-- Fictional learning example. Run only in a fresh, disposable local database.
-- Verified with SQLite. No real customer or production records.
CREATE TEMP TABLE repair_jobs (
  job_id INTEGER PRIMARY KEY,
  service TEXT NOT NULL,
  status TEXT NOT NULL,
  estimated_minutes INTEGER
);
INSERT INTO repair_jobs VALUES
(101, 'tune_up', 'queued', 45),
(102, 'puncture', 'ready', 20),
(103, 'tune_up', 'queued', 60),
(104, 'brake_adjust', 'queued', 30),
(105, 'puncture', 'cancelled', 15),
(106, 'tune_up', 'queued', NULL);

SELECT job_id, service, estimated_minutes
FROM repair_jobs
WHERE status = 'queued'
ORDER BY job_id;

SELECT service,
       COUNT(*) AS jobs,
       COUNT(estimated_minutes) AS estimates_known,
       SUM(estimated_minutes) AS minutes_known
FROM repair_jobs
WHERE status = 'queued'
GROUP BY service
ORDER BY jobs DESC, service;

-- Expected IDs: 101, 103, 104, 106
-- Expected summary: tune_up | 3 | 2 | 105; brake_adjust | 1 | 1 | 30
-- Overall: 4 queued jobs, 3 known estimates, 135 known estimated minutes.