Added queries for M4 (MongoDB).

2024-11-10 12:08:48 +05:30 · 2024-11-10 12:08:48 +05:30 · 964f534b16
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+# 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()
+
+```
+
+---