MongoDB Lab Manual – BDS456B

Prabodh C P

MongoDB Laboratory (BDS456B)

In this blog post, you will find solutions for the MongoDB Laboratory (BDS456B) course work for the IV semester of VTU university. To follow along, you will need to have up a machine running any flavour of GNULinux OS. This blog provides instructions to get MongoDB installed on your system. The solutions have been tested on Ubuntu 22.04 OS. You can find the lab syllabus on the university’s website or here below.

BDS456B_MongoDBDownload

All these solutions have been maintained at the following git repository shown below. If you want to contribute send me a PR.

https://gitlab.com/lab_manuals/current/iv-semester/bds456b_mongodb

The following blog shows how to install and configure MongoDB on Ubuntu step-by-step to power your application’s database. This comprehensive guide covers everything from installation and service setup to basic configuration and optional security measures. By following these instructions, you’ll have MongoDB up and running smoothly on your Ubuntu machine, ready to support your applications data needs efficiently and securely.

Setting Up MongoDB on Ubuntu

After getting the necessary development environment setup, Now lets focus on the solutions. For all programs the MongoDB server should be up and running which can be done as follows.

1. Start MongoDB.

Open a terminal and type the following.

sudo systemctl start mongod

2. Begin using MongoDB.

To begin using MongoDB start the MongoDB Shell.

mongosh

Now you will see a MongoDB shell, where you can issue the queries.

Solutions to lab exercises

PART A

Question 1

MongoDB Operations

b. Execute the Commands of MongoDB and operations in MongoDB : Insert, Query, Update, Delete and Projection. (Note: use any collection)

(part a is answered after part b)

Switch to a Database (Optional):

If you want to use a specific database, switch to that database using the use command. If the database doesn’t exist, MongoDB will create it implicitly when you insert data into it:

test> use ProgBooksDB
switched to db ProgBooksDB
ProgBooksDB>

 Create the ProgrammingBooks Collection:

To create the ProgrammingBooks collection, use the createCollection() method. This step is optional because MongoDB will automatically create the collection when you insert data into it, but you can explicitly create it if needed:

ProgBooksDB> db.createCollection("ProgrammingBooks")

Insert operations

Insert a Single Document into ProgrammingBooks:

Use the insertOne() method to insert a new document into the ProgrammingBooks collection:

ProgBooksDB>  db.ProgrammingBooks.insertOne({
  title: "The Pragmatic Programmer: Your Journey to Mastery",
  author: "David Thomas, Andrew Hunt",
  category: "Software Development",
  year: 1999
})
Insert multiple Documents into the ProgrammingBooks Collection :

Now, insert 5 documents representing programming books into the ProgrammingBooks collection using the insertMany() method:

ProgBooksDB> db.ProgrammingBooks.insertMany([
  {
    title: "Clean Code: A Handbook of Agile Software Craftsmanship",
    author: "Robert C. Martin",
    category: "Software Development",
    year: 2008
  },
  {
    title: "JavaScript: The Good Parts",
    author: "Douglas Crockford",
    category: "JavaScript",
    year: 2008
  },
  {
    title: "Design Patterns: Elements of Reusable Object-Oriented Software",
    author: "Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides",
    category: "Software Design",
    year: 1994
  },
  {
    title: "Introduction to Algorithms",
    author: "Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein",
    category: "Algorithms",
    year: 1990
  },
  {
    title: "Python Crash Course: A Hands-On, Project-Based Introduction to Programming",
    author: "Eric Matthes",
    category: "Python",
    year: 2015
  }
])

Query operations

Find All Documents

To retrieve all documents from the ProgrammingBooks collection:

ProgBooksDB> db.ProgrammingBooks.find().pretty()
[
  {
    _id: ObjectId('664ee3b1924a8039f62202d8'),
    title: 'The Pragmatic Programmer: Your Journey to Mastery',
    author: 'David Thomas, Andrew Hunt',
    category: 'Software Development',
    year: 1999
  },
  {
    _id: ObjectId('664ee452924a8039f62202d9'),
    title: 'Clean Code: A Handbook of Agile Software Craftsmanship',
    author: 'Robert C. Martin',
    category: 'Software Development',
    year: 2008
  },
  {
    _id: ObjectId('664ee452924a8039f62202da'),
    title: 'JavaScript: The Good Parts',
    author: 'Douglas Crockford',
    category: 'JavaScript',
    year: 2008
  },
  {
    _id: ObjectId('664ee452924a8039f62202db'),
    title: 'Design Patterns: Elements of Reusable Object-Oriented Software',
    author: 'Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides',
    category: 'Software Design',
    year: 1994
  },
  {
    _id: ObjectId('664ee452924a8039f62202dc'),
    title: 'Introduction to Algorithms',
    author: 'Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein',
    category: 'Algorithms',
    year: 1990
  },
  {
    _id: ObjectId('664ee452924a8039f62202dd'),
    title: 'Python Crash Course: A Hands-On, Project-Based Introduction to Programming',
    author: 'Eric Matthes',
    category: 'Python',
    year: 2015
  }
]
Find Documents Matching a Condition

To find books published after the year 2000:

ProgBooksDB> db.ProgrammingBooks.find({ year: { $gt: 2000 } }).pretty()
[
  {
    _id: ObjectId('664ee452924a8039f62202d9'),
    title: 'Clean Code: A Handbook of Agile Software Craftsmanship',
    author: 'Robert C. Martin',
    category: 'Software Development',
    year: 2008
  },
  {
    _id: ObjectId('664ee452924a8039f62202da'),
    title: 'JavaScript: The Good Parts',
    author: 'Douglas Crockford',
    category: 'JavaScript',
    year: 2008
  },
  {
    _id: ObjectId('664ee452924a8039f62202dd'),
    title: 'Python Crash Course: A Hands-On, Project-Based Introduction to Programming',
    author: 'Eric Matthes',
    category: 'Python',
    year: 2015
  }
]

Update Operations

a. Update a Single Document

To update a specific book (e.g., change the author of a book):

ProgBooksDB>db.ProgrammingBooks.updateOne(
  { title: "Clean Code: A Handbook of Agile Software Craftsmanship" },
  { $set: { author: "Robert C. Martin (Uncle Bob)" } }
)

//verify by displaying books published in year 2008
ProgBooksDB> db.ProgrammingBooks.find({ year: { $eq: 2008 } }).pretty()
[
  {
    _id: ObjectId('663eaaebae582498972202df'),
    title: 'Clean Code: A Handbook of Agile Software Craftsmanship',
    author: 'Robert C. Martin (Uncle Bob)',
    category: 'Software Development',
    year: 2008
  },
  {
    _id: ObjectId('663eaaebae582498972202e0'),
    title: 'JavaScript: The Good Parts',
    author: 'Douglas Crockford',
    category: 'JavaScript',
    year: 2008
  }
]

//another way to verify
ProgBooksDB> db.ProgrammingBooks.find({ author: { $regex: "Robert*" } }).pretty()
[
  {
    _id: ObjectId('664ee452924a8039f62202d9'),
    title: 'Clean Code: A Handbook of Agile Software Craftsmanship',
    author: 'Robert C. Martin (Uncle Bob)',
    category: 'Software Development',
    year: 2008
  }
]
b. Update Multiple Documents

To update multiple books (e.g., update the category of books published before 2010):

ProgBooksDB> db.ProgrammingBooks.updateMany(
  { year: { $lt: 2010 } },
  { $set: { category: "Classic Programming Books" } }
)

//verify the update operation by displaying books published before year 2010
ProgBooksDB> db.ProgrammingBooks.find({ year: { $lt: 2010 } }).pretty()
[
  {
    _id: ObjectId('663eaaebae582498972202df'),
    title: 'Clean Code: A Handbook of Agile Software Craftsmanship',
    author: 'Robert C. Martin (Uncle Bob)',
    category: 'Classic Programming Books',
    year: 2008
  },
  {
    _id: ObjectId('663eaaebae582498972202e0'),
    title: 'JavaScript: The Good Parts',
    author: 'Douglas Crockford',
    category: 'Classic Programming Books',
    year: 2008
  },
  {
    _id: ObjectId('663eaaebae582498972202e1'),
    title: 'Design Patterns: Elements of Reusable Object-Oriented Software',
    author: 'Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides',
    category: 'Classic Programming Books',
    year: 1994
  },
  {
    _id: ObjectId('663eaaebae582498972202e2'),
    title: 'Introduction to Algorithms',
    author: 'Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein',
    category: 'Classic Programming Books',
    year: 1990
  },
  {
    _id: ObjectId('663eab05ae582498972202e4'),
    title: 'The Pragmatic Programmer: Your Journey to Mastery',
    author: 'David Thomas, Andrew Hunt',
    category: 'Classic Programming Books',
    year: 1999
  }
]

Delete Operations

Delete a Single Document

To delete a specific book from the collection (e.g., delete a book by title):

ProgBooksDB> db.ProgrammingBooks.deleteOne({ title: "JavaScript: The Good Parts" })

{ acknowledged: true, deletedCount: 1 }

//Verify to see document is deleted
ProgBooksDB> db.ProgrammingBooks.find({ title: "JavaScript: The Good Parts" }).pretty()
Delete Multiple Documents

To delete multiple books based on a condition (e.g., delete all books published before 1995):

ProgBooksDB> db.ProgrammingBooks.deleteMany({ year: { $lt: 1995 } })
{ acknowledged: true, deletedCount: 2 }

You can check whether the specified documents were deleted by displaying the contents of the collection.

