scripts/optimization/queries_grouped_by_labels.sql (39 lines of code) (raw):

DECLARE num_days_to_scan INT64 DEFAULT 30; CREATE TEMP FUNCTION num_stages_with_perf_insights(query_info ANY TYPE) AS ( COALESCE(( SELECT SUM(IF(i.slot_contention, 1, 0) + IF(i.insufficient_shuffle_quota, 1, 0)) FROM UNNEST(query_info.performance_insights.stage_performance_standalone_insights) i), 0) + COALESCE(ARRAY_LENGTH(query_info.performance_insights.stage_performance_change_insights), 0) ); CREATE OR REPLACE TABLE optimization_workshop.queries_grouped_by_labels AS SELECT FORMAT("%p", JSON_ARRAY(labels)) AS labels, COUNT(DISTINCT DATE(start_time)) AS days_active, ARRAY_AGG(DISTINCT project_id IGNORE NULLS) AS project_ids, ARRAY_AGG(DISTINCT reservation_id IGNORE NULLS) AS reservation_ids, SUM(num_stages_with_perf_insights(query_info)) AS num_stages_with_perf_insights, COUNT(DISTINCT (project_id || ':us.' || job_id)) AS job_count, ARRAY_AGG( STRUCT( bqutil.fn.job_url(project_id || ':us.' || parent_job_id) AS parent_job_url, bqutil.fn.job_url(project_id || ':us.' || job_id) AS job_url ) ORDER BY total_slot_ms DESC LIMIT 10) AS top_10_job_urls, ARRAY_AGG(DISTINCT user_email) AS user_emails, SUM(total_bytes_processed) / POW(1024, 3) AS total_gigabytes_processed, AVG(total_bytes_processed) / POW(1024, 3) AS avg_gigabytes_processed, SUM(total_slot_ms) / (1000 * 60 * 60) AS total_slot_hours, SUM(total_slot_ms) / (1000 * 60 * 60) / COUNT(DISTINCT DATE(start_time)) AS avg_total_slot_hours_per_active_day, AVG(TIMESTAMP_DIFF(end_time, start_time, SECOND) ) AS avg_job_duration_seconds, SUM(total_slot_ms / TIMESTAMP_DIFF(end_time, start_time, MILLISECOND)) AS total_slots, AVG(total_slot_ms / TIMESTAMP_DIFF(end_time, start_time, MILLISECOND)) AS avg_total_slots, FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_ORGANIZATION WHERE DATE(creation_time) >= CURRENT_DATE - num_days_to_scan AND state = 'DONE' AND error_result IS NULL AND job_type = 'QUERY' AND statement_type != 'SCRIPT' AND user_email LIKE '%gserviceaccount.com' GROUP BY labels;