Added queries for M6 (mongo).
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Practical/Practical Exam/MongoDB/M6 - Map-reduce.md
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Practical/Practical Exam/MongoDB/M6 - Map-reduce.md
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# M6 - Map-reduce
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**Problem statement:**
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Design MongoDB database and perform following Map reduce operation:
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Create Employee collection by considering following Fields:
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i. Name: Embedded Doc (FName, LName)
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ii. Company Name: String
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iii. Salary: Number
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iv. Designation: String
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v. Age: Number
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vi. Expertise: Array
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vii. DOB: String or Date
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viii. Email id: String
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ix. Contact: String
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x. Address: Array of Embedded Doc (PAddr, LAddr)
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Execute the following query:
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---
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## Creating database
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```mongo
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use mapred;
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```
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## Inserting data:
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```mongo
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db.emp.insertMany([
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{
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Name: {FName: "Tanmay", LName: "Machkar"},
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Company: "TCS",
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Salary: 40000,
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Designation: "Tester",
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Age: 25,
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Expertise: ['Mongodb','Mysql','Cassandra'],
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DOB: new Date("2003-12-02"),
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Email: "xyz@gmail.com",
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Contact: 1234567890,
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Address: [{PAddr: {City: "Pune", Rd: "Bharatmata"}}, {LAddr: {Pin_code: 411001}}]
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},
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{
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Name: {FName: "Rajesh", LName: "Machkar"},
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Company: "Infosys",
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Salary: 50000,
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Designation: "Programmer",
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Age: 29,
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Expertise: ['Mongodb','Mysql'],
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DOB: new Date("1990-12-02"),
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Email: "xy@gmail.com",
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Contact: 1234567809,
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Address: [{PAddr: {City: "Pune", Rd: "JM"}}, {LAddr: {Pin_code: 411047}}]
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},
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{
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Name: {FName: "Tejas", LName: "Machkar"},
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Company: "TCS",
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Salary: 20000,
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Designation: "Developer",
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Age: 35,
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Expertise: ['Mongodb'],
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DOB: new Date("2000-11-22"),
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Email: "x@gmail.com",
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Contact: 1234567089,
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Address: [{PAddr: {City: "Mumbai", Rd: "Airport"}}, {LAddr: {Pin_code: 411030}}]
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},
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{
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Name: {FName: "Deepali", LName: "Machkar"},
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Company: "Persistent",
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Salary: 40000,
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Designation: "Tester",
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Age: 30,
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Expertise: ['Mysql'],
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DOB: new Date("1978-02-12"),
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Email: "y@gmail.com",
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Contact: 1234506789,
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Address: [{PAddr: {City: "Phaltan", Rd: "Highway"}}, {LAddr: {Pin_code: 411012}}]
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},
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{
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Name: {FName: "Om", LName: "Deokar"},
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Company: "Persistent",
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Salary: 25000,
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Designation: "Programmer",
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Age: 39,
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Expertise: ['Cassandra'],
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DOB: new Date("2007-03-15"),
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Email: "z@gmail.com",
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Contact: 1234567890,
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Address: [{PAddr: {City: "Jaipur", Rd: "Pink"}}, {LAddr: {Pin_code: 411056}}]
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},
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])
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```
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## Queries
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1. Display the total salary of per company.
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```mongo
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db.emp.mapReduce(
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function(){
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emit(this.Company, this.Salary);
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},
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function(key, values){
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return Array.sum(values);
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},
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{ out: "query1"}
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)
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db.query1.find()
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```
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2. Display the total salary of company Name:"TCS".
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```mongo
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db.emp.mapReduce(
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function(){
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if(this.Company == "TCS"){
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emit(this.Company, this.Salary);
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}
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},
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function(key, values){
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return Array.sum(values);
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},
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{ out: "query2"}
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)
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db.query2.find()
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```
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3. Return the average salary of company whose address is “Pune".
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```mongo
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db.emp.mapReduce(
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function(){
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if(this.Address[0].PAddr.City === "Pune"){
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emit(this.Company, this.Salary);
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}
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},
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function(key, values){
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return Array.avg(values);
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},
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{ out: "query3"}
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)
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db.query3.find()
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```
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4. Display total count for “City=Pune”.
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```mongo
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db.emp.mapReduce(
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function(){
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if(this.Address[0].PAddr.City === "Pune"){
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emit("Count", 1);
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}
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},
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function(key, values){
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return Array.sum(values);
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},
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{ out: "query4"}
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)
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db.query4.find()
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```
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5. Return count for city pune and age greater than 40.
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```mongo
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db.emp.mapReduce(
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function(){
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if(this.Address[0].PAddr.City === "Pune" && this.Age > 25){
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emit("Count", 1);
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}
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},
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function(key, values){
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return Array.sum(values);
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},
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{ out: "query5"}
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)
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db.query5.find()
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```
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---
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