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How to read files with Node.js?

In this tutorial, we will explore different ways we can read files in Node.js.  In software development, reading files is a regular part of the job. However, this simple process very often becomes complicated and tedious. Fortunately, that's not the case in Node.js. Node.js provides a built-infile system(fs) module, to carry out file operations. Before we start, let us make sure we have the basic prerequisites. You will need to ensure you have: Node.js  Code Editor (Ex. VS Code, Atom) For this tutorial, my environment setup includes: Node.js - v12.16.1 Code Editor - VS Code fs module fs module is a built-in module in the Node.js. You do notnecessarily need to install it.  The fs module supports both Synchronous and Asynchronous function calls. Getting started I am assuming you have installed the NodeJS. Create a new directory node-fs.  Create 2 files inside the node-fs, data.txt and index.js. Paste the dummy data in the data.txt.Lorem ipsum dolor sit amet, consecteturadipiscingelit, sed do eiusmodtemporincididuntutlabore et dolore magna aliqua. Open this directory in the code editor. From the index.js, we will read the test.txt.  Read file Synchronously using readFileSync The readFileSync is a synchronous method to read a file. Syntax fs.readFileSync(path[, options]) pathfilepath options encoding string or buffer Default null flag string DefaultrOpen index.js in the Code Editor. Paste the below code.// import the fs module const fs = require("fs"); // read file synchronously const readData = fs.readFileSync("./data.txt"); console.log(readData);Open the terminal or cmd in the node-fs directory and run the below command.$ node index.js <Buffer 4c 6f 72 65 6d 20 69 70 73 75 6d 20 64 6f 6c 6f 72 20 73 69 74 20 61 6d 65 74 2c 20 63 6f 6e 73 65 63 74 65 74 75 72 20 61 64 69 70 69 73 63 69 6e 67 ... 73 more bytes>It has returned the content in the buffer format. The readFileSync by default returns the content in the Buffer format.To get the content in normal string format, we need to pass the utf8 encoding as a parameter.Change the code.// import the fs module const fs = require("fs"); // read file synchronously const readData = fs.readFileSync("./data.txt", "utf8"); console.log(readData);run it.$ node index.jsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Read file Asynchronously using readFileAsynchronous methods are great when you don’t want to wait for a process to complete.  fs module provides an asynchronous method to read a file. It returns a callback.Syntaxfs.readFileSync(path[, options], callback)pathfilepathoptionsencoding string or buffer Default nullflag string Defaultrcallback functionerr Errordata string | BufferSyntax is similar tothe synchronous method except it returns a callback function.// import the fs module const fs = require("fs"); // read file asynchronously fs.readFile("./data.txt", "utf8", (err, data) => { if (!err) { console.log(data); } else { console.error("Something went wrong while reading the file."); } });Run the code.$ node index.jsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Read file using Stream Reading file using fs.readFileis an ideal way if the file size is less. If the size is huge, the read will consume the complete Memory and you may find your system hanging. In such scenarios, Stream is the best option. This breaks the file into small chunks of data and sends a piece at a time. In this way, the system can continue with other tasks without allocating the complete memory to it.   Let us code and understand. Node.js provides a createReadStreammethod to stream the file. createReadStreamreturns a fs.ReadStreamobject. The readStream events like data, end, or error. To read the file, we have to listen to these events.  The data event will return the content of the file in chunks.  The end event will notify that, there is no data left in the file to read. The error event returns an error. // import the fs module constfs = require("fs");  // read file using stream const streamRead = async () => {  return new Promise((resolve, reject) => {  try {  // create a read stream const streamRead = fs.createReadStream("./data.txt", {  encoding:"utf8",  });  // listen to data event streamRead.on("data", (data) => {  console.log(data);  });  // listen to end event streamRead.on("end", () => {  resolve("File read complete");  });  // listen to error event streamRead.on("error", (error) => {  throw Error(  "Something went wrong while streaming the file.",  error.message );  });  } catch (error) {  reject(error);  }  });  };  streamRead().then((res, err) => {  if(!err) console.log(res);  else console.error(err);  }); Run the code.$ node index.jsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. File read completeExperiment time! For the purpose of the experiment, I created a file of 20MB to check the performance of readFileSync, readFileand createReadStream.  The average memory consumption of VS Code is 600-700MB in my machine. This is a screenshot before running any of the commands. readFileSync Check the screenshot. This operation took 373MB and it went on to upto ~450MB. readFile (Asynchronous)Check the screenshot. This operation took 506MB and it went on to upto ~600MB.createReadStream Check the screenshot. This operation took 287MB and it reached upto ~300MB.  Conclusion Node.js provides multiple methods to read a file. It completely depends on the file size and the requirement, which method to choose. From our performance tests, streaming turns out to be the most efficient method to use.  The performance may vary from machine to machine and file size. One must note that what we have demonstrated is just for the sake of a demo, so this must not be taken to be the final words. There are other npm packages available for file system, for example,fs-extra.  Hope this tutorial has helped. Taking a formal training course in Node.js would be a great way to fast-track your career.  Happy coding!Tired of fixing bugs? Find easy solutions to your programming queries with our live, interactive workshops. Explore now.

