Practical/Practical Exam/MongoDB/M4 - Aggregation.md

@@ -1,4 +1,4 @@
-# M4 - Aggregation and Indexing
+# M4 - Aggregation
 
 **Problem Statement:**
 Design and Develop MongoDB Queries using Aggregation operations:
@@ -90,7 +90,7 @@ db.Employee.insertMany([
   Expertise: ['Blockchain', 'Hashing', 'Encryption', 'Nerd'],
   DOB: new Date("1949-12-28"),
   Email: "tanmay.m@wayne.com",
-  Contact: 9972410426,
+  Contact: 9972410425,
   Address: [{PAddr: "Viman Nagar, Pune"}, {LAddr: "Viman Nagar, Pune", Pin_code: 411001}]
 }
 ])

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

@@ -1,217 +0,0 @@
-# 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();
-
-```
-
----