DatabaseManagementSystems/Practical/Practical Exam/MongoDB/M5 - Aggregation and Indexing.md

# M5 - 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 separates value in the Expertise array
and return sum of each element of array.
2. Using Aggregate method return Max and Min Salary for each
company.
3. Using Aggregate method find Employee with Total Salary for Each
City with Designation="DBA".
4. Using aggregation method Return separates value in the Expertise
array for employee name where Swapnil Jadhav
5. To Create Compound Indexes on Name: 1, Age: -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)
7. Return a List of Indexes on created on employee Collection.

---

## Creating database & collection:

```json
use empDB3
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: "Swapnil", LName: "Jadhav"},
  Company: "Wayne Industries",
  Salary: 95000,
  Designation: "DBA",
  Age: 75,
  Expertise: ['Blockchain', 'Hashing', 'Encryption', 'Nerd'],
  DOB: new Date("1949-12-28"),
  Email: "swapnil.j@wayne.com",
  Contact: 9972410427,
  Address: [{PAddr: "Viman Nagar, Pune"}, {LAddr: "Viman Nagar, Pune", Pin_code: 411001}]
}
])

```

## Queries

1. Using aggregation Return separates value in the Expertise array and return sum of each element of array.
```json
db.Employee.aggregate([
  {
    $unwind: "$Expertise"
  },
  {
    $group: {
      _id: "$Expertise",
      count: { $sum: 1 }
    }
  }
])

```

2. Using Aggregate method return Max and Min Salary for each company.
```json
db.Employee.aggregate([
  {
    $group: {
      _id: "$Company",
      MIN: { $min: "$Salary" },
      MAX: { $max: "$Salary" }
    }
  }
])

```

3. Using Aggregate method find Employee with Total Salary for Each City with Designation="DBA".
```json
db.Employee.aggregate([
  {
    $match: {
      Designation: "DBA"
    }
  },
  {
    $group: {
      _id: "$Address.PAddr",
      Total: { $sum: "$Salary" }
    }
  }
])

```

4. Using aggregation method Return separates value in the Expertise array for employee name where Swapnil Jadhav
```json
db.Employee.aggregate([
  {
    $match: {
    "Name.FName": "Swapnil",
    "Name.LName": "Jadhav"
    }
  },
  {
    $unwind: "$Expertise"
  },
  {
    $group: {
      _id: "$Expertise"
    }
  }
])

```

5. To Create Compound Indexes on Name: 1, Age: -1
```json
db.Employee.createIndex({Name: 1, Age: -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
// Creating 10000 documents
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 before 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 after index: " + (endTime - startTime) + "ms")

```

<details>
  <summary>Output for query 6:</summary>
    Time taken to search before index: 57ms<br>
    Time taken to search after index: 35ms
</details>

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

```

---