🚀 A Materialized View is a database object that stores the result of a query physically on disk. Unlike a regular (virtual) view, which executes its query every time it is accessed, a materialized view stores the query result and can be refreshed periodically to reflect changes in the underlying tables.

📖 What is a Materialized View?

A materialized view is a precomputed snapshot of data generated from one or more tables. Since the data is stored, querying a materialized view is often much faster than executing a complex query repeatedly.

🎯 Why Use Materialized Views?

Materialized views improve query performance by avoiding repeated execution of expensive SQL statements.

📊 Sample Tables

Orders

📝 Basic Syntax

The exact syntax varies between database systems. A common form is:

CREATE MATERIALIZED VIEW view_name AS
SELECT ...
FROM ...
WHERE ...;

💡 Create a Materialized View

Create a materialized view that stores each customer's total order amount.

CREATE MATERIALIZED VIEW CustomerSales AS
SELECT
    CustomerID,
    SUM(Amount) AS TotalSales
FROM Orders
GROUP BY CustomerID;

The query result is stored physically in the database instead of being calculated every time.

🔍 Query a Materialized View

SELECT *
FROM CustomerSales;

Since the data has already been computed, the query can be much faster than executing the aggregation on the Orders table each time.

🔄 Refresh a Materialized View

As the underlying tables change, the materialized view may become outdated. Refreshing it updates the stored data.

REFRESH MATERIALIZED VIEW CustomerSales;

📊 Virtual View vs Materialized View

📈 Materialized View vs Table

💼 Real-World Example

A business intelligence dashboard displays daily sales summaries from millions of transactions. Instead of recalculating totals every time someone opens the dashboard, a materialized view stores the aggregated results and is refreshed every hour, providing fast report generation.

CREATE MATERIALIZED VIEW DailySales AS
SELECT
    OrderDate,
    SUM(Amount) AS TotalSales
FROM Orders
GROUP BY OrderDate;

🗄️ Database Compatibility

⚠️ Advantages

⚠️ Limitations

⚠️ Best Practices

🚀 Key Points to Remember