Types of Views in SQL: Explore Key Variants and Their Advantages and Limitations
Updated on Mar 18, 2025 | 23 min read | 59.5k views
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Updated on Mar 18, 2025 | 23 min read | 59.5k views
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SQL views allow you to simplify database queries and decide which data stays in plain sight. They serve as virtual snapshots of one or more tables, returning results based on the filtering or calculations you define. This approach maintains data accuracy while adding a protective layer around sensitive columns.
Different types of views in SQL also minimize the need for complex joins, which makes queries more readable and the entire system easier to maintain.
In this blog, you will learn about all types of views in SQL (Structured Query Language), steps to create and modify them, and tips for handling updates through a view. You will also discover ways to handle performance challenges and secure your data when multiple users need controlled access.
A view is a carefully defined query that presents data as though it were a table. It does not store rows on disk, so it always stays connected to underlying tables. Each time you query the view, it retrieves current information according to the conditions or columns you set.
You can highlight only the details you want, which can simplify work on large tables and keep sensitive columns out of reach. This design is especially helpful when you want consistent and secure data access without complicating your base tables.
Below are the key characteristics that define what makes a view distinct in a database:
Let’s understand this through an example.
Instead of repeatedly writing a complex query to fetch sales data for the current year, you can create a view that does the work for you:
CREATE VIEW CurrentYearSales AS
SELECT OrderID, CustomerID, TotalAmount
FROM Sales
WHERE YEAR(OrderDate) = YEAR(GETDATE());
This view, CurrentYearSales, allows you to fetch the data directly with a simple SELECT * FROM CurrentYearSales, eliminating repetitive query writing.
A view behaves like a virtual table, which runs a stored SELECT query each time it is accessed. A physical table, in contrast, stores its records and updates them directly whenever data is inserted or deleted. The contents of a virtual table never occupy permanent space, and they only refresh when someone queries the view.
Here is a brief outline of how they operate:
Also Read: The Essential Guide To Understanding SQL In DBMS
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Some types of views in SQL rely on calculations that run each time, while others hold separate copies behind the scenes. They all share one goal: keep your queries concise and your project organized.
Choosing the right type can reduce overhead when filtering or combining data. Below is a closer look at the main types of views in DBMS you might use, along with their strong points.
System-defined views come prepackaged in the database engine. They store details about your database structure and system-wide metrics, which helps you check on tables, columns, or even server statistics without manual tracking. They can also show which objects depend on each other, so you can plan changes more accurately.
Below is a quick example of how such a view might be used.
You can run the snippet below to see a list of all databases in your SQL environment, along with their IDs and creation dates:
SELECT name, database_id, create_date
FROM sys.databases;
This query displays the internal metadata that the server keeps about your databases. The result set tells you the database names, numerical IDs, and the day each was created. Although these results stem from a built-in view, you can still filter or join them with other data as needed.
System-defined views are further broken down into three types. Let’s explore each one in detail.
Information schema views are designed to show details about objects in your database. They often begin with INFORMATION_SCHEMA and can reveal everything from columns and constraints to table relationships.
Characteristics Of Information Schema Views
Example Of Information Schema View
Below is a snippet that returns all the columns in a specified table:
SELECT COLUMN_NAME, DATA_TYPE
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_NAME = 'YourTableName';
This query returns every column’s name and data type in the chosen table. You get a quick overview of column characteristics without browsing through multiple system tables. This helps when you need a standardized way to see the schema layout.
Catalog views hold metadata about various database objects, such as indexes or triggers. They help you find information that might not be directly exposed in an information schema view.
Characteristics Of Catalog Views
Example Of Catalog View
You can see a list of all user-defined tables in the current database by running this:
SELECT name, object_id
FROM sys.tables;
The results reveal each table’s name and a unique identifier. You can pair this identifier with other catalog views or system-defined tables to learn more, such as dependencies or indexing details.
Dynamic Management Views show the present state of the server, databases, or running sessions. They are valuable when assessing performance or checking for blocking queries.
Characteristics Of Dynamic Management Views
Example Of DMV
Below is a snippet that retrieves information about current connections:
SELECT session_id, connect_time, host_name
FROM sys.dm_exec_connections;
This output shows you which sessions are active, when they started, and which machines they originate from. It helps track who is connected and how long they have been running certain tasks.
Also Read: Mastering Schema in SQL
These types of views in SQL are custom-made queries that focus on specific aspects of your data. They can involve a single table or multiple ones, depending on how much detail you want to reveal.
Developers often use user-defined views to simplify queries, restrict columns, or even rename fields for clarity. They act as your personal overlays on top of the raw data, with minimal impact on physical storage.
Here is an example of creating a user-defined view.