Delete All Documents in the Collection:

To delete all documents in a collection (e.g., ProgrammingBooks), use the deleteMany() method with an empty filter {}:

//delete all documents in a collection
ProgBooksDB> db.ProgrammingBooks.deleteMany({})
{ acknowledged: true, deletedCount: 3 }

//verify by displaying the collection
ProgBooksDB> db.ProgrammingBooks.find().pretty()

Projection Operations

In MongoDB, a projection refers to the mechanism of specifying which fields (or columns) should be returned from a query result. When querying a collection, you can use projection to control the shape of the returned documents by specifying which fields to include or exclude.

In MongoDB, projection is typically specified as the second parameter to the find() method. The projection parameter takes an object where keys represent the fields to include or exclude, with values of 1 (include) or 0 (exclude).

Include Specific Fields:

Use 1 to include a field in the result:

ProgBooksDB> db.ProgrammingBooks.find({}, { title: 1, author: 1 } )
[
  {
    _id: ObjectId('665163289edbdf91e12202dd'),
    title: 'Clean Code: A Handbook of Agile Software Craftsmanship',
    author: 'Robert C. Martin'
  },
  {
    _id: ObjectId('665163289edbdf91e12202e1'),
    title: 'Python Crash Course: A Hands-On, Project-Based Introduction to Programming',
    author: 'Eric Matthes'
  }
]

Exclude Specific Fields:

Use 0 to exclude a field from the result:

ProgBooksDB> db.ProgrammingBooks.find({}, {year: 0})
[
  {
    _id: ObjectId('665163289edbdf91e12202dd'),
    title: 'Clean Code: A Handbook of Agile Software Craftsmanship',
    author: 'Robert C. Martin',
    category: 'Software Development'
  },
  {
    _id: ObjectId('665163289edbdf91e12202e1'),
    title: 'Python Crash Course: A Hands-On, Project-Based Introduction to Programming',
    author: 'Eric Matthes',
    category: 'Python'
  }
]

Where Clause, AND,OR operations in MongoDB.

a. Illustration of Where Clause, AND,OR operations in MongoDB.

In MongoDB, the equivalent of SQL’s WHERE clause is achieved using query filters within the find() method. You can also combine multiple conditions using logical operators like $and and $or. Here’s how you can illustrate the usage of these features:

Setting Up Example Data

First, let’s assume we have a collection named ProgrammingBooks with the following documents:

ProgBooksDB> use newDB
switched to db newDB
newDB> db.createCollection("ProgrammingBooks")
{ ok: 1 }
newDB> db.ProgrammingBooks.insertMany([
  { title: "Clean Code", author: "Robert C. Martin", category: "Software Development", year: 2008 },
  { title: "JavaScript: The Good Parts", author: "Douglas Crockford", category: "JavaScript", year: 2008 },
  { title: "Design Patterns", author: "Erich Gamma", category: "Software Design", year: 1994 },
  { title: "Introduction to Algorithms", author: "Thomas H. Cormen", category: "Algorithms", year: 2009 },
  { title: "Python Crash Course", author: "Eric Matthes", category: "Python", year: 2015 }
]);

{
  acknowledged: true,
  insertedIds: {
    '0': ObjectId('6651daad9edbdf91e12202e2'),
    '1': ObjectId('6651daad9edbdf91e12202e3'),
    '2': ObjectId('6651daad9edbdf91e12202e4'),
    '3': ObjectId('6651daad9edbdf91e12202e5'),
    '4': ObjectId('6651daad9edbdf91e12202e6')
  }
}

Using the WHERE Clause Equivalent

To query documents with specific conditions, you can use the find() method with a filter object. For example, to find books published in the year 2008:

newDB> db.ProgrammingBooks.find({ year: 2008 }).pretty()
[
  {
    _id: ObjectId('6651daad9edbdf91e12202e2'),
    title: 'Clean Code',
    author: 'Robert C. Martin',
    category: 'Software Development',
    year: 2008
  },
  {
    _id: ObjectId('6651daad9edbdf91e12202e3'),
    title: 'JavaScript: The Good Parts',
    author: 'Douglas Crockford',
    category: 'JavaScript',
    year: 2008
  }
]

Using the $and Operator

The $and operator is used to combine multiple conditions that must all be true. Here’s how to find books that are in the “Software Development” category and published in the year 2008:

newDB>db.ProgrammingBooks.find({
  $and: [
    { category: "Software Development" },
    { year: 2008 }
  ]
}).pretty()

[
  {
    _id: ObjectId('6651daad9edbdf91e12202e2'),
    title: 'Clean Code',
    author: 'Robert C. Martin',
    category: 'Software Development',
    year: 2008
  }
]

In this query:

  • Both conditions must be met for a document to be included in the result.

Using the $or Operator

The $or operator is used to combine multiple conditions where at least one must be true. Here’s how to find books that are either in the “JavaScript” category or published in the year 2015:

newDB> db.ProgrammingBooks.find({
  $or: [
    { category: "JavaScript" },
    { year: 2015 }
  ]
}).pretty()

[
  {
    _id: ObjectId('6651daad9edbdf91e12202e3'),
    title: 'JavaScript: The Good Parts',
    author: 'Douglas Crockford',
    category: 'JavaScript',
    year: 2008
  },
  {
    _id: ObjectId('6651daad9edbdf91e12202e6'),
    title: 'Python Crash Course',
    author: 'Eric Matthes',
    category: 'Python',
    year: 2015
  }
]

In this query:

  • A document will be included in the result if it meets either condition.

Combining $and and $or Operators

You can combine $and and $or operators for more complex queries. For example, to find books that are either in the “Software Development” category and published after 2007, or in the “Python” category:

newDB> db.ProgrammingBooks.find({
  $or: [
    {
      $and: [
        { category: "Software Development" },
        { year: { $gt: 2007 } }
      ]
    },
    { category: "Python" }
  ]
}).pretty()

[
  {
    _id: ObjectId('6651daad9edbdf91e12202e2'),
    title: 'Clean Code',
    author: 'Robert C. Martin',
    category: 'Software Development',
    year: 2008
  },
  {
    _id: ObjectId('6651daad9edbdf91e12202e6'),
    title: 'Python Crash Course',
    author: 'Eric Matthes',
    category: 'Python',
    year: 2015
  }
]

In this query:

  • The document will be included if it meets the combined $and conditions of being in the “Software Development” category and published after 2007, or if it is in the “Python” category.

Question 2

a. Select and ignore fields

Develop a MongoDB query to select certain fields and ignore some fields of the documents from any collection.

To select certain fields and ignore others in MongoDB, you use projections in your queries. Projections allow you to specify which fields to include or exclude in the returned documents.

Create database and create the Collection:

test> use MoviesDB
switched to db MoviesDB
MoviesDB> db.createCollection("Movies")
{ ok: 1 }
MoviesDB> db.Movies.insertMany([
  { title: "Inception", director: "Christopher Nolan", genre: "Science Fiction", year: 2010, ratings: { imdb: 8.8, rottenTomatoes: 87 } },
  { title: "The Matrix", director: "Wachowskis", genre: "Science Fiction", year: 1999, ratings: { imdb: 8.7, rottenTomatoes: 87 } },
  { title: "The Godfather", director: "Francis Ford Coppola", genre: "Crime", year: 1972, ratings: { imdb: 9.2, rottenTomatoes: 97 } }
]);

{
  acknowledged: true,
  insertedIds: {
    '0': ObjectId('66523751d5449c3abf2202d8'),
    '1': ObjectId('66523751d5449c3abf2202d9'),
    '2': ObjectId('66523751d5449c3abf2202da')
  }
}

Basic Syntax for Projection

When using the find() method, the first parameter is the query filter, and the second parameter is the projection object. The projection object specifies the fields to include (using 1) or exclude (using 0).

Including Specific Fields

To include specific fields, set the fields you want to include to 1:

To select only the title and director fields from the Movies collection:

MoviesDB> db.Movies.find({}, { title: 1, director: 1 })
[
  {
    _id: ObjectId('66523751d5449c3abf2202d8'),
    title: 'Inception',
    director: 'Christopher Nolan'
  },
  {
    _id: ObjectId('66523751d5449c3abf2202d9'),
    title: 'The Matrix',
    director: 'Wachowskis'
  },
  {
    _id: ObjectId('66523751d5449c3abf2202da'),
    title: 'The Godfather',
    director: 'Francis Ford Coppola'
  }
]
MoviesDB> db.Movies.find({}, { title: 1, director: 1, _id: 0 })
[
  { title: 'Inception', director: 'Christopher Nolan' },
  { title: 'The Matrix', director: 'Wachowskis' },
  { title: 'The Godfather', director: 'Francis Ford Coppola' }
]

In this query:

  • The filter {} means we want to select all documents.
  • The projection { title: 1, director: 1, _id: 0 } means we include the title and director fields, and exclude the _id field (which is included by default unless explicitly excluded).

Excluding Specific Fields

To exclude specific fields, set the fields you want to exclude to 0:

To exclude the ratings field from the results:

MoviesDB> db.Movies.find({}, { ratings: 0 })
[
  {
    _id: ObjectId('66523751d5449c3abf2202d8'),
    title: 'Inception',
    director: 'Christopher Nolan',
    genre: 'Science Fiction',
    year: 2010
  },
  {
    _id: ObjectId('66523751d5449c3abf2202d9'),
    title: 'The Matrix',
    director: 'Wachowskis',
    genre: 'Science Fiction',
    year: 1999
  },
  {
    _id: ObjectId('66523751d5449c3abf2202da'),
    title: 'The Godfather',
    director: 'Francis Ford Coppola',
    genre: 'Crime',
    year: 1972
  }
]

In this query:

  • The filter {} means we want to select all documents.
  • The projection { ratings: 0 } means we exclude the ratings field.

