Our ERP dashboard makes API calls to BigQuery for real-time reporting on sales, inventory, and financial data. Queries that join 5-6 large tables (each with 50M+ rows) are taking 45-90 seconds to return results via the BigQuery API, making the dashboard unusable.
The queries aggregate data across multiple dimensions (product, region, time period, customer segment) and include complex calculations. We’ve tried adding WHERE clauses to filter data, but response times are still unacceptable for a user-facing dashboard. The slow response is severely impacting dashboard performance and user experience.
SELECT p.category, r.region, SUM(s.amount)
FROM sales s JOIN products p ON s.product_id=p.id
JOIN regions r ON s.region_id=r.id
WHERE s.sale_date >= '2025-01-01'
GROUP BY p.category, r.region
Users expect sub-5-second response times. How can we optimize BigQuery query performance for complex ERP reporting through the API?
Also check your query execution plan in the BigQuery console. Look for stages with high slot time or shuffle operations. Sometimes rewriting the query to use different join orders or adding explicit clustering can help the optimizer choose better execution plans.
Nina, the sales table is already partitioned by sale_date (DATE type). The WHERE clause should be pruning partitions, but query execution still takes 45+ seconds. Could the joins be the bottleneck? The products and regions tables aren’t partitioned since they’re relatively small (10K rows each).
Your query is scanning massive tables without partitioning. First step: partition your sales table by sale_date. This will dramatically reduce the amount of data scanned. Also check if you’re using TIMESTAMP or DATE for partitioning - DATE partitioning is more efficient for daily aggregations.
Sam, denormalization makes sense but our ERP system writes normalized data. Changing the write path would be a major effort. Is there a way to maintain normalized tables but still get fast query performance? Maybe pre-aggregating data somehow?
Materialized views are perfect for this use case. Create a materialized view that pre-computes your common aggregations and joins. BigQuery will incrementally refresh it as new data arrives. Your API queries then hit the materialized view instead of the base tables, giving you sub-second response times. Combine with BI Engine for even faster performance.