Simple lookup by ID:
  PostgreSQL: 0.3ms
  MongoDB:    0.2ms (slightly faster)

Complex join (3 tables):
  PostgreSQL: 15ms
  MongoDB:    45ms ($lookup is slower)

Full-text search:
  PostgreSQL: 8ms
  MongoDB:    5ms (Atlas Search)

Aggregation pipeline:
  PostgreSQL: 120ms
  MongoDB:    95ms

Single insert:
  PostgreSQL: 0.5ms
  MongoDB:    0.3ms

Bulk insert (10K rows):
  PostgreSQL: 250ms
  MongoDB:    180ms

Update with conditions:
  PostgreSQL: 2ms
  MongoDB:    1.5ms

sql
-- PostgreSQL excels at complex queries
SELECT 
  u.name,
  COUNT(o.id) as order_count,
  SUM(o.total) as total_spent,
  AVG(r.rating) as avg_review
FROM users u
JOIN orders o ON u.id = o.user_id
LEFT JOIN reviews r ON u.id = r.user_id
WHERE o.created_at > NOW() - INTERVAL '1 year'
GROUP BY u.id
HAVING COUNT(o.id) > 5
ORDER BY total_spent DESC;

javascript
// MongoDB excels at nested document operations
db.products.find({
  "category": "electronics",
  "specs.ram": { $gte: 16 },
  "reviews": { $elemMatch: { rating: { $gte: 4 } } },
  "location": {
    $near: {
      $geometry: { type: "Point", coordinates: [-73.97, 40.77] },
      $maxDistance: 5000
    }
  }
}).sort({ "reviews.avg_rating": -1 });