MongoDB Exercise - For each borough, find the number of restaurants with an 'A'

Write a MongoDB query to find the count of restaurants that received a grade of 'A' for each borough.

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"
  },
  {
    $match: { "grades.grade": "A" }
  },
  {
    $group: {
      _id: "$borough",
count: { $sum: 1 }
    }
  }
])

Output:

{ _id: 'Manhattan', count: 7447 },
{ _id: 'Staten Island', count: 575 },
{ _id: 'Queens', count: 2969 },
{ _id: 'Brooklyn', count: 2651 },
{ _id: 'Bronx', count: 1207 }

Explanation:

The said query in MongoDB that returns a list of documents, each representing a borough and the count of restaurants in that borough that have a grade of 'A'.

The $unwind stage is used to "unwind" an array field grades in the restaurants collection. As a result, a separate document will be created for each grade in the restaurants collection.

The $match stage is used to filter the documents that have a grade of 'A'. The $match stage uses the dot notation to access the grade field of the grades array field.

The $group stage of the pipeline creates a new document for each distinct value of borough and calculates the count of restaurants in that borough that have a grade of 'A' using the $sum accumulator operator.

Note: This output is generated using MongoDB server version 3.6

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