Combining Filter and Projection

You can also combine a query filter with a projection. For example, to find movies directed by “Christopher Nolan” and include only the title and year fields:

MoviesDB> db.Movies.find({ director: "Christopher Nolan" }, { title: 1, year: 1, _id: 0 })
[ { title: 'Inception', year: 2010 } ]

In this query:

  • The filter { director: "Christopher Nolan" } selects documents where the director is “Christopher Nolan”.
  • The projection { title: 1, year: 1, _id: 0 } includes only the title and year fields and excludes the _id field.

In MongoDB, projections are used to control which fields are included or excluded in the returned documents. This is useful for optimizing queries and reducing the amount of data transferred over the network. You specify projections as the second parameter in the find() method.

b. Use of limit and find in MongoDB query

Develop a MongoDB query to display the first 5 documents from the results obtained in a. (illustrate use of limit and find)

To display the first 5 documents from a query result in MongoDB, you can use the limit() method in conjunction with the find() method. The limit() method restricts the number of documents returned by the query to the specified number.

Example Scenario

Assume we have the Movies collection as described previously:

test> use MoviesDB
switched to db MoviesDB
MoviesDB> db.createCollection("Movies")
{ ok: 1 }

MoviesDB>db.Movies.insertMany([
  { title: "Inception", director: "Christopher Nolan", genre: "Science Fiction", year: 2010, ratings: { imdb: 8.8, rottenTomatoes: 87 } },
  { title: "The Matrix", director: "Wachowskis", genre: "Science Fiction", year: 1999, ratings: { imdb: 8.7, rottenTomatoes: 87 } },
  { title: "The Godfather", director: "Francis Ford Coppola", genre: "Crime", year: 1972, ratings: { imdb: 9.2, rottenTomatoes: 97 } },
  { title: "Pulp Fiction", director: "Quentin Tarantino", genre: "Crime", year: 1994, ratings: { imdb: 8.9, rottenTomatoes: 92 } },
  { title: "The Shawshank Redemption", director: "Frank Darabont", genre: "Drama", year: 1994, ratings: { imdb: 9.3, rottenTomatoes: 91 } },
  { title: "The Dark Knight", director: "Christopher Nolan", genre: "Action", year: 2008, ratings: { imdb: 9.0, rottenTomatoes: 94 } },
  { title: "Fight Club", director: "David Fincher", genre: "Drama", year: 1999, ratings: { imdb: 8.8, rottenTomatoes: 79 } }
]);

Query with Projection and Limit

Suppose you want to display the first 5 documents from the Movies collection, including only the title, director, and year fields. Here’s how you can do it:

MoviesDB> db.Movies.find({}, { title: 1, director: 1, year: 1, _id: 0 }).limit(5)

[
  { "title": "Inception", "director": "Christopher Nolan", "year": 2010 },
  { "title": "The Matrix", "director": "Wachowskis", "year": 1999 },
  { "title": "The Godfather", "director": "Francis Ford Coppola", "year": 1972 },
  { "title": "Pulp Fiction", "director": "Quentin Tarantino", "year": 1994 },
  { "title": "The Shawshank Redemption", "director": "Frank Darabont", "year": 1994 }
]

Explanation:

  • find({}): This filter {} selects all documents in the collection.
  • { title: 1, director: 1, year: 1, _id: 0 }: This projection includes the title, director, and year fields, and excludes the _id field.
  • .limit(5): This method limits the query result to the first 5 documents.

By using the find() method with a projection and the limit() method, you can efficiently query and display a subset of documents from a MongoDB collection. This approach helps manage large datasets by retrieving only a specific number of documents, which is particularly useful for paginating results in applications.

Question 3

a. Query selectors (comparison selectors, logical selectors )

Execute query selectors (comparison selectors, logical selectors ) and list out the results on any collection

Let’s create a new collection called Employees and insert some documents into it. Then, we’ll demonstrate the use of comparison selectors and logical selectors to query this collection.

Create the Employees Collection and Insert Documents

First, we need to create the Employees collection and insert some sample documents.

test> use companyDB

companyDB> db.Employees.insertMany([
  { name: "Alice", age: 30, department: "HR", salary: 50000, joinDate: new Date("2015-01-15") },
  { name: "Bob", age: 24, department: "Engineering", salary: 70000, joinDate: new Date("2019-03-10") },
  { name: "Charlie", age: 29, department: "Engineering", salary: 75000, joinDate: new Date("2017-06-23") },
  { name: "David", age: 35, department: "Marketing", salary: 60000, joinDate: new Date("2014-11-01") },
  { name: "Eve", age: 28, department: "Finance", salary: 80000, joinDate: new Date("2018-08-19") }
])

{
  acknowledged: true,
  insertedIds: {
    '0': ObjectId('665356cff5b334bcf92202d8'),
    '1': ObjectId('665356cff5b334bcf92202d9'),
    '2': ObjectId('665356cff5b334bcf92202da'),
    '3': ObjectId('665356cff5b334bcf92202db'),
    '4': ObjectId('665356cff5b334bcf92202dc')
  }
}

Queries Using Comparison Selectors

1. $eq (Equal)

Find employees in the “Engineering” department.

companyDB> db.Employees.find({ department: { $eq: "Engineering" } }).pretty()
[
  {
    _id: ObjectId('665356cff5b334bcf92202d9'),
    name: 'Bob',
    age: 24,
    department: 'Engineering',
    salary: 70000,
    joinDate: ISODate('2019-03-10T00:00:00.000Z')
  },
  {
    _id: ObjectId('665356cff5b334bcf92202da'),
    name: 'Charlie',
    age: 29,
    department: 'Engineering',
    salary: 75000,
    joinDate: ISODate('2017-06-23T00:00:00.000Z')
  }
]

2. $ne (Not Equal)

Find employees who are not in the “HR” department.

companyDB> db.Employees.find({ department: { $ne: "HR" } }).pretty()
[
  {
    _id: ObjectId('665356cff5b334bcf92202d9'),
    name: 'Bob',
    age: 24,
    department: 'Engineering',
    salary: 70000,
    joinDate: ISODate('2019-03-10T00:00:00.000Z')
  },
  {
    _id: ObjectId('665356cff5b334bcf92202da'),
    name: 'Charlie',
    age: 29,
    department: 'Engineering',
    salary: 75000,
    joinDate: ISODate('2017-06-23T00:00:00.000Z')
  },
  {
    _id: ObjectId('665356cff5b334bcf92202db'),
    name: 'David',
    age: 35,
    department: 'Marketing',
    salary: 60000,
    joinDate: ISODate('2014-11-01T00:00:00.000Z')
  },
  {
    _id: ObjectId('665356cff5b334bcf92202dc'),
    name: 'Eve',
    age: 28,
    department: 'Finance',
    salary: 80000,
    joinDate: ISODate('2018-08-19T00:00:00.000Z')
  }
]

3. $gt (Greater Than)

Find employees who are older than 30.

companyDB> db.Employees.find({ age: { $gt: 30 } }).pretty()
[
  {
    _id: ObjectId('665356cff5b334bcf92202db'),
    name: 'David',
    age: 35,
    department: 'Marketing',
    salary: 60000,
    joinDate: ISODate('2014-11-01T00:00:00.000Z')
  }
]

4. $lt (Less Than)

Find employees with a salary less than 70000.

companyDB> db.Employees.find({ salary: { $lt: 70000 } }).pretty()
[
  {
    _id: ObjectId('665356cff5b334bcf92202d8'),
    name: 'Alice',
    age: 30,
    department: 'HR',
    salary: 50000,
    joinDate: ISODate('2015-01-15T00:00:00.000Z')
  },
  {
    _id: ObjectId('665356cff5b334bcf92202db'),
    name: 'David',
    age: 35,
    department: 'Marketing',
    salary: 60000,
    joinDate: ISODate('2014-11-01T00:00:00.000Z')
  }
]

5. $gte (Greater Than or Equal)

Find employees who joined on or after January 1, 2018.

companyDB> db.Employees.find({ joinDate: { $gte: new Date("2018-01-01") } }).pretty()
[
  {
    _id: ObjectId('665356cff5b334bcf92202d9'),
    name: 'Bob',
    age: 24,
    department: 'Engineering',
    salary: 70000,
    joinDate: ISODate('2019-03-10T00:00:00.000Z')
  },
  {
    _id: ObjectId('665356cff5b334bcf92202dc'),
    name: 'Eve',
    age: 28,
    department: 'Finance',
    salary: 80000,
    joinDate: ISODate('2018-08-19T00:00:00.000Z')
  }
]

6. $lte (Less Than or Equal)

Find employees who are 28 years old or younger.

companyDB> db.Employees.find({ age: { $lte: 28 } }).pretty()
[
  {
    _id: ObjectId('665356cff5b334bcf92202d9'),
    name: 'Bob',
    age: 24,
    department: 'Engineering',
    salary: 70000,
    joinDate: ISODate('2019-03-10T00:00:00.000Z')
  },
  {
    _id: ObjectId('665356cff5b334bcf92202dc'),
    name: 'Eve',
    age: 28,
    department: 'Finance',
    salary: 80000,
    joinDate: ISODate('2018-08-19T00:00:00.000Z')
  }
]

Queries Using Logical Selectors

1. $and (Logical AND)

Find employees who are in the “Engineering” department and have a salary greater than 70000.

companyDB> db.Employees.find({ 
  $and: [
    { department: "Engineering" },
    { salary: { $gt: 70000 } }
  ] 
}).pretty()

[
  {
    _id: ObjectId('665356cff5b334bcf92202da'),
    name: 'Charlie',
    age: 29,
    department: 'Engineering',
    salary: 75000,
    joinDate: ISODate('2017-06-23T00:00:00.000Z')
  }
]

2. $or (Logical OR)

Find employees who are either in the “HR” department or have a salary less than 60000.

