4 changed files with 3 additions and 208 deletions
--- a/Practical/Practical
+++ b/Practical/Practical
@ -33,7 +33,7 @@ by 5000.
 ## Creating database & collection:
 ```json
-use empDB1
+use empDB
 db.createCollection("Employee")
 ```
--- a/Practical/Practical
+++ b/Practical/Practical
@ -33,7 +33,7 @@ Company_name is "Infosys".
 ## Creating database & collection:
 ```json
-use empDB1
+use empDB
 db.createCollection("Employee")
 ```
--- a/Practical/Practical
+++ b/Practical/Practical
@ -34,7 +34,7 @@ value "Pune" and the field Pin_code with the value "411001".
 ## Creating database & collection:
 ```json
-use empDB3
+use empDB
 db.createCollection("Employee")
 ```
--- a/Practical/Practical
+++ b/Practical/Practical
@ -1,205 +0,0 @@
 # M4 - Aggregation
 **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 empDB
 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: 9972410425,
  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 ${i}`,
    Designation: `${Math.floor(Math.random() * 5) + 1}`
  });
 }
 // Wait for it to insert 10000 documents
 // Time without index
 let startTime = new Date();
 db.Employee.find({ Emp_id: 7500 }).toArray();
 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 }).toArray();
 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: 51 ms<br>
    Time taken to search with index: 48 ms<br>
 </details>
 7. Return a List of Indexes on created on employee Collection.
 ```sql
 db.Employee.getIndexes()
 ```
 ---