialized view like any other table or view. Since the data is precomputed, queries against the materialized view can be faster than running the original query on the source tables.

SELECT * FROM my_materialized_view WHERE column1 = 'value';


6. Benefits of Precomputed Results: By storing precomputed results, materialized views reduce the need to repeatedly execute complex queries against the raw data, leading to improved query performance.

7. Trade-Offs and Considerations: While materialized views offer performance benefits, they come with trade-offs, such as the need for storage space, periodic maintenance, and potential staleness of data between refreshes.

Differences between Views and Materialized Views in SQL

Feature  View  Materialized View
Data Storage No physical data storage; a virtual table.  Physically stores query results.
Data Freshness Always up-to-date with the underlying tables. Requires refresh to update data.
Performance It can be slower for complex queries as it runs the query in real-time.  Faster for read operations as data is precomputed.
Storage Requirement  None, since no data is stored. Requires additional storage space.
Write Operations Not directly possible. Possible under certain conditions with limitations.
Query Complexity Handling Better for simple queries. Better for complex, time-consuming queries.
Use Case  Real-time data access without storage overhead.  Optimizing performance for complex queries with acceptable data latency.

What are the challenges with materialized views in SQL?

While materialized views in SQL offer significant performance benefits, they come with certain challenges and considerations that should be taken into account:

  • The data in a materialized view can become stale between refreshes. This is particularly relevant in scenarios where real-time data is critical, and the materialized view may not reflect the most recent changes in the underlying tables.
  • Periodic refreshes can impose overhead on the database, especially for large materialized views. The time and resources required for refreshing the materialized view need to be balanced against the benefits gained from improved query performance.
  • Materialized views consume storage space to store precomputed results. Large or frequently updated materialized views may require substantial storage, impacting overall database size.
  • Managing and maintaining materialized views, including scheduling and executing refreshes, can add complexity to the database maintenance tasks. Consideration must be given to the timing and frequency of refresh operations.
  • Materialized views are not suitable for scenarios requiring real-time or near-real-time data access. They are more suited for applications where slightly outdated data is acceptable.

Also See, difference between sql and nosql

Frequently Asked Questions

Do materialized views exist in SQL Server?

SQL Server supports materialized views through indexed views. These indexed views store precomputed results for improved query performance.

Why do we use materialized views?

Materialized views are used to store precomputed query results, enhancing query performance by reducing the need to recompute complex queries.

What is the difference between view and materialized view in Nosql?

NoSQL databases typically don't have materialized views. Views in NoSQL databases are virtual and don't store precomputed results like materialized views in SQL.

What is the difference between materialized view and normal view in SQL?

Normal views in SQL are virtual and execute queries on-the-fly. Materialized views store precomputed results physically, offering performance benefits but may become stale between refreshes.


In conclusion, views and materialized views cater to different needs within SQL databases. Views offer a real-time snapshot of data with no storage cost, ideal for simple and dynamic queries. Materialized views, while requiring storage space and periodic refreshing, provide a performance advantage for complex and resource-intensive queries. Choosing between them depends on the specific requirements for data freshness and query performance in your database design.

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