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ba4b4629ce
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6eae704642
@ -51,8 +51,8 @@ db.Employee.insertMany([
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Expertise: ['Docker', 'Linux', 'Networking', 'Politics'],
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DOB: new Date("1969-03-12"),
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Email: "ayush.k@tcs.com",
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Contact: 9972410427,
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Address: [{PAddr: "Kokan, Maharashtra"}, {LAddr: "Lohegaon, Pune"}]
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contact: 9972410427,
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address: [{PAddr: "Kokan, Maharashtra"}, {LAddr: "Lohegaon, Pune"}]
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},
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{
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Name: {FName: "Mehul", LName: "Patil"},
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@ -63,8 +63,8 @@ db.Employee.insertMany([
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Expertise: ['HTML', 'CSS', 'Javascript', 'Teaching'],
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DOB: new Date("1964-06-22"),
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Email: "mehul.p@mepa.com",
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Contact: 9972410426,
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Address: [{PAddr: "NDB, Maharashtra"}, {LAddr: "Camp, Pune"}]
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contact: 9972410426,
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address: [{PAddr: "NDB, Maharashtra"}, {LAddr: "Camp, Pune"}]
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},
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{
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Name: {FName: "Himanshu", LName: "Patil"},
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@ -75,8 +75,8 @@ db.Employee.insertMany([
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Expertise: ['Mongodb', 'Mysql', 'Cassandra', 'Farming'],
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DOB: new Date("1957-04-28"),
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Email: "himanshu.p@infosys.com",
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Contact: 9972410425,
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Address: [{PAddr: "NDB, Maharashtra"}, {LAddr: "Camp, Pune"}]
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contact: 9972410425,
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address: [{PAddr: "NDB, Maharashtra"}, {LAddr: "Camp, Pune"}]
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}
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])
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@ -51,8 +51,8 @@ db.Employee.insertMany([
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Expertise: ['Docker', 'Linux', 'Networking', 'Politics'],
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DOB: new Date("1998-03-12"),
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Email: "ayush.k@tcs.com",
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Contact: 9972410427,
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Address: [{PAddr: "Kokan, Maharashtra"}, {LAddr: "Lohegaon, Pune"}]
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contact: 9972410427,
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address: [{PAddr: "Kokan, Maharashtra"}, {LAddr: "Lohegaon, Pune"}]
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},
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{
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Name: {FName: "Mehul", LName: "Patil"},
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@ -63,8 +63,8 @@ db.Employee.insertMany([
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Expertise: ['HTML', 'CSS', 'Javascript', 'Teaching'],
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DOB: new Date("1964-06-22"),
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Email: "mehul.p@mepa.com",
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Contact: 9972410426,
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Address: [{PAddr: "NDB, Maharashtra"}, {LAddr: "Camp, Pune"}]
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contact: 9972410426,
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address: [{PAddr: "NDB, Maharashtra"}, {LAddr: "Camp, Pune"}]
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},
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{
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Name: {FName: "Himanshu", LName: "Patil"},
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@ -75,8 +75,8 @@ db.Employee.insertMany([
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Expertise: ['Mongodb', 'Mysql', 'Cassandra', 'Farming'],
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DOB: new Date("1957-04-28"),
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Email: "himanshu.p@infosys.com",
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Contact: 9972410425,
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Address: [{PAddr: "NDB, Maharashtra"}, {LAddr: "Camp, Pune"}]
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contact: 9972410425,
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address: [{PAddr: "NDB, Maharashtra"}, {LAddr: "Camp, Pune"}]
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}
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])
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@ -1,158 +0,0 @@
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# M2 - Crud Operations
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**Problem Statement:**
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Design and Develop MongoDB Queries using CRUD operations:
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Create Employee collection by considering following Fields:
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i. Emp_id : Number
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ii. Name: Embedded Doc (FName, LName)
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iii. Company Name: String
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iv. Salary: Number
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v. Designation: String
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vi. Age: Number
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vii. Expertise: Array
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viii. DOB: String or Date
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ix. Email id: String
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x. Contact: String
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xi. Address: Array of Embedded Doc (PAddr, LAddr)
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Insert at least 5 documents in collection by considering above
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attribute and execute following queries:
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1. Creates a new document if no document in the employee collection
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contains
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{Designation: "Tester", Company_name: "TCS", Age: 25}
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2. Finds all employees working with Company_name: "TCS" and
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increase their salary by 2000.