You can run the snippet below to consolidate essential employee records into a single view:
CREATE VIEW EmployeeBrief
AS
SELECT EmployeeID, FullName, Department
FROM Employees
WHERE IsActive = 1;
This statement defines a view named EmployeeBrief that returns selected columns from an Employees table. By avoiding rummaging through every column in the original table, you keep results focused only on active individuals. Queries against EmployeeBrief are also shorter, which helps you cut down on repetitive code.
Next, you will see several categories of user-defined views, each aiming to solve different problems.
A simple view references just one table and does not include advanced features such as aggregate functions or subqueries. It works best when you want to filter or rename columns without complicating your SQL.
Characteristics of Simple View
Example of Simple View
Below is a snippet that returns only active job postings from a table named JobOpenings:
CREATE VIEW ActiveJobs
AS
SELECT JobID, Title, Department
FROM JobOpenings
WHERE IsOpen = 1;
This query creates a view called ActiveJobs that pulls in basic details. You can now query ActiveJobs instead of scanning all positions in the JobOpenings table, which limits clutter and keeps the focus on the rows that truly matter.
Here’s a typical structure for JobOpenings, which shows the columns and their constraints:
Column Name |
Data Type |
Constraints |
JobID | INT | PRIMARY KEY |
Title | VARCHAR(200) | — |
Department | VARCHAR(100) | — |
IsOpen | BIT | Uses 1 for active jobs |
When you query the ActiveJobs view, only rows with IsOpen set to 1 appear in the results. This approach keeps the underlying data intact while giving you a streamlined subset for quick reference.
Complex views involve more than one table or include clauses like GROUP BY, aggregate functions, and subqueries. They are suitable when combining or summarizing data from several sources.
Characteristics of Complex View
Example of Complex View
In the snippet below, the view returns the total client revenue from two tables.
CREATE VIEW ClientRevenue
AS
SELECT c.ClientName, SUM(t.Amount) AS TotalRevenue
FROM Clients c
JOIN Transactions t ON c.ClientID = t.ClientID
GROUP BY c.ClientName;
Below are the two tables that feed into this view. Their relationship hinges on a foreign key, which links each transaction to a matching client.
Clients’ Table
Column Name |
Data Type |
Constraints |
ClientID | INT | PRIMARY KEY |
ClientName | VARCHAR(100) | - |
Transactions’ Table
Column Name |
Data Type |
Constraints |
TransactionID | INT | PRIMARY KEY |
ClientID | INT | FOREIGN KEY REFERENCES Clients(ClientID) |
Amount | DECIMAL(10,2) | - |
Each row in Transactions includes a ClientID that maps back to Clients, ensuring every transaction belongs to a valid client. The ClientRevenue view then groups those transactions by client and sums each total.
You now have an easy way to access each client’s overall revenue. You can join this view with others or apply further filtering as needed. However, direct inserts or updates often fail because data is drawn from multiple places.
An inline view is a SELECT subquery within the FROM clause of another query. It stays temporary and does not store anything separately, but it simplifies code by breaking down multi-step logic.
Characteristics of Inline View
Example of Inline View
In the snippet below, a subquery calculates average sales per region, then the outer query filters those regions:
SELECT RegionName, AvgSales
FROM
(
SELECT RegionName, AVG(SalesAmount) AS AvgSales
FROM RegionalSales
GROUP BY RegionName
) RegionStats
WHERE AvgSales > 10000;
The inline view, labeled RegionStats, gives each region’s average sales. The main query then retrieves rows only if average sales exceed 10000. This approach avoids creating a permanent view definition.
Materialized views and indexed views physically store data from their defining queries. They reduce the time it takes to run certain heavy queries because the results are partially or fully precomputed.
Characteristics of Materialized Views / Indexed Views
Example of Materialized View / Indexed View
Here is a snippet that stores aggregated sales totals by product:
CREATE MATERIALIZED VIEW ProductTotals
AS
SELECT ProductID, SUM(QuantitySold) AS TotalSold
FROM SalesHistory
GROUP BY ProductID;
The view ProductTotals now has its own stored rows containing product sales. Queries against it run faster than recalculating sums every time. However, you must refresh it (on a schedule or manually) to keep numbers current.
Partitioned views stitch together rows from several physical tables into a single, unified result set. These tables can be local to one server or distributed across multiple servers.
Characteristics of Partitioned Views
Example of Partitioned View
The snippet below merges quarterly sales tables into one logical set:
CREATE VIEW AllSales
AS
SELECT * FROM Sales_Q1
UNION ALL
SELECT * FROM Sales_Q2
UNION ALL
SELECT * FROM Sales_Q3
UNION ALL
SELECT * FROM Sales_Q4;
The view AllSales then seamlessly shows the complete year's data. This strategy helps maintain large datasets in separate physical segments yet still provides a single point of reference.
