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
200000.
2. Using Aggregate method returns names and _id in upper case and
in alphabetical order.
3. Using aggregation method find Employee with Total Salary for
Each City with Designation="DBA".
4. Create Single Field Indexes on Designation field of employee
collection
5. To Create Multikey Indexes on Expertise field of employee
collection.
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)
7. Return a List of Indexes on created on employee Collection.

---

## Creating database & collection:

```json
use empDB2
db.createCollection("Employee")

```

## Inserting data:

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

```

4. Create Single Field Indexes on Designation field of employee collection
```json
db.Employee.createIndex( { Designation: 1 } )

```

5. To Create Multikey Indexes on Expertise field of employee collection.
```json
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)
```json
// 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");

```

<details>
  <summary>Output for query 6:</summary>
    Time taken to search without index: 41 ms<br>
    Time taken to search with index: 29 ms<br>
</details>

7. Return a List of Indexes on created on employee Collection.
```sql
db.Employee.getIndexes()

```

---