Introduction
BigQuery is a serverless and enterprise data warehouse offered by Google Cloud. It enables organizations to focus on analytics and productivity by relieving them from the burden of infrastructure management. While BigQuery provides powerful features out of the box, optimizing the costs associated with storing and processing data is essential. This brief explores effective strategies for reducing BigQuery spending, focusing on cost optimization techniques for storage and querying.
Pricing Fundamentals
Understanding BigQuery’s pricing structure is crucial for managing expenses effectively. The pricing breakdown includes storage costs (active storage, long-term storage, and streaming inserts) and query processing costs (on-demand and flat-rate). It is important to note that certain operations in BigQuery, such as batch loading data, automatic re-clustering, and queries resulting in errors, do not incur any charges.
Cost Optimization Techniques: Storage
Manage data retention wisely:
Set appropriate table expiration dates to retain data for the required duration. Configure default table expiration for temporary staging data and use Data Definition Language (DDL) statements to modify expiration settings for existing tables.
Exercise caution when modifying data
Editing a table or partition resets the 90-day timer for storage cost reduction. Be mindful of actions that bring data back to active storage and consider loading new data batches into new tables or specific partitions to utilize long-term storage.
Minimize duplicate data copies
Utilize BigQuery’s federated data access model to query data directly from external sources, reducing redundant data copies and storage costs.
Evaluate the use of streaming inserts
Switch to batch loading if immediate availability of data in BigQuery is not necessary, as streaming inserts incur costs.
Cost Optimization Techniques: Querying
Only query necessary data
Select only the required columns in queries to reduce the volume of bytes processed and optimize cost. Using SELECT * is expensive as it scans all columns.
Safeguard against inadvertent errors
Use the ‘maximum bytes billed’ setting to limit query costs and avoid unintentional queries that can result in significant expenses.
Utilize caching strategically
Enable caching to improve query performance and avoid charges for retrieving results from cached tables. Employ caching for dashboards accessed by multiple users.
Implement table partitioning
Partition tables based on factors like ingestion time or date to reduce query processing costs. Analyze specific partitions instead of the entire table and leverage the independent evaluation of partitions for long-term storage cost savings.
Use clustering to minimize data scanning
Apply clustering to arrange data based on specific columns, allowing BigQuery to scan only relevant blocks during query execution. This process, known as block pruning, improves query performance.
Using material views
Store the results of complex or resource-intensive queries in materialized views to accelerate subsequent query executions. Materialized views are automatically updated by BigQuery whenever the underlying data changes, ensuring data consistency and accuracy.
Utilizing BI Engine for Enhanced Querying Cost on GCP
Leverage the BI Engine add-on to enable real-time analysis of large datasets. BI Engine utilizes in-memory caching and vectorized processing to reduce query latency and cost.
Balancing Compute and Query Costs
Make trade-offs with storage costs. Saving copies of the original data, partitioned and clustered based on different attribute combinations, can mitigate high querying costs while still maintaining cost efficiency.
Conclusion
Optimizing storage efficiency and minimizing query costs in BigQuery is crucial for organizations aiming to make the most of their data analytics initiatives. By implementing the cost optimization techniques discussed in this brief, users can effectively manage expenses and ensure efficient data storage and querying practices in BigQuery.