Certain design choices determine how smoothly a database runs. If you find that storing raw data alone is not enough, or your SQL queries keep growing in complexity, there is a structured approach you can adopt instead of managing everything in base tables.
Below are a few reasons to consider using views:
Design requirements may shift as your database evolves, so having a clear way to introduce, revise, or remove views is fundamental. Each process involves distinct SQL commands that ensure you retain control over how data is exposed and manipulated.
Despite minor syntax variations between database systems, the main actions stay consistent: you pick a name for the view, define its columns or conditions, and then either update or drop it as you see fit.
Below is a roadmap for setting up a brand-new view, modifying existing ones, and removing irrelevant ones.
The CREATE VIEW command gives you a single reference point for a query that might otherwise be repeated multiple times. You can form a view based on one table or unify data from multiple sources into a logical entity. You are free to filter rows, rename columns, or even combine data from multiple tables.
Let’s walk through the basic pattern, then see an example.
Below is a snippet that showcases the core syntax:
CREATE VIEW view_name AS
SELECT column1, column2, ...
FROM table_name
WHERE condition;
This template outlines how you declare your view, specify the columns to include, and optionally apply conditions like WHERE to limit rows. For instance, a single-table view might only show records meeting certain criteria, while a multi-table definition could join multiple sources in one place.
You can also rename columns within the SELECT statement to make your view more intuitive. For example:
CREATE VIEW EmployeeOverview
AS
SELECT EmployeeID AS ID, FullName AS Name
FROM StaffRecords
WHERE IsActive = 1;
In this scenario, the table StaffRecords has columns named EmployeeID and FullName, but the view presents them as ID and Name. You also filter out inactive employees by using a WHERE clause. Anyone querying the EmployeeOverview view sees a narrowed, user-friendly version of the table.
Sometimes, a view needs to reflect a shifting reality, like when new columns appear in the base table, or your naming conventions change. Editing a view requires redefining it by default, so many databases offer a CREATE OR REPLACE VIEW statement to streamline the process.
Below is a snippet that adds a column to an existing view:
CREATE OR REPLACE VIEW EmployeeOverview
AS
SELECT EmployeeID AS ID, FullName AS Name, Department
FROM StaffRecords
WHERE IsActive = 1;
Here, the main difference is the OR REPLACE phrase, which instructs the database to keep the same name (EmployeeOverview) but swap in the new definition. This helps maintain references or permissions already granted on the view.
However, check whether the new definition aligns with existing queries or applications. Adding columns may be simpler than removing them since dependent queries might break if columns suddenly vanish.
When revising views, consider the following:
If a view has outlived its purpose or needs to be replaced entirely, you can remove it. This is done with a simple DROP VIEW command, which stops the database from maintaining a definition under that view name.
Below is the relevant snippet:
DROP VIEW EmployeeOverview;
When you drop a view:
Make sure no vital dependencies remain before you run this command, especially in production environments. If a view is crucial to ongoing operations, dropping it could break a range of front-end or reporting tasks.
It can be helpful to see which views exist, especially in larger databases. Different systems offer various ways to look up existing views. Some platforms let you run a statement like SHOW FULL TABLES to spot entries labeled as views, while others use catalog or information schema tables for that purpose.
Below is a short snippet that references system metadata to list every view in your current database:
SELECT table_name
FROM information_schema.views
WHERE table_schema = 'YourDatabaseName';
The result set includes each view’s name, which helps you locate definitions that need review or cleanup. In some environments, you can also retrieve details like the exact SELECT statement used to create each view, which aids in documentation.
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Some types of views in SQL allow you to run data manipulation language (DML) commands such as UPDATE, INSERT, or DELETE without directly touching the underlying tables. This depends on how the view is defined and whether it includes complex clauses like GROUP BY or references multiple sources.
In many cases, adjusting the real data through a simplified or filtered perspective can save time. However, not all views qualify, so it's helpful to know exactly when your changes will succeed and how they influence the base tables.
Views that let you modify underlying data must meet certain requirements. Here is a brief list of conditions:
Some updatable views let you change values in the underlying table without opening multiple objects. If the conditions above are satisfied, your changes automatically flow back to the real data.
Below is a snippet that demonstrates how to update a record through a view. There are no extra columns beyond what’s stored in the base table, so the update works:
UPDATE ActiveJobs
SET Department = 'Technology'
WHERE JobID = 101;
When you run this statement, the system locates the corresponding row in the JobOpenings table (referenced by the ActiveJobs view) and sets its Department to 'Technology'. This approach helps you keep your code centralized if your view has already filtered out irrelevant rows.
When a view includes all the required columns (especially those marked NOT NULL in the base table), you can insert new records by treating the view almost like a direct table reference.
Below is a snippet that adds a fresh job posting via the same ActiveJobs view. Keep in mind that you must include columns for any fields that do not accept null values.
