218 lines
5.0 KiB
Markdown
218 lines
5.0 KiB
Markdown
# M5 - Aggregation and Indexing
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**Problem Statement:**
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Design and Develop MongoDB Queries using Aggregation 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:
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1. Using aggregation Return separates value in the Expertise array
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and return sum of each element of array.
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2. Using Aggregate method return Max and Min Salary for each
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company.
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3. Using Aggregate method find Employee with Total Salary for Each
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City with Designation="DBA".
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4. Using aggregation method Return separates value in the Expertise
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array for employee name where Swapnil Jadhav
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5. To Create Compound Indexes on Name: 1, Age: -1
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6. Create an Index on Emp_id field, compare the time require to
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search Emp_id before and after creating an index. (Hint Add at
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least 10000 Documents)
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7. Return a List of Indexes on created on employee Collection.
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---
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## Creating database & collection:
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```json
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use empDB3
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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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Name: {FName: "Swapnil", LName: "Jadhav"},
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Company: "Wayne Industries",
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Salary: 95000,
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Designation: "DBA",
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Age: 75,
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Expertise: ['Blockchain', 'Hashing', 'Encryption', 'Nerd'],
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DOB: new Date("1949-12-28"),
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Email: "swapnil.j@wayne.com",
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Contact: 9972410427,
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Address: [{PAddr: "Viman Nagar, Pune"}, {LAddr: "Viman Nagar, 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. Using aggregation Return separates value in the Expertise array and return sum of each element of array.
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```json
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db.Employee.aggregate([
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{
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$unwind: "$Expertise"
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},
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{
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$group: {
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_id: "$Expertise",
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count: { $sum: 1 }
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}
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}
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])
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```
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2. Using Aggregate method return Max and Min Salary for each company.
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```json
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db.Employee.aggregate([
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{
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$group: {
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_id: "$Company",
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MIN: { $min: "$Salary" },
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MAX: { $max: "$Salary" }
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}
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}
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])
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```
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3. Using Aggregate method find Employee with Total Salary for Each City with Designation="DBA".
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```json
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db.Employee.aggregate([
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{
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$match: {
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Designation: "DBA"
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}
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},
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{
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$group: {
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_id: "$Address.PAddr",
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Total: { $sum: "$Salary" }
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}
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}
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])
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```
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4. Using aggregation method Return separates value in the Expertise array for employee name where Swapnil Jadhav
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```json
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db.Employee.aggregate([
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{
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$match: {
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"Name.FName": "Swapnil",
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"Name.LName": "Jadhav"
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}
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},
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{
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$unwind: "$Expertise"
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},
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{
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$group: {
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_id: "$Expertise"
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}
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}
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])
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```
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5. To Create Compound Indexes on Name: 1, Age: -1
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```json
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db.Employee.createIndex({Name: 1, Age: -1})
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```
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6. 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)
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```json
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// Creating 10000 documents
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for (let i=1; i<=10000; i++) {
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db.Employee.insertOne({
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Emp_id: i,
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Name: `Employee ${i}`,
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Designation: `Work ${i*5}`
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});
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}
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// Wait for it to insert 10000 documents!
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// Time without index
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let startTime = new Date();
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db.Employee.find( { Emp_id: 7500 } );
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let endTime = new Date();
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print("Time taken to search before index: " + (endTime - startTime) + "ms");
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// Creating index on Emp_id
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db.Employee.createIndex( { Emp_id: 1 } )
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// Time with index
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startTime = new Date();
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db.Employee.find( { Emp_id: 7500 } );
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endTime = new Date();
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print("Time taken to search after index: " + (endTime - startTime) + "ms")
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```
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<details>
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<summary>Output for query 6:</summary>
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Time taken to search before index: 57ms<br>
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Time taken to search after index: 35ms
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</details>
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7. Return a List of Indexes on created on employee Collection.
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```json
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db.Employee.getIndexes();
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```
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---
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