companyDB> db.Employees.find({ 
  $or: [
    { department: "HR" },
    { salary: { $lt: 60000 } }
  ] 
}).pretty()

[
  {
    _id: ObjectId('665356cff5b334bcf92202d8'),
    name: 'Alice',
    age: 30,
    department: 'HR',
    salary: 50000,
    joinDate: ISODate('2015-01-15T00:00:00.000Z')
  }
]

3. $not (Logical NOT)

Find employees who are not in the “Engineering” department.

companyDB> db.Employees.find({ 
  department: { 
    $not: { $eq: "Engineering" } 
  } 
}).pretty()

[
  {
    _id: ObjectId('665356cff5b334bcf92202d8'),
    name: 'Alice',
    age: 30,
    department: 'HR',
    salary: 50000,
    joinDate: ISODate('2015-01-15T00:00:00.000Z')
  },
  {
    _id: ObjectId('665356cff5b334bcf92202db'),
    name: 'David',
    age: 35,
    department: 'Marketing',
    salary: 60000,
    joinDate: ISODate('2014-11-01T00:00:00.000Z')
  },
  {
    _id: ObjectId('665356cff5b334bcf92202dc'),
    name: 'Eve',
    age: 28,
    department: 'Finance',
    salary: 80000,
    joinDate: ISODate('2018-08-19T00:00:00.000Z')
  }
]

4. $nor (Logical NOR)

Find employees who are neither in the “HR” department nor have a salary greater than 75000.

companyDB> db.Employees.find({ 
  $nor: [
    { department: "HR" },
    { salary: { $gt: 75000 } }
  ] 
}).pretty()

[
  {
    _id: ObjectId('665356cff5b334bcf92202d9'),
    name: 'Bob',
    age: 24,
    department: 'Engineering',
    salary: 70000,
    joinDate: ISODate('2019-03-10T00:00:00.000Z')
  },
  {
    _id: ObjectId('665356cff5b334bcf92202da'),
    name: 'Charlie',
    age: 29,
    department: 'Engineering',
    salary: 75000,
    joinDate: ISODate('2017-06-23T00:00:00.000Z')
  },
  {
    _id: ObjectId('665356cff5b334bcf92202db'),
    name: 'David',
    age: 35,
    department: 'Marketing',
    salary: 60000,
    joinDate: ISODate('2014-11-01T00:00:00.000Z')
  }
]

b. Query selectors (Geospatial selectors, Bitwise selectors )

Execute query selectors (Geospatial selectors, Bitwise selectors ) and list out the results on any collection

Let’s extend our MongoDB examples to include queries using geospatial selectors and bitwise selectors. We will create a new collection called Places for geospatial queries and a collection called Devices for bitwise queries.

Geospatial Selectors

First, let’s create a Places collection with geospatial data.

Create the Places Collection and Insert Documents

test> use geoDatabase
switched to db geoDatabase

geoDatabase> db.Places.insertMany([
  { name: "Central Park", location: { type: "Point", coordinates: [-73.9654, 40.7829] } },
  { name: "Times Square", location: { type: "Point", coordinates: [-73.9851, 40.7580] } },
  { name: "Brooklyn Bridge", location: { type: "Point", coordinates: [-73.9969, 40.7061] } },
  { name: "Empire State Building", location: { type: "Point", coordinates: [-73.9857, 40.7488] } },
  { name: "Statue of Liberty", location: { type: "Point", coordinates: [-74.0445, 40.6892] } }
])
{
  acknowledged: true,
  insertedIds: {
    '0': ObjectId('66536a9799cad9cd2b2202d8'),
    '1': ObjectId('66536a9799cad9cd2b2202d9'),
    '2': ObjectId('66536a9799cad9cd2b2202da'),
    '3': ObjectId('66536a9799cad9cd2b2202db'),
    '4': ObjectId('66536a9799cad9cd2b2202dc')
  }
}

// Create a geospatial index
geoDatabase> db.Places.createIndex({ location: "2dsphere" })
location_2dsphere

Geospatial Queries

1. $near (Find places near a certain point)

Find places near a specific coordinate, for example, near Times Square.

geoDatabase> db.Places.find({
  location: {
    $near: {
      $geometry: {
        type: "Point",
        coordinates: [-73.9851, 40.7580]
      },
      $maxDistance: 5000 // distance in meters
    }
  }
}).pretty()

[
  {
    _id: ObjectId('66536a9799cad9cd2b2202d9'),
    name: 'Times Square',
    location: { type: 'Point', coordinates: [ -73.9851, 40.758 ] }
  },
  {
    _id: ObjectId('66536a9799cad9cd2b2202db'),
    name: 'Empire State Building',
    location: { type: 'Point', coordinates: [ -73.9857, 40.7488 ] }
  },
  {
    _id: ObjectId('66536a9799cad9cd2b2202d8'),
    name: 'Central Park',
    location: { type: 'Point', coordinates: [ -73.9654, 40.7829 ] }
  }
]
2. $geoWithin (Find places within a specific area)

Find places within a specific polygon, for example, an area covering part of Manhattan.

geoDatabase> db.Places.find({
  location: {
    $geoWithin: {
      $geometry: {
        type: "Polygon",
        coordinates: [
          [
            [-70.016, 35.715],
            [-74.014, 40.717],
            [-73.990, 40.730],
            [-73.990, 40.715],
            [-70.016, 35.715]
          ]
        ]
      }
    }
  }
}).pretty()

[
  {
    _id: ObjectId('66536a9799cad9cd2b2202da'),
    name: 'Brooklyn Bridge',
    location: { type: 'Point', coordinates: [ -73.9969, 40.7061 ] }
  }
]

Bitwise Selectors

Next, let’s create a Devices collection for bitwise operations.

Create the Devices Collection and Insert Documents

test> use techDB

techDB> db.Devices.insertMany([
  { name: "Device A", status: 5 }, // Binary: 0101
  { name: "Device B", status: 3 }, // Binary: 0011
  { name: "Device C", status: 12 }, // Binary: 1100
  { name: "Device D", status: 10 }, // Binary: 1010
  { name: "Device E", status: 7 }  // Binary: 0111
])

Execute Bitwise Queries

1. $bitsAllSet (Find documents where all bits are set)

Find devices where the binary status has both the 1st and 3rd bits set (binary mask 0101, or decimal 5).

techDB> db.Devices.find({
  status: { $bitsAllSet: [0, 2] }
}).pretty()

[
  {
    _id: ObjectId('6653703d4e38f292e52202d8'),
    name: 'Device A',
    status: 5
  },
  {
    _id: ObjectId('6653703d4e38f292e52202dc'),
    name: 'Device E',
    status: 7
  }
]
2. $bitsAnySet (Find documents where any of the bits are set)

Find devices where the binary status has at least the 2nd bit set (binary mask 0010, or decimal 2).

techDB> db.Devices.find({
  status: { $bitsAnySet: [1] }
}).pretty()

[
  {
    _id: ObjectId('6653703d4e38f292e52202d9'),
    name: 'Device B',
    status: 3
  },
  {
    _id: ObjectId('6653703d4e38f292e52202db'),
    name: 'Device D',
    status: 10
  },
  {
    _id: ObjectId('6653703d4e38f292e52202dc'),
    name: 'Device E',
    status: 7
  }
]
3. $bitsAllClear (Find documents where all bits are clear)

Find devices where the binary status has both the 2nd and 4th bits clear (binary mask 1010, or decimal 10).

techDB> db.Devices.find({
  status: { $bitsAllClear: [1, 3] }
}).pretty()

[
  {
    _id: ObjectId('6653703d4e38f292e52202d8'),
    name: 'Device A',
    status: 5
  }
]
4. $bitsAnyClear (Find documents where any of the bits are clear)

Find devices where the binary status has at least the 1st bit clear (binary mask 0001, or decimal 1).

techDB> db.Devices.find({
  status: { $bitsAnyClear: [0] }
}).pretty()

[
  {
    _id: ObjectId('6653703d4e38f292e52202da'),
    name: 'Device C',
    status: 12
  },
  {
    _id: ObjectId('6653703d4e38f292e52202db'),
    name: 'Device D',
    status: 10
  }
]

Question 4

Projection Operators

Create and demonstrate how projection operators ($, $elematch and $slice) would be used in the MondoDB.

To demonstrate the use of projection operators ($, $elemMatch, and $slice) in MongoDB, let’s create a Products collection. We’ll insert documents that include arrays, which will allow us to showcase these operators effectively.

Create the Products Collection and Insert Documents

test> use retailDB
switched to db retailDB

retailDB> db.Products.insertMany([
  {
    name: "Laptop",
    brand: "BrandA",
    features: [
      { name: "Processor", value: "Intel i7" },
      { name: "RAM", value: "16GB" },
      { name: "Storage", value: "512GB SSD" }
    ],
    reviews: [
      { user: "Alice", rating: 5, comment: "Excellent!" },
      { user: "Bob", rating: 4, comment: "Very good" },
      { user: "Charlie", rating: 3, comment: "Average" }
    ]
  },
  {
    name: "Smartphone",
    brand: "BrandB",
    features: [
      { name: "Processor", value: "Snapdragon 888" },
      { name: "RAM", value: "8GB" },
      { name: "Storage", value: "256GB" }
    ],
    reviews: [
      { user: "Dave", rating: 4, comment: "Good phone" },
      { user: "Eve", rating: 2, comment: "Not satisfied" }
    ]
  }
])

Use Projection Operators

1. The $ Projection Operator

The $ operator is used to project the first matching element from an array of embedded documents.

