From 51a7f8cb493f99a1f6d248e7a52f8a3515a46f06 Mon Sep 17 00:00:00 2001
From: Kshitij <kshitijk@tuta.io>
Date: Sun, 10 Nov 2024 12:51:21 +0530
Subject: [PATCH] Added queries for M5 (MongoDB).

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+# 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();
+
+```
+
+---