BigQuery Materialized View Cost and Performance Best Practices💰📈

BigQuery Materialized Views is a magical feature that can help you improve query performance and reduce costs in your data warehouse. Materialized views are precomputed query results that reside in BigQuery. They are used to speed up queries that would otherwise be sluggish due to the complexity of the underlying data.

In this article, I’ll cover some best practices for using BigQuery Materialized Views to optimize cost and performance.

➡️ Understand the Cost and Performance Trade-Offs

When creating Materialized Views, it’s critical to understand the cost and performance trade-offs and to design your views to optimize for both.

To minimize the cost of Materialized Views, I would recommend the following:

• ✅ Use partitioning: By partitioning your Materialized Views on a column that is frequently accessed in your queries, you can reduce the amount of data that scans when executing the queries.
• ✅ Set expiration dates: Consider setting an expiration date on your Materialized Views to avoid incurring unnecessary storage and compute costs for views that are no longer required.
• ✅ Use Materialized Views selectively: Not all queries will benefit from Materialized Views, so use them selectively for queries that are most often used and require significant compute resources.

➡️ Optimize View Refresh Settings

By default, BigQuery updates Materialized Views once every 24 hours. However, you can adjust the refresh frequency to optimize performance and reduce costs.

To optimize your view refresh settings, evaluate the following:

• ✅ Tune the refresh frequency: Adjust the refresh frequency based on the rate of data changes in the underlying tables. If the data changes frequently, you may need to increase the refresh frequency to ensure that the Materialized Views are up-to-date.
• ✅ Use incremental refresh: If you’re only interested in updating a subset of the data in the Materialized View, consider using incremental refresh instead of a full refresh.
• ✅ Monitor the refresh schedule: Keep tracking the Materialized View refresh schedule to ensure that you are updating the views in a timely and efficient manner.

➡️ Optimize Query Design for Materialized Views

The design of your queries will have a significant impact on the performance of Materialized Views. By optimizing your queries for Materialized Views, you can further improve query performance and reduce costs.

To optimize query design for Materialized Views, assess the following:

• ✅ Use Materialized Views in combination with standard tables: Consider using Materialized Views in combination with standard tables to optimize query performance. By using Materialized Views to precompute frequently used queries, you can reduce the amount of data that needs to be scanned when executing queries against standard tables.
• ✅ Avoid over-aggregation: When creating Materialized Views, be careful not to over-aggregate the data. Over-aggregation can result in Materialized Views that are too large and slow to refresh, which can negatively impact query performance.
• ✅ Use query pruning: Query pruning is a technique that involves removing unnecessary columns and rows from Materialized Views to reduce the amount of data that needs to be scanned when executing queries. By using query pruning, you can further improve query performance and reduce costs.

Conclusion

By following the best practices outlined above, you can optimize the cost and performance of Materialized Views to better suit the needs of your organization.