Example: Find the product named “Laptop” and project the review from the user “Alice”.

retailDB> db.Products.find(
  { name: "Laptop", "reviews.user": "Alice" },
  { "reviews.$": 1 }
).pretty()

Result:

{
  "_id": ObjectId("..."),
  "reviews": [
    { "user": "Alice", "rating": 5, "comment": "Excellent!" }
  ]
}

2. The $elemMatch Projection Operator

The $elemMatch operator is used to project the first matching element from an array based on specified criteria.

Example: Find the product named “Laptop” and project the review where the rating is greater than 4.

retailDB> db.Products.find(
  { name: "Laptop" },
  { reviews: { $elemMatch: { rating: { $gt: 4 } } } }
).pretty()

Result:

{
  "_id": ObjectId("..."),
  "reviews": [
    { "user": "Alice", "rating": 5, "comment": "Excellent!" }
  ]
}

3. The $slice Projection Operator

The $slice operator is used to include a subset of the array field.

Example: Find the product named “Smartphone” and project the first review.

retailDB> db.Products.find(
  { name: "Smartphone" },
  { reviews: { $slice: 1 } }
).pretty()

Result:

{
  "_id": ObjectId("..."),
  "reviews": [
    { "user": "Dave", "rating": 4, "comment": "Good phone" }
  ]
}

Additional Example with Multiple Projection Operators

Example: Find the product named “Laptop” and project the name, the first two features, and the review with the highest rating.

retailDB> db.Products.find(
  { name: "Laptop" },
  {
    name: 1,
    features: { $slice: 2 },
    reviews: { $elemMatch: { rating: 5 } }
  }
).pretty()

Result:

{
  "_id": ObjectId("..."),
  "name": "Laptop",
  "features": [
    { "name": "Processor", "value": "Intel i7" },
    { "name": "RAM", "value": "16GB" }
  ],
  "reviews": [
    { "user": "Alice", "rating": 5, "comment": "Excellent!" }
  ]
}

Using projection operators in MongoDB, you can fine-tune the data returned by your queries:

  • The $ operator is useful for projecting the first matching element from an array.
  • The $elemMatch operator allows you to project the first array element that matches specified criteria.
  • The $slice operator lets you project a subset of an array, such as the first n elements or a specific range.

Question 5

Aggregation operations

Execute Aggregation operations ($avg, $min,$max, $push, $addToSet etc.). students encourage to execute several queries to demonstrate various aggregation operators)

To demonstrate aggregation operations such as $avg, $min, $max, $push, and $addToSet in MongoDB, we will use a Sales collection. This collection will contain documents representing sales transactions.

Create the Sales Collection and Insert Documents

First, we’ll create the Sales collection and insert sample documents.

test> use salesDB

salesDB> db.Sales.insertMany([
  { date: new Date("2024-01-01"), product: "Laptop", price: 1200, quantity: 1, customer: "Amar" },
  { date: new Date("2024-01-02"), product: "Laptop", price: 1200, quantity: 2, customer: "Babu" },
  { date: new Date("2024-01-03"), product: "Mouse", price: 25, quantity: 5, customer: "Chandra" },
  { date: new Date("2024-01-04"), product: "Keyboard", price: 45, quantity: 3, customer: "Amar" },
  { date: new Date("2024-01-05"), product: "Monitor", price: 300, quantity: 1, customer: "Babu" },
  { date: new Date("2024-01-06"), product: "Laptop", price: 1200, quantity: 1, customer: "Deva" }
])

Execute Aggregation Operations

1. $avg (Average)

Calculate the average price of each product.

salesDB> db.Sales.aggregate([
  {
    $group: {
      _id: "$product",
      averagePrice: { $avg: "$price" }
    }
  }
]).pretty()

Result:

[
  { "_id": "Laptop", "averagePrice": 1200 },
  { "_id": "Mouse", "averagePrice": 25 },
  { "_id": "Keyboard", "averagePrice": 45 },
  { "_id": "Monitor", "averagePrice": 300 }
]

2. $min (Minimum)

Find the minimum price of each product.

salesDB> db.Sales.aggregate([
  {
    $group: {
      _id: "$product",
      minPrice: { $min: "$price" }
    }
  }
]).pretty()

Result:

[
  { "_id": "Laptop", "minPrice": 1200 },
  { "_id": "Mouse", "minPrice": 25 },
  { "_id": "Keyboard", "minPrice": 45 },
  { "_id": "Monitor", "minPrice": 300 }
]

3. $max (Maximum)

Find the maximum price of each product.

salesDB> db.Sales.aggregate([
  {
    $group: {
      _id: "$product",
      maxPrice: { $max: "$price" }
    }
  }
]).pretty()

Result:

[
  { "_id": "Laptop", "maxPrice": 1200 },
  { "_id": "Mouse", "maxPrice": 25 },
  { "_id": "Keyboard", "maxPrice": 45 },
  { "_id": "Monitor", "maxPrice": 300 }
]

4. $push (Push Values to an Array)

Group sales by customer and push each purchased product into an array.

salesDB> db.Sales.aggregate([
  {
    $group: {
      _id: "$customer",
      products: { $push: "$product" }
    }
  }
]).pretty()

Result:

[
  { "_id": "Amar", "products": ["Laptop", "Keyboard"] },
  { "_id": "Babu", "products": ["Laptop", "Monitor"] },
  { "_id": "Chandra", "products": ["Mouse"] },
  { "_id": "Deva", "products": ["Laptop"] }
]

5. $addToSet (Add Unique Values to an Array)

Group sales by customer and add each unique purchased product to an array.

salesDB> db.Sales.aggregate([
  {
    $group: {
      _id: "$customer",
      uniqueProducts: { $addToSet: "$product" }
    }
  }
]).pretty()

Result:

[
  { "_id": "Amar", "uniqueProducts": ["Laptop", "Keyboard"] },
  { "_id": "Babu", "uniqueProducts": ["Laptop", "Monitor"] },
  { "_id": "Chandra", "uniqueProducts": ["Mouse"] },
  { "_id": "Deva", "uniqueProducts": ["Laptop"] }
]

Combining Aggregation Operations

Let’s combine several aggregation operations to get a comprehensive report.

Example: Calculate the total quantity and total sales amount for each product, and list all customers who purchased each product.

salesDB> db.Sales.aggregate([
  {
    $group: {
      _id: "$product",
      totalQuantity: { $sum: "$quantity" },
      totalSales: { $sum: { $multiply: ["$price", "$quantity"] } },
      customers: { $addToSet: "$customer" }
    }
  }
]).pretty()

Result:

[
  {
    "_id": "Laptop",
    "totalQuantity": 4,
    "totalSales": 4800,
    "customers": ["Amar", "Babu", "Deva"]
  },
  {
    "_id": "Mouse",
    "totalQuantity": 5,
    "totalSales": 125,
    "customers": ["Chandra"]
  },
  {
    "_id": "Keyboard",
    "totalQuantity": 3,
    "totalSales": 135,
    "customers": ["Amar"]
  },
  {
    "_id": "Monitor",
    "totalQuantity": 1,
    "totalSales": 300,
    "customers": ["Babu"]
  }
]

By using aggregation operations such as $avg, $min, $max, $push, and $addToSet, you can perform complex data analysis and transformations on MongoDB collections. These operations enable you to calculate averages, find minimum and maximum values, push values into arrays, and create sets of unique values. The examples provided show how to use these operators to analyze a Sales collection

Question 6

Aggregation Pipeline and its operations

Execute Aggregation Pipeline and its operations (pipeline must contain $match, $group, $sort, $project, $skip etc.)

Let’s consider a scenario involving a restaurantDB database with a restaurants collection. Each document in the restaurants collection contains details about a restaurant, including its name, cuisine, location, and an array of reviews. Each review includes a rating and a comment. After creating the restaurantDB database and insert sample documents into the restaurants collection we will create an aggregation pipeline as shown below.

// Switch to the restaurantDB database
use restaurantDB

// Insert sample documents into the restaurants collection
db.restaurants.insertMany([
  {
    name: "Biryani House",
    cuisine: "Indian",
    location: "Jayanagar",
    reviews: [
      { user: "Aarav", rating: 5, comment: "Amazing biryani!" },
      { user: "Bhavana", rating: 4, comment: "Great place!" }
    ]
  },
  {
    name: "Burger Joint",
    cuisine: "American",
    location: "Koramangala",
    reviews: [
      { user: "Chirag", rating: 3, comment: "Average burger" },
      { user: "Devika", rating: 4, comment: "Good value" }
    ]
  },
  {
    name: "Pasta House",
    cuisine: "Italian",
    location: "Rajajinagar",
    reviews: [
      { user: "Esha", rating: 5, comment: "Delicious pasta!" },
      { user: "Farhan", rating: 4, comment: "Nice ambiance" }
    ]
  },
  {
    name: "Curry Palace",
    cuisine: "Indian",
    location: "Jayanagar",
    reviews: [
      { user: "Gaurav", rating: 4, comment: "Spicy and tasty!" },
      { user: "Harini", rating: 5, comment: "Best curry in town!" }
    ]
  },
  {
    name: "Taco Stand",
    cuisine: "Mexican",
    location: "Jayanagar",
    reviews: [
      { user: "Ishaan", rating: 5, comment: "Fantastic tacos!" },
      { user: "Jaya", rating: 4, comment: "Very authentic" }
    ]
  }
])

// Run the aggregation pipeline query to display reviews summary
db.restaurants.aggregate([
  {
    $match: {
      location: "Jayanagar"
    }
  },
  {
    $unwind: "$reviews"
  },
  {
    $group: {
      _id: "$name",
      averageRating: { $avg: "$reviews.rating" },
      totalReviews: { $sum: 1 }
    }
  },
  {
    $sort: {
      averageRating: -1
    }
  },
  {
    $project: {
      _id: 0,
      restaurant: "$_id",
      averageRating: 1,
      totalReviews: 1
    }
  },
  {
    $skip: 1
  }
]).pretty()

Now, let’s execute an aggregation pipeline that includes the $match, $unwind, $group, $sort, $project, and $skip stages.

