MongoDB Exercise - Find the top 5 restaurants for each cuisine type, along with their average score

Write a MongoDB query to find the top 5 restaurants with the highest average score for each cuisine type, along with their average scores.

Structure of 'restaurants' collection :

{
  "address": {
     "building": "1007",
     "coord": [ -73.856077, 40.848447 ],
     "street": "Morris Park Ave",
     "zipcode": "10462"
  },
  "borough": "Bronx",
  "cuisine": "Bakery",
  "grades": [
     { "date": { "$date": 1393804800000 }, "grade": "A", "score": 2 },
     { "date": { "$date": 1378857600000 }, "grade": "A", "score": 6 },
     { "date": { "$date": 1358985600000 }, "grade": "A", "score": 10 },
     { "date": { "$date": 1322006400000 }, "grade": "A", "score": 9 },
     { "date": { "$date": 1299715200000 }, "grade": "B", "score": 14 }
  ],
  "name": "Morris Park Bake Shop",
  "restaurant_id": "30075445"
}

Query:

db.restaurants.aggregate([
  {$unwind: "$grades"},
  {$group: {
    _id: {cuisine: "$cuisine", restaurant_id: "$restaurant_id"},
avgScore: {$avg: "$grades.score"}
  }},
  {$sort: {
    "_id.cuisine": 1,
avgScore: -1
  }},
  {$group: {
    _id: "$_id.cuisine",
topRestaurants: {$push: {restaurant_id: "$_id.restaurant_id", avgScore: "$avgScore"}}
  }},
  {$project: {
    _id: 0,
cuisine: "$_id",
topRestaurants: {$slice: ["$topRestaurants", 5]}
  }}
])

Output:

{
cuisine: 'Bagels/Pretzels',
topRestaurants: [
{ restaurant_id: '40396464', avgScore: 20.2 },
{ restaurant_id: '40363565', avgScore: 19.166666666666668 },
{ restaurant_id: '40667700', avgScore: 14.5 },
{ restaurant_id: '40759924', avgScore: 14.4 },
{ restaurant_id: '40392339', avgScore: 14.285714285714286 }
    ]
  },
  {
cuisine: 'Latin (Cuban, Dominican, Puerto Rican, South & Central American)',
topRestaurants: [
{ restaurant_id: '40596377', avgScore: 26.666666666666668 },
{ restaurant_id: '40582271', avgScore: 25.285714285714285 },
{ restaurant_id: '40393688', avgScore: 22.625 },
{ restaurant_id: '40791454', avgScore: 21.5 },
{ restaurant_id: '40743578', avgScore: 21.2 }
    ]
  },
  {
cuisine: 'Vietnamese/Cambodian/Malaysia',
topRestaurants: [
{ restaurant_id: '40700664', avgScore: 27.833333333333332 },
{ restaurant_id: '40578058', avgScore: 15.2 },
{ restaurant_id: '40751226', avgScore: 13.833333333333334 },
{ restaurant_id: '40559606', avgScore: 8.6 }
    ]
  },
.....

Explanation:

The given query in MongoDB that finds the top 5 restaurants with the highest average score for each cuisine type.

The $unwind stage is used to create a separate document for each element in the grades array.

The $group stage is used to group the documents by cuisine and restaurant_id, and calculate the average score using the $avg aggregation operator.

The _id field in the $group stage is a composite key that consists of the cuisine and restaurant_id fields.

The $sort sorts the documents by cuisine and average score in descending order.

The $group stage is used again to group the documents by cuisine and create an array of the top 5 restaurants for each cuisine using the $push aggregation operator.

The $slice operator limits the size of the topRestaurants array to 5.

Note: This output is generated using MongoDB server version 3.6

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