Review BigQuery SQL for performance, cost, and optimization suggestions.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "sql-review-agent" yet — see the docs or source repo.
Please review the following BigQuery SQL, identify performance bottlenecks and possible full table scan issues, and provide rewrite suggestions with reasons: SELECT user_id, COUNT(*) FROM `project.dataset.events` WHERE DATE(event_time) >= '2024-01-01' GROUP BY user_id ORDER BY COUNT(*) DESC LIMIT 100;
Returns performance findings, explanations of inefficient patterns, and a more efficient rewritten SQL query.
Please evaluate the approximate scanned data volume and cost risk of this BigQuery query, and explain which filters or partition conditions could reduce cost: SELECT * FROM `project.dataset.orders` WHERE customer_region = 'APAC' AND status = 'paid';
Returns a cost risk assessment, rationale for scan estimates, and suggestions to reduce query cost.
Please rewrite this BigQuery SQL into a production-friendly version, prioritizing partition pruning, reducing scanned columns, and readability, and list the changes made: SELECT * FROM `project.dataset.sessions` WHERE EXTRACT(YEAR FROM session_start)=2024 AND country IN ('US','CA');Returns optimized SQL, a change-by-change explanation, and the expected performance improvement direction.
Query, analyze, and compare Google BigQuery data using natural language.
Query and explore BigQuery databases with natural language and schema discovery.
Analyze BigQuery search and analytics data with conclusions and actionable recommendations.
Automate code reviews with syntax checks, explanations, and improvement suggestions.
Query BigQuery in natural language with schema exploration and history tracking.
Review code or branches for correctness, compatibility, architecture, tests, performance, and security.