Aggregation Pipeline Explanation

  1. $match: Filter restaurants by cuisine ("Jayanagar" location).
  2. $unwind: Deconstruct the reviews array from each document to output a document for each review.
  3. $group: Group the documents by restaurant name and calculate the average rating and total number of reviews.
  4. $sort: Sort the results by average rating in descending order.
  5. $project: Restructure the output to include only the restaurant name, average rating, and total reviews.
  6. $skip: Skip the first document.

Question 7

a. Find all listings

Find all listings with listing_url, name, address, host_picture_url in the listings And Reviews collection that have a host with a picture url

To find all listings with listing_url, name, address, and host_picture_url in the listingsAndReviews collection where the host has a picture URL, let is create appropriate databases and queries as follows.

Create the Database

First, switch to or create the database you want to use. For this example, let’s call the database vacationRentals.

test> use vacationRentals
switched to db vacationRentals
vacationRentals>

Create the listingsAndReviews Collection and Insert Documents

Next, create the listingsAndReviews collection and insert sample documents. Here are a few example documents to illustrate the structure:

vacationRentals> db.listingsAndReviews.insertMany([
  {
    listing_url: "http://www.example.com/listing/123456",
    name: "Beautiful Apartment",
    address: {
      street: "123 Main Street",
      suburb: "Central",
      city: "Metropolis",
      country: "Wonderland"
    },
    host: {
      name: "Alice",
      picture_url: "http://www.example.com/images/host/host123.jpg"
    }
  },
  {
    listing_url: "http://www.example.com/listing/654321",
    name: "Cozy Cottage",
    address: {
      street: "456 Another St",
      suburb: "North",
      city: "Smallville",
      country: "Wonderland"
    },
    host: {
      name: "Bob",
      picture_url: ""
    }
  },
  {
    listing_url: "http://www.example.com/listing/789012",
    name: "Modern Condo",
    address: {
      street: "789 Side Road",
      suburb: "East",
      city: "Gotham",
      country: "Wonderland"
    },
    host: {
      name: "Charlie",
      picture_url: "http://www.example.com/images/host/host789.jpg"
    }
  }
])

Query to Find Listings with Host Picture URLs

Now that the collection is set up, you can run the query to find all listings with listing_url, name, address, and host_picture_url where the host has a picture URL.

db.listingsAndReviews.find(
  {
    "host.picture_url": { $exists: true, $ne: "" }
  },
  {
    listing_url: 1,
    name: 1,
    address: 1,
    "host.picture_url": 1
  }
).pretty()

Explanation:

  • Query Filter:
    • "host.picture_url": { $exists: true, $ne: "" }: This part of the query ensures that only documents where the host.picture_url field exists and is not an empty string are selected.
  • Projection:
    • { listing_url: 1, name: 1, address: 1, "host.picture_url": 1 }: This part of the query specifies the fields to include in the output. The 1 indicates that these fields should be included.

Expected Result

The query should return documents where the host has a picture URL. Based on the inserted documents, the result should look something like this:

{
  "_id": ObjectId("..."),
  "listing_url": "http://www.example.com/listing/123456",
  "name": "Beautiful Apartment",
  "address": {
    "street": "123 Main Street",
    "suburb": "Central",
    "city": "Metropolis",
    "country": "Wonderland"
  },
  "host": {
    "picture_url": "http://www.example.com/images/host/host123.jpg"
  }
}
{
  "_id": ObjectId("..."),
  "listing_url": "http://www.example.com/listing/789012",
  "name": "Modern Condo",
  "address": {
    "street": "789 Side Road",
    "suburb": "East",
    "city": "Gotham",
    "country": "Wonderland"
  },
  "host": {
    "picture_url": "http://www.example.com/images/host/host789.jpg"
  }
}

b. E-commerce collection

Using E-commerce collection write a query to display reviews summary.

To display a summary of reviews in an e-commerce collection, we can assume the ecommerce database contains a products collection with documents structured to include reviews. Each product document could have a reviews array with review details such as rating, comment, and user.

// Switch to the ecommerce database
use ecommerce

// Insert sample documents into the products collection
db.products.insertMany([
  {
    product_id: 1,
    name: "Laptop",
    category: "Electronics",
    price: 1200,
    reviews: [
      { user: "Alice", rating: 5, comment: "Excellent!" },
      { user: "Bob", rating: 4, comment: "Very good" },
      { user: "Charlie", rating: 3, comment: "Average" }
    ]
  },
  {
    product_id: 2,
    name: "Smartphone",
    category: "Electronics",
    price: 800,
    reviews: [
      { user: "Dave", rating: 4, comment: "Good phone" },
      { user: "Eve", rating: 2, comment: "Not satisfied" },
      { user: "Frank", rating: 5, comment: "Amazing!" }
    ]
  },
  {
    product_id: 3,
    name: "Headphones",
    category: "Accessories",
    price: 150,
    reviews: [
      { user: "Grace", rating: 5, comment: "Great sound" },
      { user: "Heidi", rating: 3, comment: "Okay" }
    ]
  }
])

// Run the aggregation query to display reviews summary
db.products.aggregate([
  {
    $unwind: "$reviews"
  },
  {
    $group: {
      _id: "$name",
      totalReviews: { $sum: 1 },
      averageRating: { $avg: "$reviews.rating" },
      comments: { $push: "$reviews.comment" }
    }
  },
  {
    $project: {
      _id: 0,
      product: "$_id",
      totalReviews: 1,
      averageRating: 1,
      comments: 1
    }
  }
]).pretty()

This script will set up the ecommerce database, populate the products collection with sample data, and execute an aggregation query to summarize the reviews.

Explanation:

  1. $unwind: Deconstructs the reviews array from each document to output a document for each element.
  2. $group: Groups the documents by product name, and calculates:
    • totalReviews: The total number of reviews for each product.
    • averageRating: The average rating of the reviews for each product.
    • comments: An array of all review comments for each product.
  3. $project: Restructures the output documents to include the product name, total reviews, average rating, and comments.

Sample Output:

The query will return a summary for each product in the collection:

[
  {
    "product": "Laptop",
    "totalReviews": 3,
    "averageRating": 4,
    "comments": [
      "Excellent!",
      "Very good",
      "Average"
    ]
  },
  {
    "product": "Smartphone",
    "totalReviews": 3,
    "averageRating": 3.6666666666666665,
    "comments": [
      "Good phone",
      "Not satisfied",
      "Amazing!"
    ]
  },
  {
    "product": "Headphones",
    "totalReviews": 2,
    "averageRating": 4,
    "comments": [
      "Great sound",
      "Okay"
    ]
  }
]

Question 8

a. Demonstrate different types of indexes

Demonstrate creation of different types of indexes on collection (unique, sparse, compound and multikey indexes)

Let’s demonstrate the creation of various types of indexes on a restaurants collection in the restaurantDB database. We’ll cover unique, sparse, compound, and multikey indexes.

Step 1: Create the Database and Collection

First, let’s set up the restaurantDB database and insert sample documents into the restaurants collection.

// Switch to the restaurantDB databasenuse restaurantDBnn// Insert sample documents into the restaurants collectionndb.restaurants.insertMany([n  {n    name: u0022Biryani Houseu0022,n    cuisine: u0022Indianu0022,n    location: u0022Downtownu0022,n    reviews: [n      { user: u0022Aaravu0022, rating: 5, comment: u0022Amazing biryani!u0022 },n      { user: u0022Bhavanau0022, rating: 4, comment: u0022Great place!u0022 }n    ],n    contact: { phone: u00221234567890u0022, email: u0022contact@biryanihouse.comu0022 }n  },n  {n    name: u0022Curry Palaceu0022,n    cuisine: u0022Indianu0022,n    location: u0022Downtownu0022,n    reviews: [n      { user: u0022Gauravu0022, rating: 4, comment: u0022Spicy and tasty!u0022 },n      { user: u0022Hariniu0022, rating: 5, comment: u0022Best curry in town!u0022 }n    ],n    contact: { phone: u00220987654321u0022, email: u0022contact@currypalace.comu0022 }n  },n  {n    name: u0022Taco Standu0022,n    cuisine: u0022Mexicanu0022,n    location: u0022Downtownu0022,n    reviews: [n      { user: u0022Ishaanu0022, rating: 5, comment: u0022Fantastic tacos!u0022 },n      { user: u0022Jayau0022, rating: 4, comment: u0022Very authenticu0022 }n    ],n    contact: { phone: u00221122334455u0022, email: u0022contact@tacostand.comu0022 }n  }n])

Step 2: Create Various Indexes

1. Unique Index

A unique index ensures that the indexed field does not contain duplicate values.

// Create a unique index on the contact.email fieldndb.restaurants.createIndex({ u0022contact.emailu0022: 1 }, { unique: true })

2. Sparse Index

A sparse index only includes documents that contain the indexed field, ignoring documents where the field is missing.

// Create a sparse index on the location fieldndb.restaurants.createIndex({ location: 1 }, { sparse: true })

3. Compound Index

A compound index indexes multiple fields within a single index.

// Create a compound index on the name and location fieldsndb.restaurants.createIndex({ name: 1, location: 1 })

4. Multikey Index

A multikey index is created on an array field, indexing each element of the array.

// Create a multikey index on the reviews fieldndb.restaurants.createIndex({ reviews: 1 })

Step 3: Verify Indexes

To verify the created indexes, you can use the getIndexes method.

