Concrete guidelines to cut cost and speed up queries in Google BigQuery.
They reduce query cost, improve performance, and keep datasets maintainable by enforcing smart table design, query patterns, and resource governance.
Partition large fact tables by a frequently-filtered DATE/TIMESTAMP column such as order_date
. Add clustering on high-cardinality columns like customer_id
or product_id
to prune blocks during scans.
Partitioning narrows scanned partitions; clustering orders data within each partition, allowing BigQuery to skip more blocks and lower slot usage.
Only in ad-hoc exploration on small tables. In production queries, list required columns to avoid reading unneeded column blocks.
Store datasets and run queries in the same location. Specify location="US"
or location="EU"
in client libraries to prevent costly cross-region copies.
Filter early, use subqueries or CTEs to restrict rows before joins, and join on partition and clustering keys to preserve pruning. Always qualify tables with project.dataset.table
to avoid accidental wildcard scans.
Materialized views cache results and auto-refresh, cutting cost on repeated reporting queries. Temporary CTEs are free but evaluate on every run. Use materialized views for dashboards refreshed many times a day.
Query PLAN and STAGE details in the UI reveal bytes scanned. Set custom cost controls with maximum_bytes_billed
in a job configuration to fail runaway queries automatically.
Create separate projects for dev, staging, and prod. Apply IAM roles minimally and use authorized views or column-level security to expose only necessary data to each group.
No. Clustering only affects how data is stored internally and does not change storage pricing.
You can select into a new partitioned table or use CREATE TABLE ... PARTITION BY
with a query. Direct ALTER is not supported.
Up to four. More than that offers diminishing returns and can slow write performance.