INSERT INTO ActiveJobs (JobID, Title, Department, IsOpen)
VALUES (202, 'Lead Designer', 'Design', 1);
The DELETE operation works if the view is directly tied to one base table. In a multi-table scenario, the database cannot determine which physical rows to remove reliably.
Here is a snippet that deletes a row through the ActiveJobs view:
DELETE FROM ActiveJobs
WHERE JobID = 202;
Any row that meets this condition is erased in the JobOpenings table. If your view combined records from more than one table, this command would typically fail or be disallowed.
Updatable views sometimes need further control so you do not enter data that falls outside their defined conditions. You can add WITH CHECK OPTION to enforce the same WHERE clause used in the view when inserting or updating.
Below is a snippet that illustrates how you might define a view with this clause:
CREATE VIEW ActiveJobsSecure
AS
SELECT JobID, Title, Department
FROM JobOpenings
WHERE IsOpen = 1
WITH CHECK OPTION;
Because of the WITH CHECK OPTION:
This mechanism ensures data inserted or updated through the view always stays consistent with your filter logic.
Some data teams need minimal overhead and immediate visibility, while others prefer higher performance for large aggregations. Each view category responds to a different challenge, which is why selecting the right one can make a significant impact on daily work.
Below are some key advantages different types of views in SQL bring:
If not planned well, views can hamper performance or add complexity. They can also limit certain updates or hide important row-level details. Recognizing these drawbacks makes it easier to decide whether a specific view approach is right for a given problem.
Below are a few factors that can turn views into a liability:
As a beginner, you might find views a bit overwhelming at first, with their virtual structure, query dependencies, and potential performance challenges. However, once you grasp the different types of views in SQL, you’ll gain a valuable tool to boost your database management skills and efficiency.
Let’s dive into the key practices for using views.
1. Naming Conventions
Consistent naming conventions improve readability and collaboration when working with views. A transparent naming scheme helps developers understand the purpose of the view and its associated table.
Best practices:
Have a look at the code snippet for how to use the proper naming:
-- Create a view with a clear, descriptive name
CREATE VIEW vw_EmployeeDetails AS
SELECT EmployeeID, FirstName, LastName, Department
FROM Employees
WHERE Status = 'Active';
This approach makes it clear that vw_EmployeeDetails provides information about active employees.
2. Performance Considerations
Based on their design, views can either improve or degrade performance. Simple views are lightweight, while complex or nested views can become slow if not optimized.
Performance tips:
Understand via the code example:
-- Optimized view with minimal columns and filters
CREATE VIEW vw_DepartmentSales AS
SELECT DepartmentID, SUM(TotalAmount) AS TotalSales
FROM Sales
WHERE SaleDate >= '2025-01-01'
GROUP BY DepartmentID;
This view limits data to recent sales and ensures faster query performance.
3. Security
Views are an excellent way to restrict access to sensitive data. Exposing only specific columns or rows allows you to control what information users can see without modifying the underlying table.
Security tips:
Look at the code example to have a better understanding:
-- Create a secure view to restrict salary information
CREATE VIEW vw_PublicEmployeeData AS
SELECT EmployeeID, FirstName, LastName, Department
FROM Employees;
-- Grant access to the view, not the table
GRANT SELECT ON vw_PublicEmployeeData TO PublicUser;
Here, the PublicUser cannot access sensitive salary data in the Employees table.
4. Indexing
Indexing can improve the performance of queries that run against views, especially materialized ones. Indexed views (materialized views with indexes) speed up data retrieval for large datasets.
Best tips for indexing:
Have a look below for a better understanding:
-- Create an indexed materialized view for faster lookups
CREATE MATERIALIZED VIEW mv_IndexedSales AS
SELECT Region, ProductID, SUM(SalesAmount) AS TotalSales
FROM Sales
GROUP BY Region, ProductID;
-- Add an index to the materialized view
CREATE INDEX idx_RegionProductSales ON mv_IndexedSales (Region, ProductID);
5. Limit Nested Views
While creating views that depend on others is tempting, excessive nesting can lead to performance degradation and debugging difficulties. Instead, simplify your queries by reducing dependencies.
Best Practices:
Go through the example below:
-- Nested views can slow down queries
CREATE VIEW vw_RegionSales AS
SELECT Region, SUM(SalesAmount) AS TotalSales
FROM Sales
GROUP BY Region;
CREATE VIEW vw_FilteredRegionSales AS
SELECT Region, TotalSales
FROM vw_RegionSales
WHERE TotalSales > 10000;
-- Better approach: combine into a single view
CREATE VIEW vw_OptimizedRegionSales AS
SELECT Region, SUM(SalesAmount) AS TotalSales
FROM Sales
WHERE SalesAmount > 10000
GROUP BY Region;
Reducing nested layers makes the query more efficient and easier to maintain.
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