// Verify the created indexesndb.restaurants.getIndexes()

Output

testu003e use restaurantDBnswitched to db restaurantDBnrestaurantDBu003e db.restaurants.insertMany([n...   {n...     name: u0022Biryani Houseu0022,n...     cuisine: u0022Indianu0022,n...     location: u0022Downtownu0022,n...     reviews: [n...       { user: u0022Aaravu0022, rating: 5, comment: u0022Amazing biryani!u0022 },n...       { user: u0022Bhavanau0022, rating: 4, comment: u0022Great place!u0022 }n...     ],n...     contact: { phone: u00221234567890u0022, email: u0022contact@biryanihouse.comu0022 }n...   },n...   {n...     name: u0022Curry Palaceu0022,n...     cuisine: u0022Indianu0022,n...     location: u0022Downtownu0022,n...     reviews: [n...       { user: u0022Gauravu0022, rating: 4, comment: u0022Spicy and tasty!u0022 },n...       { user: u0022Hariniu0022, rating: 5, comment: u0022Best curry in town!u0022 }n...     ],n...     contact: { phone: u00220987654321u0022, email: u0022contact@currypalace.comu0022 }n...   },n...   {n...     name: u0022Taco Standu0022,n...     cuisine: u0022Mexicanu0022,n...     location: u0022Downtownu0022,n...     reviews: [n...       { user: u0022Ishaanu0022, rating: 5, comment: u0022Fantastic tacos!u0022 },n...       { user: u0022Jayau0022, rating: 4, comment: u0022Very authenticu0022 }n...     ],n...     contact: { phone: u00221122334455u0022, email: u0022contact@tacostand.comu0022 }n...   }n... ])n{n  acknowledged: true,n  insertedIds: {n    '0': ObjectId('667b3c596809bfbfae149f48'),n    '1': ObjectId('667b3c596809bfbfae149f49'),n    '2': ObjectId('667b3c596809bfbfae149f4a')n  }n}nrestaurantDBu003e db.restaurants.createIndex({ u0022contact.emailu0022: 1 }, { unique: true })ncontact.email_1nrestaurantDBu003e db.restaurants.createIndex({ location: 1 }, { sparse: true })nlocation_1nrestaurantDBu003e db.restaurants.createIndex({ name: 1, location: 1 })nname_1_location_1nrestaurantDBu003e db.restaurants.createIndex({ reviews: 1 })nreviews_1nrestaurantDBu003e db.restaurants.getIndexes()n[n  { v: 2, key: { _id: 1 }, name: '_id_' },n  {n    v: 2,n    key: { 'contact.email': 1 },n    name: 'contact.email_1',n    unique: truen  },n  { v: 2, key: { location: 1 }, name: 'location_1', sparse: true },n  { v: 2, key: { name: 1, location: 1 }, name: 'name_1_location_1' },n  { v: 2, key: { reviews: 1 }, name: 'reviews_1' }n]nrestaurantDBu003e n

This script sets up the restaurantDB database, populates the restaurants collection with sample data, and demonstrates the creation of unique, sparse, compound, and multikey indexes. The getIndexes method at the end allows you to verify the indexes created on the collection.

b. Demonstrate optimization of queries using indexes.

To demonstrate the optimization of queries using indexes, we have to use a fairly large database where query execution times are longer. For the following examples, we’ll use a dataset of daily NASDAQ summaries. To follow along, you’ll need this data locally.

Step 1: Create the Database and Collection

First, download the archive from here . Then, unzip the file to a temporary folder.

$ unzip stocks.zip nArchive:  stocks.zipn   creating: dump/stocks/n  inflating: dump/stocks/system.indexes.bson  n  inflating: dump/stocks/values.bson

Now its time to import this stocks database into the MongoDB using the mongorestore command. After that switch to the stocks database. 

$ mongorestore -d stocks dump/stocksnn$ mongoshnntestu003e use stocksnswitched to db stocks

Lets have a look at the structure of this database to find the various fields using the following command. The stocks database has a value collection that contains, for a certain subset of the NASDAQ stock exchange’s symbols, there’s a document for each day’s high, low, close, and volume for a 25-year period beginning in 1983.

stocksu003e show collectionsnvaluesnnkeys = db.values.findOne()n{n  _id: ObjectId('4d094f58c96767d7a0099d49'),n  exchange: 'NASDAQ',n  stock_symbol: 'AACC',n  date: '2008-03-07',n  open: 8.4,n  high: 8.75,n  low: 8.08,n  close: 8.55,n  volume: 275800,n  'adj close': 8.55n}nnstocksu003e db.values.countDocuments()n4308303n

You can also see that this database has more than four million records/documents and a huge amount of information in it. Queries run on such databases usually take more time to execute. For example if we want to find out the first occurrence of Google’s stock price, we issue the following query.

stocks> db.values.find({"stock_symbol": "GOOG"}).sort({date: -1}).limit(1)
[
  {
    _id: ObjectId('4d094f7ec96767d7a02a0af6'),
    exchange: 'NASDAQ',
    stock_symbol: 'GOOG',
    date: '2008-03-07',
    open: 428.88,
    high: 440,
    low: 426.24,
    close: 433.35,
    volume: 8071800,
    'adj close': 433.35
  }
]

We observe that this query takes time to run. Lets see if we can actually measure the time taken. Fortunately we have a explain() method. MongoDB’s explain command provides detailed information about a given query execution details. It provides even more detail with the executionStats parameter. Let us see an example

Step 2: Issue a slow query

Let us develop a query to finding the highest closing price in the data set.

stocks> db.values.find({}).sort({close: -1}).limit(1)

When we execute this query you observe it takes more time to execute and produce the following result.

[
  {
    _id: ObjectId('4d094fc2c96767d7a0360a64'),
    exchange: 'NASDAQ',
    stock_symbol: 'BORD',
    date: '2000-09-20',
    open: 7500,
    high: 7500,
    low: 7500,
    close: 7500,
    volume: 400,
    'adj close': 6679.94
  }
]

Step 3: Execution statistics

To obtain execution statistics we need to append the explain method. To get more details pass executionStats to the explain method.

stocks> db.values.find({}).sort({close: -1}).limit(1).explain()

stocks> db.values.find({}).sort({close: -1}).limit(1).explain("executionStats")

You can see from its output that it provides us with a wealth of information regarding the query. But what we are interested is in the execution time and number of documents it has scanned to obtain the result. These information can be obtained as follows.

stocks> db.values.find({}).sort({close: -1}).limit(1).explain("executionStats").executionStats.totalDocsExamined
4308303

stocks> db.values.find({}).sort({close: -1}).limit(1).explain("executionStats").executionStats.executionTimeMillis
1831

We see that it has scanned all the documents(4308303) to arrive at the result and it has taken 1.8 seconds. The reason for this performance is that the database is not indexed. Let us now index and see the performance.

Step 2: Create a Index and optimize query performance

We will add an index on the close field.

stocks> db.values.createIndex({close: 1})
close_1

We will rerun the queries now and examine the query performance.

stocks> db.values.find({}).sort({close: -1}).limit(1).explain("executionStats").executionStats.totalDocsExamined
1

stocks> db.values.find({}).sort({close: -1}).limit(1).explain("executionStats").executionStats.executionTimeMillis
57

Now after the database is indexed we see a huge improvement in performance. The no of records scanned is just 1 and query took only 57 milliseconds to execute.

Question 9

a. Develop a query to demonstrate Text search using catalog data collection for a given word.

To demonstrate text search in MongoDB using a catalog collection, we’ll follow these steps:

  1. Create the catalog collection and insert sample documents.
  2. Create a text index on the relevant fields.
  3. Perform a text search query to find documents containing a specific word.

For this example let us consider a movie database that has been imported from a CSV file. We can import data from the CSV file using the mongoimport utility as follows:

The CSV file kan_movies.csv is provided below for your reference.

kan_moviesDownload

Step 1: Importing from CSV file using mongoimport into the catalog Collection 

mongoimport --db=kannadaMoviesDB --collection='catalog' --file=kan_movies.csv --type=csv --fields="name","year","duration","rating","genre","lang"
  • –db parameter is used to specify the database into which data is to be imported.
  • –file parameter is used to specify the file from which data is to be imported
  • –type parameter is used to specify the file type (csv, json,…..)
  • –collection parameter is used to specify the collection into which data is to be imported.
  • –fields parameter is used to specify a list of strings that are field names in the collection.

On success you should get the following output.

2024-06-29T02:53:01.473+0530	connected to: mongodb://localhost/
2024-06-29T02:53:02.252+0530	701 document(s) imported successfully. 0 document(s) failed to import.

Now launch MongoDB and choose the newly created database as follows:

test> use kannadaMoviesDB
switched to db kannadaMoviesDB

kannadaMoviesDB> show collections
catalog

kannadaMoviesDB> db.catalog.countDocuments()
701

Alternatively you can create a catalog collection by adding documents using insertMany query as done in previous exercises.

Step 2: Create a Text Index

Next, create a text index on the name and genre fields to enable text search.

// Create a text index on the name and genre fields
db.catalog.createIndex({name: "text", genre: "text"})

Step 3: Perform a Text Search Query

Now, let’s perform a text search to find documents containing a specific word. For example, let’s search for the word “maga”.

