Database Materialized View vs. Regular View

What Is a View in a Database? 

A view in a database is a stored SQL query that presents data from one or more underlying tables as if it were a single table. Unlike a table, a view does not store data itself. Instead, it acts as a virtual layer that dynamically fetches and displays data when queried. Views are useful for organizing complex data relationships into simpler structures that are easier for developers and users to interact with.

One common use case for views is abstraction. For example, a company might have multiple tables capturing customer details, orders, and payments. A view can consolidate key fields from these tables into a single virtual table, such as a summary of customer activity. Users can query this summary view without needing to understand the underlying schema or write SQL joins.

Views can also help enforce security. By exposing only specified columns or rows through a view, database administrators can limit what data certain users are allowed to see. This is helpful in multi-user environments where not all users should have access to sensitive information like salaries or personal identifiers.

How Regular Views Work 

Regular views are non-materialized, meaning they do not store data. Instead, when a regular view is queried, the database engine interprets the view's definition and runs the underlying SQL query in real time. This ensures that the data retrieved through the view is always current, reflecting the latest updates to the underlying tables.

For example, if a view shows the total number of orders per customer, and a new order is inserted into the base table, the next time the view is queried, it will include this new data. This live linkage to the source tables ensures data freshness. However, it also means that queries on complex views can be slow, especially if they involve joins across large datasets or multiple aggregations.

Regular views also do not have indexes of their own, so the performance of a view-based query relies entirely on the indexes and structure of the underlying tables. This can lead to inefficiencies in high-load systems or when views are used in nested or chained queries.

What Are Materialized Views? 

A materialized view is a type of view that stores the result set of a query physically on disk. Unlike regular views, which generate data dynamically, a materialized view saves the query output and reuses it for future queries. This can lead to substantial performance gains, especially when dealing with expensive computations or frequently accessed aggregated data.

Materialized views are useful in data warehousing and reporting environments, where queries often involve joining large tables, filtering historical records, or calculating aggregates. By precomputing and storing these results, materialized views reduce the workload on the database engine and allow users to get query results much faster.

However, materialized views introduce the challenge of data freshness. Because they are snapshots, the data can become stale if not updated regularly. To address this, databases provide options for refreshing materialized views. These include manual refresh, scheduled refresh, and automatic refresh triggered by changes in the base tables.

How Materialized Views Work 

Materialized views work by executing the defined SQL query and storing the results in a physical table-like structure. This stored data can then be queried directly, bypassing the need to re-run the original query logic each time. The performance benefit comes from this decoupling: users query the stored results, not the underlying tables.

The key to effective use of materialized views lies in how and when they are refreshed. Some databases support complete refreshes, where the entire result set is recomputed, while others support incremental refreshes (also known as fast refreshes), where only the changes since the last update are applied. Incremental refreshes are generally faster and use fewer resources, but they require that the database can track changes in the source data accurately.

Materialized views can also be indexed independently, which further improves performance for some query patterns. However, because they consume additional storage and require maintenance, they are best used selectively—primarily for queries that are complex, run frequently, and tolerate some degree of data staleness.

Materialized Views vs. Regular Views: The Core Differences 

1. Storage and Data Persistence

Regular views do not store any data. They are essentially saved SQL queries that are parsed and executed each time they are called. The data shown by a view comes directly from the underlying base tables, meaning regular views impose minimal storage overhead. They function as logical abstractions rather than physical structures.

Materialized views consume disk space because they physically store the output of the SQL query at the time of creation or last refresh. This result set is persisted in the database, just like a table. Some systems store materialized views in separate schemas or tablespaces, and administrators need to account for the additional storage, especially if the materialized views contain large datasets or pre-aggregated information.

The difference in persistence means that materialized views can survive a restart or outage with their data intact, while regular views always require a functioning base table to generate data.

2. Data Freshness and Maintenance

Regular views always return the most current data because they query the underlying tables live. Any update, insert, or delete operation on a base table is reflected instantly in a regular view. This eliminates the need for any refresh mechanism and ensures consistency at the cost of performance, particularly for views built on top of complex queries or large datasets.

Materialized views are static snapshots of data that remain unchanged until explicitly refreshed. This introduces a freshness gap: the data users query may be outdated compared to the base tables. To mitigate this, most database systems support different types of refresh strategies:

• Manual refresh: Executed by a DBA or developer as needed
• Scheduled refresh: Configured to occur at specified time intervals (e.g., hourly, daily)
• Automatic/on-commit refresh: Triggered by changes in the source data, if supported by the DBMS

Fast (incremental) refreshes can update only the changed rows, reducing overhead, but they may require configurations like change tracking mechanisms (CDC).