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3. Matches all documents where the value of the field Address is an
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embedded document that contains only the field city with the
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value "Pune" and the field Pin_code with the value "411001".
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4. Find employee details who are working as "Developer" or
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"Tester".
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5. Drop Single documents where designation="Developer".
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6. Count number of documents in employee collection.
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---
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## Creating database & collection:
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```json
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use empDB
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db.createCollection("Employee")
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```
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## Inserting data:
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```json
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db.Employee.insertMany([
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{
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Name: {FName: "Ayush", LName: "Kalaskar"},
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Company: "TCS",
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Salary: 45000,
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Designation: "Programmer",
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Age: 24,
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Expertise: ['Docker', 'Linux', 'Networking', 'Politics'],
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DOB: new Date("1998-03-12"),
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Email: "ayush.k@tcs.com",
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Contact: 9972410427,
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Address: [{PAddr: "Kokan, Maharashtra"}, {LAddr: "Lohegaon, Pune", Pin_code: 411014}]
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},
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{
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Name: {FName: "Mehul", LName: "Patil"},
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Company: "MEPA",
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Salary: 55000,
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Designation: "Tester",
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Age: 20,
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Expertise: ['HTML', 'CSS', 'Javascript', 'Teaching'],
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DOB: new Date("1964-06-22"),
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Email: "mehul.p@mepa.com",
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Contact: 9972410426,
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Address: [{PAddr: "NDB, Maharashtra"}, {LAddr: "Camp, Pune", Pin_code: 411001}]
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},
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{
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Name: {FName: "Himanshu", LName: "Patil"},
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Company: "Infosys",
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Salary: 85000,
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Designation: "Developer",
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Age: 67,
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Expertise: ['Mongodb', 'Mysql', 'Cassandra', 'Farming'],
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DOB: new Date("1957-04-28"),
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Email: "himanshu.p@infosys.com",
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Contact: 9972410425,
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Address: [{PAddr: "NDB, Maharashtra"}, {LAddr: "Camp, Pune", Pin_code: 411001}]
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}
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])
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```
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## Queries
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1. Creates a new document if no document in the employee collection contains `{Designation: "Tester", Company_name: "TCS", Age: 25}`
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```json
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db.Employee.updateOne(
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{Designation: "Tester", Company: "TCS", Age: 25},
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{ $setOnInsert: {
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Name: {FName: "Karan", LName: "Salvi"},
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Salary: 67500,
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Expertise: ['Blockchain', 'C++', 'Python', 'Fishing'],
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DOB: new Date("1999-11-01"),
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Email: "karan.s@tcs.com",
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Contact: 9972410424,
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Address: [{PAddr: "Kolhapur, Maharashtra"}, {LAddr: "Viman Nagar, Pune", Pin_code: 411014}]
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}
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},
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{ upsert: true }
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)
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```
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2. Finds all employees working with Company_name: "TCS" and increase their salary by 2000.
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```json
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db.Employee.updateMany(
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{ Company: "TCS" },
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{ $inc: { Salary: 2000 } }
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)
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```
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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".
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```json
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db.Employee.find(
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{ $or: [
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{
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"Address.Pin_code": 411001,
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"Address.LAddr": { $regex: /Pune/i }
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},
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{
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"Address.Pin_code": 411001,
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"Address.PAddr": { $regex: /Pune/i }
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}
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]
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}
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)
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```
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4. Find employee details who are working as "Developer" or "Tester".
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```json
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db.Employee.find(
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{ $or: [
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{ Designation: "Developer" },
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{ Designation: "Tester" }
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]
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}
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)
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```
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5. Drop Single documents where Designation="Developer"
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```json
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db.Employee.deleteMany( { Designation: "Developer" } )
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```
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6. Count number of documents in employee collection.
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```json
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db.Employee.countDocuments();
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```
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---
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