// Perform a text search query to find documents containing the word "maga"
db.catalog.find({$text: {$search: "maga"}})

Output

[
  {
    _id: ObjectId('667f29b50c118ded9b39bd44'),
    name: 'Jayammana Maga',
    year: 2013,
    duration: '139 min',
    rating: 7.1,
    genre: 'Drama',
    lang: 'kannada'
  },
  {
    _id: ObjectId('667f29b50c118ded9b39bc7b'),
    name: 'Rajannana Maga',
    year: 2018,
    duration: '143 min',
    rating: 7.8,
    genre: 'Action',
    lang: 'kannada'
  },
  {
    _id: ObjectId('667f29b50c118ded9b39beea'),
    name: 'Thayige thakka maga',
    year: 2018,
    duration: '147 min',
    rating: 5.4,
    genre: 'Drama',
    lang: 'kannada'
  },
  {
    _id: ObjectId('667f29b50c118ded9b39bd97'),
    name: 'Bhootayyana Maga Ayyu',
    year: 1974,
    duration: '155 min',
    rating: 8.3,
    genre: 'Drama',
    lang: 'kannada'
  },
  {
    _id: ObjectId('667f29b50c118ded9b39bd31'),
    name: 'Jaga Mechida Maga',
    year: 1972,
    duration: '153 min',
    rating: 8.2,
    genre: 'Drama',
    lang: 'kannada'
  },
  {
    _id: ObjectId('667f29b50c118ded9b39bd11'),
    name: 'Daari Tappida Maga',
    year: 1975,
    duration: '138 min',
    rating: 7.6,
    genre: 'Crime, Drama            ',
    lang: 'kannada'
  }
]

// Perform a text search query to find documents containing the word "raju"
db.catalog.find({$text: {$search: "raju"}})

Output

[
  {
    _id: ObjectId('667f29b50c118ded9b39bd33'),
    name: 'Raju Kannada Medium',
    year: 2018,
    duration: '159 min',
    rating: 7.2,
    genre: 'Comedy, Drama            ',
    lang: 'kannada'
  },
  {
    _id: ObjectId('667f29b50c118ded9b39bcd0'),
    name: 'First Rank Raju',
    year: 2015,
    duration: '148 min',
    rating: 7.8,
    genre: 'Comedy, Drama            ',
    lang: 'kannada'
  }
]

Step 4: Perform a Text Search Query for a phrase

Now, let’s perform a text search to find documents containing a specific phrase. For example, let’s search for the phrase “tappida Maga”.

// Perform a text search query to find documents containing the phrase "maga"
db.catalog.find({$text: {$search: ""tappida Maga""}})

Output

[
  {
    _id: ObjectId('667f29b50c118ded9b39bd11'),
    name: 'Daari Tappida Maga',
    year: 1975,
    duration: '138 min',
    rating: 7.6,
    genre: 'Crime, Drama            ',
    lang: 'kannada'
  }
]

Explanation

  1. Inserting Documents: We insert several documents into the catalog collection with fields name,year,duration,rating,genre, and lang.
  2. Creating a Text Index: We create a text index on the name and description fields to enable text search.
  3. Performing a Text Search: We use the $text operator with the $search parameter to find documents that contain the word “raju” in either the name or genre fields.

This script sets up the catalog collection, creates a text index, and demonstrates a text search query to find documents containing a specific word.

b. Develop queries to illustrate excluding documents with certain words and phrases

To exclude documents containing certain words or phrases in MongoDB, you can use the $text operator combined with the $search parameter and the negation (-) operator. This allows you to perform text searches that exclude specific terms.

A negated term is a term that is prefixed by a minus sign -. If you negate a term, the $text operator excludes the documents that contain those terms from the results.

Step-by-Step Process

  1. Set up the catalog collection: Insert sample documents.
  2. Create a text index: Enable text search.
  3. Perform queries to exclude documents: Use the $text operator with negation.

Step 1: Create a catalog collection

For this we will use the same catalog collection from our previous example. You can follow the same steps as earlier to create the collection.

Step 2: Create a Text Index

Create a text index on the name and description fields.

// Create a text index on the name and description fields
db.catalog.createIndex({ name: "text", description: "text" })

Step 3: Perform Queries to Exclude Documents

Use the $text operator with negation to exclude documents containing specific words or phrases.

Example 1: Exclude Documents Containing the Word “action”

Suppose we want to list movies that belong to crime or romance (or both) genre but not belonging to the action genre. Since this will yield too many results we will restrict the search to the year 2021.

// Exclude documents containing the word "action"
db.catalog.find({ $text: { $search: "crime romance -action" }, year:2021 } )

Output

[
  {
    _id: ObjectId('667f29b50c118ded9b39bcaa'),
    name: 'Raktha Gulabi',
    year: 2021,
    duration: '132 min',
    rating: 9.4,
    genre: 'Crime',
    lang: 'kannada'
  },
  {
    _id: ObjectId('667f29b50c118ded9b39bd82'),
    name: 'Laddu',
    year: 2021,
    duration: '127 min',
    rating: 8.6,
    genre: 'Comedy, Romance            ',
    lang: 'kannada'
  }
]

Example 2: Exclude Documents Containing the Phrase “da maga”

We display those documents that have the word maga but not the phrase da maga.

// Exclude documents containing the phrase "da maga"
db.catalog.find({$text: {$search: "maga -"da maga""}})

Output

[
  {
    _id: ObjectId('667f29b50c118ded9b39bd44'),
    name: 'Jayammana Maga',
    year: 2013,
    duration: '139 min',
    rating: 7.1,
    genre: 'Drama',
    lang: 'kannada'
  },
  {
    _id: ObjectId('667f29b50c118ded9b39bc7b'),
    name: 'Rajannana Maga',
    year: 2018,
    duration: '143 min',
    rating: 7.8,
    genre: 'Action',
    lang: 'kannada'
  },
  {
    _id: ObjectId('667f29b50c118ded9b39beea'),
    name: 'Thayige thakka maga',
    year: 2018,
    duration: '147 min',
    rating: 5.4,
    genre: 'Drama',
    lang: 'kannada'
  },
  {
    _id: ObjectId('667f29b50c118ded9b39bd97'),
    name: 'Bhootayyana Maga Ayyu',
    year: 1974,
    duration: '155 min',
    rating: 8.3,
    genre: 'Drama',
    lang: 'kannada'
  }
]

Note:

  • The negated word excludes documents that contain the negated word from the result set.
  • When passed a string that only contains negated words, $text does not match any documents.

Question 10

Aggregation Pipeline to illustrate Text search on Catalog data collection

Develop an aggregation pipeline to illustrate Text search on Catalog data collection.

The aggregation framework in MongoDB is a powerful tool for data processing and transformation. It consists of a series of stages, each stage performing an operation on the input documents and passing the results to the next stage. This sequence of operations is called an aggregation pipeline.

Here is a step-by-step guide to using the aggregation pipeline in MongoDB, with examples of various stages such as $match, $group, $sort, $project, $skip, and others.

Example Aggregation Pipeline

Let’s create an aggregation pipeline that includes various stages:

  1. $match: Filter documents to include only those in the year 2017.
  2. $group: Group documents by genre and calculate the average rating for each genre.
  3. $sort: Sort the results by avgRating in descending order.
  4. $project: Include specific fields in the output
  5. $limit: Limit the output to 5 results.
  6. $skip: Skip the first two results.
result = db.catalog.aggregate([
  // 1. Match stage: Filter documents by year 2017
{$match:{year :2017}}, 
  // 2. Group stage: Group by genre and calculate average rating
{$group:{_id: "$genre", avgRating:{$avg: "$rating"}}}, 
  // 3. Sort stage: Sort by avgRating in descending order
{$sort: {avgRating:-1}},
  // 4. Project stage: Include specific fields
{$project:{year:"$year", avgRating:1, genre:1} }, 
  // 5. Limit stage: Limit the output to 5 results
{$limit:5} ]).toArray()

print("Top 5 rated movie genres with their average rating")
printjson(result)

Output

Top 5 rated movie genres with their average rating


[
  {
    _id: 'Mystery',
    avgRating: 7.9
  },
  {
    _id: 'Comedy, Drama            ',
    avgRating: 7.9
  },
  {
    _id: 'Adventure, Family            ',
    avgRating: 7.8
  },
  {
    _id: 'Drama, Romance, Thriller            ',
    avgRating: 7.7
  },
  {
    _id: 'Drama',
    avgRating: 7.616666666666667
  }
]

Adding another stage using $skip

To find the remaining five genres among the top-rated seven genres after skipping the first two, we can use the $skip stage in the aggregation pipeline.

result2 = db.catalog.aggregate([
  // 1. Match stage: Filter documents by year 2017
{$match:{year :2017}}, 
  // 2. Group stage: Group by genre and calculate average rating
{$group:{_id: "$genre", avgRating:{$avg: "$rating"}}}, 
  // 3. Sort stage: Sort by avgRating in descending order
{$sort: {avgRating:-1}},
  // 4. Project stage: Include specific fields
{$project:{year:"$year", avgRating:1, genre:1} }, 
  // 5. Limit stage: Limit the output to 7 results
{$limit:7},
  // 6. Skip stage: Skip the first two results
{$skip:2} ]).toArray()

print("Top 7 rated movie genres with their average rating with he firsttwo skipped")
printjson(result2)

Output

Top 7 rated movie genres with their average rating with he firsttwo skipped

kannadaMoviesDB> printjson(result2)
[
  {
    _id: 'Adventure, Family            ',
    avgRating: 7.8
  },
  {
    _id: 'Drama, Romance, Thriller            ',
    avgRating: 7.7
  },
  {
    _id: 'Drama',
    avgRating: 7.616666666666667
  },
  {
    _id: 'Drama, History, Musical            ',
    avgRating: 7.5
  },
  {
    _id: 'Drama, Family, History            ',
    avgRating: 7.5
  }
]

The aggregation pipeline in MongoDB provides a flexible and powerful framework for data processing. By chaining multiple stages, you can filter, group, sort, project, and transform your data to suit your specific requirements.

If you are also looking for other Lab Manuals, head over to my following blog :

VTU Lab Manuals using FOSS

Prabodh C P is a faculty in the Dept of CSE SIT, Tumkur and also currently a Research Scholar pursuing PhD in IIT Hyderabad. He conducts online classes for C, C++, Python. For more info call +919392302100

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