# Queries-B1 ## Creation ```mongodb use empDB db.createCollection("Employee") ``` ## Inserting data ```mongodb db.Employee.insertMany([ { Empid: 1, Name: { FName: "Rahul", LName: "Sharma" }, Company_name: "TCS", Salary: 50000, Designation: "Programmer", Age: 28, Expertise: ["Java", "Spring", "MongoDB"], DOB: "1995-04-15", Email_id: "rahul.sharma@example.com", Contact: "9876543210", Address: [{ PAddr: "123, Street A, Pune", LAddr: "Maharashtra" }] }, { Empid: 2, Name: { FName: "Sita", LName: "Patel" }, Company_name: "Infosys", Salary: 60000, Designation: "Developer", Age: 30, Expertise: ["JavaScript", "React", "Node.js"], DOB: "1993-06-25", Email_id: "sita.patel@example.com", Contact: "9876543211", Address: [{ PAddr: "234, Street B, Bangalore", LAddr: "Karnataka" }] }, { Empid: 3, Name: { FName: "Anil", LName: "Kumar" }, Company_name: "Wipro", Salary: 45000, Designation: "Tester", Age: 29, Expertise: ["Selenium", "Python"], DOB: "1994-08-12", Email_id: "anil.kumar@example.com", Contact: "9876543212", Address: [{ PAddr: "345, Street C, Hyderabad", LAddr: "Telangana" }] }, { Empid: 4, Name: { FName: "Priya", LName: "Verma" }, Company_name: "Infosys", Salary: 70000, Designation: "Project Manager", Age: 35, Expertise: ["Agile", "Scrum"], DOB: "1988-02-20", Email_id: "priya.verma@example.com", Contact: "9876543213", Address: [{ PAddr: "456, Street D, Chennai", LAddr: "Tamil Nadu" }] }, { Empid: 5, Name: { FName: "Raj", LName: "Singh" }, Company_name: "TCS", Salary: 32000, Designation: "Programmer", Age: 27, Expertise: ["Java", "Angular"], DOB: "1996-03-30", Email_id: "raj.singh@example.com", Contact: "9876543214", Address: [{ PAddr: "567, Street E, Delhi", LAddr: "Delhi" }] }, { Empid: 6, Name: { FName: "Neha", LName: "Iyer" }, Company_name: "HCL", Salary: 50000, Designation: "Designer", Age: 32, Expertise: ["Photoshop", "Illustrator"], DOB: "1991-11-11", Email_id: "neha.iyer@example.com", Contact: "9876543215", Address: [{ PAddr: "678, Street F, Kolkata", LAddr: "West Bengal" }] }, { Empid: 7, Name: { FName: "Karan", LName: "Bansal" }, Company_name: "TCS", Salary: 45000, Designation: "Tester", Age: 26, Expertise: ["Selenium", "Java"], DOB: "1997-07-07", Email_id: "karan.bansal@example.com", Contact: "9876543216", Address: [{ PAddr: "789, Street G, Pune", LAddr: "Maharashtra" }] }, { Empid: 8, Name: { FName: "Aarti", LName: "Mehta" }, Company_name: "Accenture", Salary: 80000, Designation: "Architect", Age: 33, Expertise: ["Cloud", "Microservices"], DOB: "1990-01-01", Email_id: "aarti.mehta@example.com", Contact: "9876543217", Address: [{ PAddr: "890, Street H, Noida", LAddr: "Uttar Pradesh" }] }, { Empid: 9, Name: { FName: "Vikram", LName: "Yadav" }, Company_name: "Infosys", Salary: 40000, Designation: "Developer", Age: 31, Expertise: ["C#", ".NET"], DOB: "1992-05-05", Email_id: "vikram.yadav@example.com", Contact: "9876543218", Address: [{ PAddr: "901, Street I, Jaipur", LAddr: "Rajasthan" }] }, { Empid: 10, Name: { FName: "Sneha", LName: "Dutta" }, Company_name: "Wipro", Salary: 39000, Designation: "HR", Age: 29, Expertise: ["Recruitment", "Employee Relations"], DOB: "1994-09-09", Email_id: "sneha.dutta@example.com", Contact: "9876543219", Address: [{ PAddr: "1234, Street J, Ahmedabad", LAddr: "Gujarat" }] } ]) ``` ## Queries 1. Select all documents where Designation is "Programmer" and Salary > 30000: ```mongodb db.Employee.find({ Designation: "Programmer", Salary: { $gt: 30000 } }) ``` 2. Create a new document if no document contains `{Designation: "Tester", Company_name: "TCS", Age: 25}`: ```mongodb db.Employee.update( { Designation: "Tester", Company_name: "TCS", Age: 25 }, { $setOnInsert: { Empid: 11, Name: { FName: "New", LName: "Tester" }, Salary: 30000, DOB: "1998-01-01", Email_id: "new.tester@example.com", Contact: "9876543220", Address: [{ PAddr: "New Address", LAddr: "New City" }] } }, { upsert: true } ) ``` 3. Select all documents where Age < 30 or Salary > 40000: ```mongodb db.Employee.find({ $or: [{ Age: { $lt: 30 } }, { Salary: { $gt: 40000 } }] }) ``` 4. Match documents where Address contains city "Pune" and Pin_code "411001": ```mongodb db.Employee.find({ Address: { $elemMatch: { city: "Pune", Pin_code: "411001" } } }) ``` 5. Find all documents with Company_name "TCS" and modify their salary by 2000: ```mongodb db.Employee.updateMany( { Company_name: "TCS" }, { $inc: { Salary: 2000 } } ) ``` 6. Find documents where Designation is not equal to "Developer": ```mongodb db.Employee.find({ Designation: { $ne: "Developer" } }) ``` 7. Find _id, Designation, Address, and Name where Company_name is "Infosys": ```mongodb db.Employee.find( { Company_name: "Infosys" }, { _id: 1, Designation: 1, Address: 1, Name: 1 } ) ``` 8. Select all documents where Designation is either "Developer" or "Tester": ```mongodb db.Employee.find({ Designation: { $in: ["Developer", "Tester"] } }) ``` 9. Find all documents with exact match on Expertise array: ```mongodb db.Employee.find({ Expertise: { $all: ['Mongodb', 'Mysql', 'Cassandra'] } }) ``` 10. Drop single documents where Designation is "Developer": ```mongodb db.Employee.deleteMany({ Designation: "Developer" }) ```