4.9 KiB
4.9 KiB
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:
- Using aggregation Return Designation with Total Salary is Above
- Using Aggregate method returns names and _id in upper case and in alphabetical order.
- Using aggregation method find Employee with Total Salary for Each City with Designation="DBA".
- Create Single Field Indexes on Designation field of employee collection
- To Create Multikey Indexes on Expertise field of employee collection.
- 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)
- Return a List of Indexes on created on employee Collection.
Creating database & collection:
use empDB2
db.createCollection("Employee")
Inserting data:
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
- Using aggregation Return Designation with Total Salary is Above 200000.
db.Employee.aggregate([
{
$group: {
_id: "$Designation",
TotalSalary: { $sum: "$Salary" }
}
},
{
$match: {
TotalSalary: { $gt: 20000 }
}
}
])
- Using Aggregate method returns names and _id in upper case and in alphabetical order.
db.Employee.aggregate([
{
$project: {
_id: 1,
Name: { $toUpper: { $concat: [ "$Name.FName", " ", "$Name.LName" ] } }
}
},
{ $sort: { Name: 1 } }
])
- Using aggregation method find Employee with Total Salary for Each City with Designation="DBA".
db.Employee.aggregate([
{
$match: {
Designation: "DBA"
}
},
{
$group: {
_id: "$Address.PAddr",
Salary: { $sum: "$Salary" }
}
}
])
- Create Single Field Indexes on Designation field of employee collection
db.Employee.createIndex( { Designation: 1 } )
- To Create Multikey Indexes on Expertise field of employee collection.
db.Employee.createIndex( { Expertise: 1 } )
- 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)
// 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");
Output for query 6:
Time taken to search without index: 41 msTime taken to search with index: 29 ms
- Return a List of Indexes on created on employee Collection.
db.Employee.getIndexes()