MongoDB Lab Manual – BDS456B

Prabodh C P

SYSTEM PROGRAMMING LABORATORY

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.

Part A

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 database
use restaurantDB

// Insert sample documents into the restaurants collection
db.restaurants.insertMany([
  {
    name: "Biryani House",
    cuisine: "Indian",
    location: "Downtown",
    reviews: [
      { user: "Aarav", rating: 5, comment: "Amazing biryani!" },
      { user: "Bhavana", rating: 4, comment: "Great place!" }
    ],
    contact: { phone: "1234567890", email: "contact@biryanihouse.com" }
  },
  {
    name: "Curry Palace",
    cuisine: "Indian",
    location: "Downtown",
    reviews: [
      { user: "Gaurav", rating: 4, comment: "Spicy and tasty!" },
      { user: "Harini", rating: 5, comment: "Best curry in town!" }
    ],
    contact: { phone: "0987654321", email: "contact@currypalace.com" }
  },
  {
    name: "Taco Stand",
    cuisine: "Mexican",
    location: "Downtown",
    reviews: [
      { user: "Ishaan", rating: 5, comment: "Fantastic tacos!" },
      { user: "Jaya", rating: 4, comment: "Very authentic" }
    ],
    contact: { phone: "1122334455", email: "contact@tacostand.com" }
  }
])

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 field
db.restaurants.createIndex({ "contact.email": 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 field
db.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 fields
db.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 field
db.restaurants.createIndex({ reviews: 1 })

Step 3: Verify Indexes

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

// Verify the created indexes
db.restaurants.getIndexes()

Output

test> use restaurantDB
switched to db restaurantDB
restaurantDB> db.restaurants.insertMany([
...   {
...     name: "Biryani House",
...     cuisine: "Indian",
...     location: "Downtown",
...     reviews: [
...       { user: "Aarav", rating: 5, comment: "Amazing biryani!" },
...       { user: "Bhavana", rating: 4, comment: "Great place!" }
...     ],
...     contact: { phone: "1234567890", email: "contact@biryanihouse.com" }
...   },
...   {
...     name: "Curry Palace",
...     cuisine: "Indian",
...     location: "Downtown",
...     reviews: [
...       { user: "Gaurav", rating: 4, comment: "Spicy and tasty!" },
...       { user: "Harini", rating: 5, comment: "Best curry in town!" }
...     ],
...     contact: { phone: "0987654321", email: "contact@currypalace.com" }
...   },
...   {
...     name: "Taco Stand",
...     cuisine: "Mexican",
...     location: "Downtown",
...     reviews: [
...       { user: "Ishaan", rating: 5, comment: "Fantastic tacos!" },
...       { user: "Jaya", rating: 4, comment: "Very authentic" }
...     ],
...     contact: { phone: "1122334455", email: "contact@tacostand.com" }
...   }
... ])
{
  acknowledged: true,
  insertedIds: {
    '0': ObjectId('667b3c596809bfbfae149f48'),
    '1': ObjectId('667b3c596809bfbfae149f49'),
    '2': ObjectId('667b3c596809bfbfae149f4a')
  }
}
restaurantDB> db.restaurants.createIndex({ "contact.email": 1 }, { unique: true })
contact.email_1
restaurantDB> db.restaurants.createIndex({ location: 1 }, { sparse: true })
location_1
restaurantDB> db.restaurants.createIndex({ name: 1, location: 1 })
name_1_location_1
restaurantDB> db.restaurants.createIndex({ reviews: 1 })
reviews_1
restaurantDB> db.restaurants.getIndexes()
[
  { v: 2, key: { _id: 1 }, name: '_id_' },
  {
    v: 2,
    key: { 'contact.email': 1 },
    name: 'contact.email_1',
    unique: true
  },
  { v: 2, key: { location: 1 }, name: 'location_1', sparse: true },
  { v: 2, key: { name: 1, location: 1 }, name: 'name_1_location_1' },
  { v: 2, key: { reviews: 1 }, name: 'reviews_1' }
]
restaurantDB>

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.

Let’s demonstrate the optimization of queries using indexes on a ProgrammingBooksDB database with a books collection. We’ll cover the creation of unique, sparse, compound, and multikey indexes and show how they optimize query performance.

Step 1: Create the Database and Collection

First, set up the ProgrammingBooksDB database and insert sample documents into the books collection.

// Switch to the ProgrammingBooksDB database
use ProgrammingBooksDB

// Insert sample documents into the books collection
db.books.insertMany([
  {
    title: "Learning Python",
    author: "Mark Lutz",
    genre: "Programming",
    publication_year: 2013,
    reviews: [
      { user: "Aarav", rating: 5, comment: "Excellent resource!" },
      { user: "Bhavana", rating: 4, comment: "Very detailed." }
    ],
    isbn: "978-1449355739"
  },
  {
    title: "JavaScript: The Good Parts",
    author: "Douglas Crockford",
    genre: "Programming",
    publication_year: 2008,
    reviews: [
      { user: "Chirag", rating: 5, comment: "Must-read for JS developers." },
      { user: "Devika", rating: 4, comment: "Insightful and concise." }
    ],
    isbn: "978-0596517748"
  },
  {
    title: "Clean Code",
    author: "Robert C. Martin",
    genre: "Programming",
    publication_year: 2008,
    reviews: [
      { user: "Esha", rating: 5, comment: "Invaluable for writing clean code." },
      { user: "Farhan", rating: 4, comment: "Great principles and practices." }
    ],
    isbn: "978-0132350884"
  },
  {
    title: "You Don't Know JS: Scope & Closures",
    author: "Kyle Simpson",
    genre: "Programming",
    publication_year: 2014,
    reviews: [
      { user: "Gaurav", rating: 4, comment: "Deep dive into JS." },
      { user: "Harini", rating: 5, comment: "Very informative." }
    ],
    isbn: "978-1449335588"
  },
  {
    title: "The Pragmatic Programmer",
    author: "Andrew Hunt",
    genre: "Programming",
    publication_year: 1999,
    reviews: [
      { user: "Ishaan", rating: 5, comment: "Timeless advice for programmers." },
      { user: "Jaya", rating: 5, comment: "Highly recommended." }
    ],
    isbn: "978-0201616224"
  }
])

Step 2: Create Various Indexes

1. Unique Index

A unique index ensures that the indexed field does not contain duplicate values. We’ll use the isbn field for this example.

// Create a unique index on the isbn field
db.books.createIndex({ isbn: 1 }, { unique: true })

2. Sparse Index

A sparse index only includes documents that contain the indexed field, ignoring documents where the field is missing. We’ll use the publication_year field for this example.

// Create a sparse index on the publication_year field
db.books.createIndex({ publication_year: 1 }, { sparse: true })

3. Compound Index

A compound index indexes multiple fields within a single index. We’ll use the author and title fields for this example.

// Create a compound index on the author and title fields
db.books.createIndex({ author: 1, title: 1 })

4. Multikey Index

A multikey index is created on an array field, indexing each element of the array. We’ll use the reviews array field for this example.

// Create a multikey index on the reviews field
db.books.createIndex({ reviews: 1 })

Question 9

a.

b.

Question 10

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

Related Posts

Leave a Reply

Your email address will not be published. Required fields are marked *