SQL Materialized View: Enhancing Query Performance

Refreshing a Materialized View in SQL

Data in materialized views can be refreshed to stay updated with the underlying tables. The choice of refresh method depends on the business requirements and performance considerations of the particular database. Let us look at the following data refresh methods of materialized views.

Manual refresh

In manual refresh, the materialized view is refreshed only when explicitly requested by the user. This approach gives the most control over when data is updated, making it suitable for scenarios where data changes infrequently, or updates are performed during off-peak hours.

The following query shows the manual refresh method in PostgreSQL.

REFRESH MATERIALIZED VIEW sales_summary;

Periodic refresh

The materialized view is automatically refreshed at specified intervals during the refresh period, ensuring data is up-to-date without user intervention. This method is useful for time-sensitive applications where data needs to be relatively current.

The example below shows how to include periodic refreshes in Oracle by defining refresh schedules directly in the CREATE MATERIALIZED VIEW statement. The refresh time is set at hourly intervals.

-- Create a materialized view to aggregate sales data
-- Set to refresh completely every hour
CREATE MATERIALIZED VIEW sales_summary
REFRESH COMPLETE START WITH (SYSDATE) NEXT (SYSDATE + 1/24)
AS
SELECT product_id, 
       SUM(quantity) AS total_quantity, 
       SUM(price * quantity) AS total_revenue
FROM sales
GROUP BY product_id;

On-demand refresh

The on-demand refresh occurs whenever the underlying data changes, typically through a trigger mechanism. This ensures that the materialized view always contains the updated data. For example, in PostgreSQL, triggers can be set up to refresh the view when changes occur in the underlying table.

Full vs. incremental refreshes

You can refresh the data in full or incremental refreshes in materialized views. The following table summarizes the two methods and the use cases.

Best Practices for Materialized Views

When using materialized views, it is important to consider the following practices for optimal usage.

• Choosing the Right Queries to Materialize: Materialize complex, resource-intensive queries such as joins, aggregations, and subqueries. Such queries would benefit from precomputed results reducing the load on the database.
• Balancing Data Freshness and Performance: Depending on your use case, choose the appropriate refresh strategy, such as manual, periodic, or on-demand refresh methods. Use incremental refresh to reduce computational load and monitor data change frequency to decide refresh intervals.
• Using Materialized Views to Optimize Query-Heavy Workloads: Leverage materialized views for BI reports and dashboards where quick response times are essential. You can also index the columns used in materialized views for faster filtering and sorting.

Materialized Views in Different Database Systems

As you have seen, different database systems offer varying support for materialized views. The following table summarizes the unique features and limitations of materialized views in these databases.

If you use SQL Server as your preferred database, I recommend taking DataCamp’s Introduction to SQL Server course to master the basics of Microsoft SQL Server for data analysis. Also, check out our SQL Server Developer career track to understand how to optimize queries and troubleshoot issues in SQL Server.

Additional Things to Consider

While SQL materialized views are useful for query optimization, they also come with some challenges and limitations. The following are the common issues with materialized views and how to address them.

• Storage Overhead: Materialized views store query results physically on disk, which increases storage requirements. To avoid consuming unnecessary storage space, only materialize views for resource-intensive queries and partition the materialized views for large datasets.
• Update Costs and Refresh Overhead: Keeping materialized views in sync with the underlying tables can be resource-intensive, especially for views that require frequent updates or involve complex calculations. To avoid the refresh overhead, use incremental refresh where supported or set the appropriate refresh intervals when database usage is lower.
• Data Consistency and Synchronization: Materialized views can become outdated if the underlying data changes frequently, leading to stale data issues. To avoid this problem, select the appropriate refresh strategy and monitor the data changes to adjust the refresh strategy as needed.
• Maintenance Overhead: Materialized views require ongoing maintenance, such as setting appropriate refresh schedules, monitoring storage usage, and tracking dependencies on the underlying tables. To overcome this challenge, always use automated refresh schedules, monitor system performance, and set up alerts for failed refreshes.

Conclusion

Materialized views are useful in SQL databases for optimizing query performance. They store the query's results physically on disk, offering faster query performance by reducing recomputation. This feature makes the materialized views useful for handling complex, resource-intensive queries that involve joins, aggregations, and large datasets. Understanding how to implement materialized views in different databases will help you enhance your query and database optimization skills.

If you are looking to advance your SQL skills, I recommend trying out DataCamp’s Associate Data Analyst in SQL career track to become a proficient data analyst. The Reporting in SQL course will also help you become proficient in building complex reports and dashboards for effective data presentation. Finally, you should obtain the SQL Associate Certification to showcase your mastery in using SQL to solve business problems and stand out among other professionals.