DatabaseManagementSystems/Practical/Practical Exam/MongoDB/M4 - Aggregation and Indexing.md

M4 - Aggregation and Indexing

Problem Statement: Design and Develop MongoDB Queries using Aggregation operations: Create Employee collection by considering following Fields: i. Emp_id : Number ii. Name: Embedded Doc (FName, LName) iii. Company Name: String iv. Salary: Number v. Designation: String vi. Age: Number vii. Expertise: Array viii. DOB: String or Date ix. Email id: String x. Contact: String xi. Address: Array of Embedded Doc (PAddr, LAddr) Insert at least 5 documents in collection by considering above attribute and execute following:

1. Using aggregation Return Designation with Total Salary is Above
3. Using Aggregate method returns names and _id in upper case and in alphabetical order.
4. Using aggregation method find Employee with Total Salary for Each City with Designation="DBA".
5. Create Single Field Indexes on Designation field of employee collection
6. To Create Multikey Indexes on Expertise field of employee collection.
7. Create an Index on Emp_id field, compare the time require to search Emp_id before and after creating an index. (Hint Add at least 10000 Documents)
8. Return a List of Indexes on created on employee Collection.

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Creating database & collection:

use empDB2
db.createCollection("Employee")

Inserting data:

db.Employee.insertMany([
{
  Name: {FName: "Ayush", LName: "Kalaskar"},
  Company: "TCS",
  Salary: 45000,
  Designation: "Programmer",
  Age: 24,
  Expertise: ['Docker', 'Linux', 'Networking', 'Politics'],
  DOB: new Date("1998-03-12"),
  Email: "ayush.k@tcs.com",
  Contact: 9972410427,
  Address: [{PAddr: "Kokan, Maharashtra"}, {LAddr: "Lohegaon, Pune", Pin_code: 411014}]
},
{
  Name: {FName: "Mehul", LName: "Patil"},
  Company: "MEPA",
  Salary: 55000,
  Designation: "Tester",
  Age: 20,
  Expertise: ['HTML', 'CSS', 'Javascript', 'Teaching'],
  DOB: new Date("1964-06-22"),
  Email: "mehul.p@mepa.com",
  Contact: 9972410426,
  Address: [{PAddr: "NDB, Maharashtra"}, {LAddr: "Camp, Pune", Pin_code: 411001}]
},
{
  Name: {FName: "Himanshu", LName: "Patil"},
  Company: "Infosys",
  Salary: 85000,
  Designation: "Developer",
  Age: 67,
  Expertise: ['Mongodb', 'Mysql', 'Cassandra', 'Farming'],
  DOB: new Date("1957-04-28"),
  Email: "himanshu.p@infosys.com",
  Contact: 9972410425,
  Address: [{PAddr: "NDB, Maharashtra"}, {LAddr: "Camp, Pune", Pin_code: 411001}]
},
{
  Name: {FName: "Tanmay", LName: "Macho"},
  Company: "Wayne Industries",
  Salary: 95000,
  Designation: "DBA",
  Age: 75,
  Expertise: ['Blockchain', 'Hashing', 'Encryption', 'Nerd'],
  DOB: new Date("1949-12-28"),
  Email: "tanmay.m@wayne.com",
  Contact: 9972410426,
  Address: [{PAddr: "Viman Nagar, Pune"}, {LAddr: "Viman Nagar, Pune", Pin_code: 411001}]
}
])

Queries

1. Using aggregation Return Designation with Total Salary is Above 200000.

db.Employee.aggregate([
  {
    $group: {
      _id: "$Designation",
      TotalSalary: { $sum: "$Salary" }
    }
  },
  {
    $match: {
      TotalSalary: { $gt: 20000 }
    }
  }
])

2. Using Aggregate method returns names and _id in upper case and in alphabetical order.

db.Employee.aggregate([
  {
    $project: {
      _id: 1,
      Name: { $toUpper: { $concat: [ "$Name.FName", " ", "$Name.LName" ] } }
    }
  },
  { $sort: { Name: 1 } }
])

3. Using aggregation method find Employee with Total Salary for Each City with Designation="DBA".

db.Employee.aggregate([
  {
    $match: {
      Designation: "DBA"
    }
  },
  {
    $group: {
      _id: "$Address.PAddr",
      Salary: { $sum: "$Salary" }
    }
  }
])

4. Create Single Field Indexes on Designation field of employee collection

db.Employee.createIndex( { Designation: 1 } )

5. To Create Multikey Indexes on Expertise field of employee collection.

db.Employee.createIndex( { Expertise: 1 } )

6. Create an Index on Emp_id field, compare the time require to search Emp_id before and after creating an index. (Hint Add at least 10000 Documents)

// Adding 1000 employees
for (let i = 1; i <= 10000; i++) {
  db.Employee.insertOne({
    Emp_id: i,
    Name: `Employee ${i}`,
    Designation: `Work ${i*5}`
  });
}
// Wait for it to insert 10000 documents!

// Time without index
let startTime = new Date();
db.Employee.find({ Emp_id: 7500 })
let endTime = new Date();
print("Time taken to search without index: " + (endTime - startTime) + " ms");

// Creating index on Emp_id
db.Employee.createIndex( { Emp_id: 1 });

// Time with index
startTime = new Date();
db.Employee.find({ Emp_id: 7500 })
endTime = new Date();
print("Time taken to search with index: " + (endTime - startTime) + " ms");

Output for query 6:

Time taken to search without index: 41 ms
Time taken to search with index: 29 ms

7. Return a List of Indexes on created on employee Collection.

db.Employee.getIndexes()

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