Epsio, for example -- allows you to create incremental, always up-to-date materialized views—combining the freshness of regular views with the performance of precomputed results.

Related content: Read our guide on Postgres materialized view auto refresh.

3. Syntax

To illustrate the difference between regular and materialized views, let’s see an example in PostgreSQL, a database that supports both types of views.

‍Regular views are created using simple syntax:

CREATE VIEW customer_summary AS
SELECT customer_id, COUNT(*) AS total_orders
FROM orders
GROUP BY customer_id;

There are few restrictions on what can be included in a regular view. Most SQL features are allowed, including joins, subqueries, window functions, and even non-deterministic functions like NOW() or RAND().

Materialized views use slightly more complex syntax and often include clauses to control refresh behavior:

CREATE MATERIALIZED VIEW customer_summary_mv
BUILD IMMEDIATE
REFRESH FAST ON COMMIT
AS
SELECT customer_id, COUNT(*) AS total_orders
FROM orders
GROUP BY customer_id;

However, materialized views are often limited in terms of what functions and joins can be used, especially if fast refresh is desired. For example, certain types of joins, expressions, or aggregations may be disallowed or require workarounds. These constraints depend on the database system (e.g., Oracle, PostgreSQL, BigQuery).

4. Performance

Regular views do not inherently speed up query performance. Every time a view is queried, the database engine must reprocess the SQL logic and access the source tables. If the underlying tables are large or if the view contains nested queries and joins, the performance cost can be significant. There are no indexes on views themselves; any performance benefits must come from the indexes on the base tables.

Materialized views are for performance optimization. Since they store precomputed results, queries that would otherwise involve multiple joins and aggregations can now retrieve data quickly with a simple SELECT statement. Materialized views can also be indexed independently, allowing even faster access to selected rows or groups of data. This is beneficial in data warehousing or BI environments where query response time is critical.

There is a trade-off: maintaining a materialized view’s freshness, especially under frequent data changes, can consume significant system resources during refresh operations.

5. Use Cases

Regular views are well-suited for operational systems and applications where:

• Real-time data access is required
• Queries need to be simplified or standardized
• Sensitive columns or rows must be hidden via access control
• Code reusability and logical abstraction are priorities

They are typically used in OLTP systems to encapsulate business logic or simplify repetitive queries.

Materialized views are useful in analytical and reporting scenarios where:

• Query performance is more important than real-time accuracy
• Data does not change frequently, or the system can tolerate some data staleness
• Aggregated data or historical snapshots are needed
• Resources are available to maintain and refresh the materialized views regularly

They are common in OLAP systems, data marts, and ETL pipelines, where they serve as precomputed intermediates for dashboards or batch analytics.

Materialized View vs. Regular View: How to Choose 

Choosing between a materialized view and a regular view depends on the organization’s goals for performance, data freshness, and maintenance. Each option fits different workloads and usage patterns. Use the following considerations to guide this decision:

• Performance needs: Use materialized views when query performance is critical and the same complex logic runs repeatedly. Regular views are better when performance is less of a concern or when underlying data is small and well-indexed.
• Data freshness: Choose regular views if real-time accuracy is essential. Materialized views are better suited for scenarios that can tolerate delayed data due to scheduled or manual refreshes.
• Query complexity: Materialized views are useful for precomputing expensive joins and aggregations. Regular views are suitable for encapsulating logic or simplifying access to base tables without storage overhead.
• Storage and maintenance: Regular views require no additional storage or refresh logic. Materialized views consume disk space and need a refresh strategy, which adds operational complexity.
• System type: Use regular views in OLTP systems for transactional queries and enforcing access controls. Use materialized views in OLAP systems, reporting layers, or ETL pipelines where performance and pre-aggregation are priorities.
• Change frequency in source data: If source tables change frequently and freshness is critical, regular views are more appropriate. For mostly static or periodically updated datasets, materialized views offer better performance with manageable staleness.

A combination of both can often provide the best balance—using regular views for flexibility and security, and materialized views for performance-intensive reporting.

Epsio: Incremental & Always Up-To-Date Materialized Views

Traditional materialized views come with trade-offs between performance and freshness—but they don’t have to. Epsio brings the best of both worlds with incremental, automatically updated materialized views that stay lightning-fast and always current. Whether you're building analytical dashboards or scaling your data infrastructure, Epsio eliminates the overhead of manual refresh logic and stale data.

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