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Added queries for M3 (crud operations)

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# M2 - Crud Operations
**Problem Statement:**
Design and Develop MongoDB Queries using CRUD 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 queries:
1. Creates a new document if no document in the employee collection
contains
{Designation: "Tester", Company_name: "TCS", Age: 25}
2. Finds all employees working with Company_name: "TCS" and
increase their salary by 2000.
3. Matches all documents where the value of the field Address is an
embedded document that contains only the field city with the
value "Pune" and the field Pin_code with the value "411001".
4. Find employee details who are working as "Developer" or
"Tester".
5. Drop Single documents where designation="Developer".
6. Count number of documents in 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}]
}
])
```
## Queries
1. Creates a new document if no document in the employee collection contains `{Designation: "Tester", Company_name: "TCS", Age: 25}`
```json
db.Employee.updateOne(
{Designation: "Tester", Company: "TCS", Age: 25},
{ $setOnInsert: {
Name: {FName: "Karan", LName: "Salvi"},
Salary: 67500,
Expertise: ['Blockchain', 'C++', 'Python', 'Fishing'],
DOB: new Date("1999-11-01"),
Email: "karan.s@tcs.com",
Contact: 9972410424,
Address: [{PAddr: "Kolhapur, Maharashtra"}, {LAddr: "Viman Nagar, Pune", Pin_code: 411014}]
}
},
{ upsert: true }
)
```
2. Finds all employees working with Company_name: "TCS" and increase their salary by 2000.
```json
db.Employee.updateMany(
{ Company: "TCS" },
{ $inc: { Salary: 2000 } }
)
```
3. Matches all documents where the value of the field Address is an embedded document that contains only the field city with the value "Pune" and the field Pin_code with the value "411001".
```json
db.Employee.find(
{ $or: [
{
"Address.Pin_code": 411001,
"Address.LAddr": { $regex: /Pune/i }
},
{
"Address.Pin_code": 411001,
"Address.PAddr": { $regex: /Pune/i }
}
]
}
)
```
4. Find employee details who are working as "Developer" or "Tester".
```json
db.Employee.find(
{ $or: [
{ Designation: "Developer" },
{ Designation: "Tester" }
]
}
)
```
5. Drop Single documents where Designation="Developer"
```json
db.Employee.deleteMany( { Designation: "Developer" } )
```
6. Count number of documents in employee collection.
```json
db.Employee.countDocuments();
```
---