The CREATE MATERIALIZED VIEW command improves performance by caching complex query results so they can be accessed without rerunning the SQL. It is especially useful for large datasets, reducing compute costs and speeding up reporting by reusing previously calculated results.
When to Use CREATE MATERIALIZED VIEW in BigQuery
CREATE MATERIALIZED VIEW is used when queries are repeated often and performance or cost efficiency is a priority.
Key uses include:
- Recurring analysis: Ideal for dashboards and scheduled reports that regularly run the same aggregations or filters, ensuring results are delivered instantly.
- Performance needs: Useful for queries on large datasets where recalculating results each time would slow workflows and increase resource consumption.
- Operational monitoring: Supports near real-time business reporting by automatically refreshing materialized results without rerunning the full SQL query.
- Resource optimization: Helps save on BigQuery compute costs by storing precomputed results and reducing repeated scans of underlying tables.
Required Permissions for CREATE MATERIALIZED VIEW in BigQuery
Creating a materialized view in BigQuery requires specific permissions that control who can create, manage, and query these views.
Key permissions include:
- Table creation rights: Users need the bigquery.tables.create permission on the target dataset to create a new materialized view.
- Querying base tables: Permissions to access and query the underlying tables are required, since the materialized view is built from them.
- IAM roles support: These permissions are included in predefined roles like BigQuery Data Editor and BigQuery Admin, simplifying role assignment.
- Controlled access: Administrators can grant permissions selectively to ensure only authorized users manage materialized views within shared datasets.
Benefits of Using CREATE MATERIALIZED VIEW in BigQuery
CREATE MATERIALIZED VIEW in BigQuery offers performance, cost, and usability benefits that make it valuable for frequent and complex queries.
Key benefits include:
- Faster performance: Precomputes and stores results, reducing query time for repeated aggregations, filters, or joins on large datasets.
- Cost savings: Minimizes BigQuery compute costs by avoiding repeated scans of underlying base tables whenever queries are run.
- Automatic refresh: Keeps data updated by refreshing materialized results automatically, ensuring reports reflect the most recent changes.
- Simplified workflows: Reduce complexity for analysts by eliminating the need to write or execute resource-intensive SQL queries repeatedly.
- Scalable analytics: Supports growing data volumes while maintaining fast response times, making it suitable for enterprise-level reporting.
Limitations of Using CREATE MATERIALIZED VIEW in BigQuery
While materialized views offer speed and cost advantages, they also have important technical restrictions that users must consider.
Key limitations include:
- Partition alignment: Only time-based partition transformations, such as YEAR, MONTH, DAY, or HOUR, are supported when aligning with base tables.
- Partition granularity: The view’s partition cannot be finer than the base table, meaning monthly partitions and yearly partitions are not supported.
- Partition limits: If Iceberg tables change across more than 4000 partitions, the materialized view is fully invalidated during refresh.
- Partition evolution: Changes to partitioning columns in the base table may invalidate the view, requiring recreation instead of a simple refresh.
- Snapshot requirement: At least one snapshot must exist in the base table for the materialized view to function properly.
- BigLake dependency: Iceberg tables must be BigLake tables, such as authorized external tables, for materialized views to work.
- Metadata risks: Queries may fail if the metadata.json file of an Iceberg table is corrupted, disrupting refreshes or queries.
- VPC Service Controls: Service accounts of authorized external tables must be added to ingress rules, or background refresh will be blocked.
Examples of CREATE MATERIALIZED VIEW in BigQuery
CREATE MATERIALIZED VIEW can be applied in multiple scenarios to improve performance and simplify repeated queries on large datasets.
Key examples include:
CREATE MATERIALIZED VIEW my_dataset.sales_summary AS
SELECT customer_id, SUM(order_amount) AS total_sales
FROM my_dataset.orders
GROUP BY customer_id;
This example precomputes total sales per customer, making repeated reporting faster and more cost-efficient.
CREATE MATERIALIZED VIEW my_dataset.daily_users AS
SELECT DATE(login_time) AS login_date, COUNT(DISTINCT user_id) AS active_users
FROM my_dataset.user_logins
GROUP BY login_date;
Here, daily active users are calculated once and stored, avoiding heavy recalculations on login events.
- Campaign performance view:
CREATE MATERIALIZED VIEW my_dataset.campaign_metrics AS
SELECT campaign_id, SUM(clicks) AS total_clicks, SUM(conversions) AS total_conversions
FROM my_dataset.ad_data
GROUP BY campaign_id;
This example enables marketers to quickly track campaign performance without rerunning large aggregations on ad data.
From Data to Decisions: OWOX BI SQL Copilot for Optimized Queries
OWOX BI SQL Copilot helps teams work smarter in BigQuery by generating, optimizing, and explaining SQL queries for tasks like CREATE MATERIALIZED VIEW. It minimizes errors, speeds up workflows, and ensures accuracy, allowing analysts to focus on insights and decision-making instead of manual query tuning.
