Added queries for B1 and B2.

2024-10-16 11:27:45 +05:30 · 2024-10-16 11:27:45 +05:30 · 126d0dbe8a
commit 126d0dbe8a
parent a5722f05cf
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--- a/Practical/Assignment-B1/Queries-B1.md
+++ b/Practical/Assignment-B1/Queries-B1.md
@ -0,0 +1,207 @@
+# 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" })
+```
--- a/Practical/Assignment-B2/Queries-B2.md
+++ b/Practical/Assignment-B2/Queries-B2.md
@ -0,0 +1,177 @@
+## Queries
+
+### A
+
+1. Return Designation with Total Salary Above 200000:
+```mongodb
+db.Employee.aggregate([
+    {
+        $group: {
+            _id: "$Designation",
+            TotalSalary: { $sum: "$Salary" }
+        }
+    },
+    {
+        $match: {
+            TotalSalary: { $gt: 200000 }
+        }
+    }
+])
+```
+
+2. Find Employee with Total Salary for Each City with Designation "DBA":
+```mongodb
+db.Employee.aggregate([
+    {
+        $match: { Designation: "DBA" }
+    },
+    {
+        $group: {
+            _id: { $arrayElemAt: ["$Address.PAddr", 0] },
+            TotalSalary: { $sum: "$Salary" }
+        }
+    }
+])
+```
+
+3. Find Total Salary of Employee with Designation "DBA" for Each Company:
+```mongodb
+db.Employee.aggregate([
+    {
+        $match: { Designation: "DBA" }
+    },
+    {
+        $group: {
+            _id: "$Company_name",
+            TotalSalary: { $sum: "$Salary" }
+        }
+    }
+])
+```
+
+4. Returns Names and _id in Upper Case and in Alphabetical Order:
+```mongodb
+db.Employee.aggregate([
+    {
+        $project: {
+            _id: 1,
+            Name: { $toUpper: { $concat: ["$Name.FName", " ", "$Name.LName"] } }
+        }
+    },
+    {
+        $sort: { Name: 1 }
+    }
+])
+```
+
+5. Count All Records from Collection:
+```mongodb
+db.Employee.countDocuments()
+```
+
+6. For Each Unique Designation, Find Avg Salary and Output Sorted by AvgSal:
+```mongodb
+db.Employee.aggregate([
+    {
+        $group: {
+            _id: "$Designation",
+            AvgSalary: { $avg: "$Salary" }
+        }
+    },
+    {
+        $sort: { AvgSalary: 1 }
+    }
+])
+```
+
+7. Return Separate Value in the Expertise Array Where Name of Employee is "Swapnil":
+```mongodb
+db.Employee.aggregate([
+    {
+        $match: { "Name.FName": "Swapnil" }
+    },
+    {
+        $unwind: "$Expertise"
+    },
+    {
+        $project: { Expertise: 1 }
+    }
+])
+```
+
+8. Return Separate Value in the Expertise Array and Return Sum of Each Element of Array:
+```mongodb
+db.Employee.aggregate([
+    {
+        $unwind: "$Expertise"
+    },
+    {
+        $group: {
+            _id: "$Expertise",
+            TotalCount: { $sum: 1 }
+        }
+    }
+])
+```
+
+9. Return Array for Designation Whose Address is "Pune":
+```mongodb
+db.Employee.aggregate([
+    {
+        $match: { "Address.PAddr": { $regex: "Pune" } }
+    },
+    {
+        $project: { Designation: 1 }
+    }
+])
+```
+
+10. Return Max and Min Salary for Each Company:
+```mongodb
+db.Employee.aggregate([
+    {
+        $group: {
+            _id: "$Company_name",
+            MaxSalary: { $max: "$Salary" },
+            MinSalary: { $min: "$Salary" }
+        }
+    }
+])
+```
+
+### B
+
+1. Create Single Field Indexes on Designation:
+```mongodb
+db.Employee.createIndex({ Designation: 1 })
+```
+
+2. Create Compound Indexes on Name and Age:
+```mongodb
+db.Employee.createIndex({ "Name.FName": 1, Age: -1 })
+```
+
+3. Create Multikey Indexes on Expertise Array:
+```mongodb
+db.Employee.createIndex({ Expertise: 1 })
+```
+
+4. Return a List of All Indexes on Collection:
+```mongodb
+db.Employee.getIndexes()
+```
+
+5. Rebuild Indexes:
+```mongodb
+db.Employee.reIndex()
+```
+
+6. Drop Index on Remove Specific Index:
+```mongodb
+db.Employee.dropIndex("empIndex") 
+```
+
+7. Remove All Indexes Except for the _id Index from a Collection:
+```mongodb
+db.Employee.dropIndexes()
+```