I need to compute distribution statistics (min, max, mean, median, spread) for all numeric columns in the dataset. From the evidence, I can see there are many numeric columns across different tables with types BIGINT and DOUBLE. Let me write a comprehensive query that profiles all numeric columns in one pass, computing the requested statistics. ```json { "action": "tool_call", "reasoning": "I need to compute min, max, mean, median, and spread (stddev) for all numeric columns. The evidence shows numeric columns across many tables. I'll write a single query that unions statistics from all numeric columns, using UNPIVOT or multiple SELECT statements to gather stats efficiently. Since there are 43 tables with many numeric columns, I'll query the metadata to dynamically generate statistics for all numeric columns.", "tool": "query_sql", "args": { "sql": "WITH numeric_cols AS (SELECT table_name, column_name, phys_type FROM meta.columns WHERE phys_type IN ('BIGINT', 'DOUBLE', 'DECIMAL', 'INTEGER', 'FLOAT') ORDER BY table_name, column_name LIMIT 100), stats AS (SELECT 'analysis_run_llm_usage' as table_name, 'calls' as column_name, MIN(calls) as min_val, MAX(calls) as max_val, AVG(calls) as mean_val, MEDIAN(calls) as median_val, STDDEV(calls) as stddev_val FROM raw.analysis_run_llm_usage UNION ALL SELECT 'analysis_run_llm_usage', 'cost_usd', MIN(cost_usd), MAX(cost_usd), AVG(cost_usd), MEDIAN(cost_usd), STDDEV(cost_usd) FROM raw.analysis_run_llm_usage UNION ALL SELECT 'analysis_run_llm_usage', 'input_tokens', MIN(input_tokens), MAX(input_tokens), AVG(input_tokens), MEDIAN(input_tokens), STDDEV(input_tokens) FROM raw.analysis_run_llm_usage UNION ALL SELECT 'analysis_run_llm_usage', 'output_tokens', MIN(output_tokens), MAX(output_tokens), AVG(output_tokens), MEDIAN(output_tokens), STDDEV(output_tokens) FROM raw.analysis_run_llm_usage UNION ALL SELECT 'analysis_runs', 'cost_compute_seconds', MIN(cost_compute_seconds), MAX(cost_compute_seconds), AVG(cost_compute_seconds), MEDIAN(cost_compute_seconds), STDDEV(cost_compute_seconds) FROM raw.analysis_runs UNION ALL SELECT 'analysis_runs', 'cost_storage_bytes', MIN(cost_storage_bytes), MAX(cost_storage_bytes), AVG(cost_storage_bytes), MEDIAN(cost_storage_bytes), STDDEV(cost_storage_bytes) FROM raw.analysis_runs UNION ALL SELECT 'analysis_runs', 'queries_executed', MIN(queries_executed), MAX(queries_executed), AVG(queries_executed), MEDIAN(queries_executed), STDDEV(queries_executed) FROM raw.analysis_runs UNION ALL SELECT 'analysis_runs', 'total_cost_usd', MIN(total_cost_usd), MAX(total_cost_usd), AVG(total_cost_usd), MEDIAN(total_cost_usd), STDDEV(total_cost_usd) FROM raw.analysis_runs UNION ALL SELECT 'analysis_runs', 'total_input_tokens', MIN(total_input_tokens), MAX(total_input_tokens), AVG(total_input_tokens), MEDIAN(total_input_tokens), STDDEV(total_input_tokens) FROM raw.analysis_runs UNION ALL SELECT 'analysis_runs', 'total_llm_calls', MIN(total_llm_calls), MAX(total_llm_calls), AVG(total_llm_calls), MEDIAN(total_llm_calls), STDDEV(total_llm_calls) FROM raw.analysis_runs UNION ALL SELECT 'analysis_runs', 'total_output_tokens', MIN(total_output_tokens), MAX(total_output_tokens), AVG(total_output_tokens), MEDIAN(total_output_tokens), STDDEV(total_output_tokens) FROM raw.analysis_runs UNION ALL SELECT 'analysis_runs', 'total_tool_calls', MIN(total_tool_calls), MAX(total_tool_calls), AVG(total_tool_calls), MEDIAN(total_tool_calls), STDDEV(total_tool_calls) FROM raw.analysis_runs UNION ALL SELECT 'batches', 'elapsed_ms', MIN(elapsed_ms), MAX(elapsed_ms), AVG(elapsed_ms), MEDIAN(elapsed_ms), STDDEV(elapsed_ms) FROM raw.batches UNION ALL SELECT 'batches', 'total_bytes', MIN(total_bytes), MAX(total_bytes), AVG(total_bytes), MEDIAN(total_bytes), STDDEV(total_bytes) FROM raw.batches UNION ALL SELECT 'batches', 'total_rows', MIN(total_rows), MAX(total_rows), AVG(total_rows), MEDIAN(total_rows), STDDEV(total_rows) FROM raw.batches) SELECT table_name, column_name, ROUND(min_val, 2) as min, ROUND(max_val, 2) as max, ROUND(mean_val, 2) as mean, ROUND(median_val, 2) as median, ROUND(stddev_val, 2) as spread FROM stats ORDER BY table_name, column_name", "purpose": "Compute distribution statistics (min, max, mean, median, stddev) for a sample of numeric columns across the dataset", "source": "raw" } } ```