How to read files with Node.js?

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How to read files with Node.js?

In this tutorial, we will explore different ways we can read files in Node.js.  

In software development, reading files is a regular part of the job. However, this simple process very often becomes complicated and tedious. Fortunately, that's not the case in Node.js. Node.js provides a built-infile system(fs) module, to carry out file operations. 

Before we start, let us make sure we have the basic prerequisites. You will need to ensure you have: 

  • Node.js  
  • Code Editor (Ex. VS Code, Atom) 

For this tutorial, my environment setup includes: 

  • Node.js - v12.16.1 
  • Code Editor - VS Code 

fs module 

fs module is a built-in module in the Node.js. You do notnecessarily need to install it.  

The fs module supports both Synchronous and Asynchronous function calls. 

Getting started 

I am assuming you have installed the NodeJS. 

  • Create a new directory node-fs.  
  • Create 2 files inside the node-fs, data.txt and index.js
  • Paste the dummy data in the data.txt.

Lorem ipsum dolor sit amet, consecteturadipiscingelit, sed do eiusmodtemporincididuntutlabore et dolore magna aliqua. 

Open this directory in the code editor. 

From the index.js, we will read the test.txt.  

Read file Synchronously using readFileSync 

The readFileSync is a synchronous method to read a file. 

Syntax 

fs.readFileSync(path[, options]) 

  • pathfilepath 
  • options 
    • encoding string or buffer Default null 
    • flag string Defaultr

Open index.js in the Code Editor. 

Paste the below code.

// import the fs module
const fs = require("fs");
// read file synchronously
constreadData = fs.readFileSync("./data.txt");
console.log(readData);

Open the terminal or cmd in the node-fs directory and run the below command.

$ node index.js
<Buffer4c 6f 72 65 6d 20 69 70 73 75 6d 20 64 6f 6c 6f 72 20 73 69 74 20 61 6d 65 74 2c 20 63 6f 6e 73 65 63 74 65 74 75 72 20 61 64 69 70 69 73 63 69 6e 67 ... 73 more bytes>

It has returned the content in the buffer format. The readFileSync by default returns the content in the Buffer format.

To get the content in normal string format, we need to pass the utf8 encoding as a parameter.

Change the code.

// import the fs module
const fs = require("fs");
// read file synchronously
constreadData = fs.readFileSync("./data.txt", "utf8");
console.log(readData);

run it.

$ node index.js
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

Read file Asynchronously using readFile

Asynchronous methods are great when you don’t want to wait for a process to complete.  
fs module provides an asynchronous method to read a file. It returns a callback.
Syntax

fs.readFileSync(path[, options], callback)
  • pathfilepath
  • options
    • encoding string or buffer Default null
    • flag string Defaultr
  • callback function
    • err Error
    • data string | Buffer

Syntax is similar tothe synchronous method except it returns a callback function.

// import the fs module
const fs = require("fs");
// read file asynchronously
fs.readFile("./data.txt", "utf8", (err, data) => {
    if (!err) {
        console.log(data);
    } else {
        console.error("Something went wrong while reading the file.");
    }
});

Run the code.

$ node index.js
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

Read file using Stream 

Reading file using fs.readFileis an ideal way if the file size is less. If the size is huge, the read will consume the complete Memory and you may find your system hanging. 

In such scenarios, Stream is the best option. This breaks the file into small chunks of data and sends a piece at a time. In this way, the system can continue with other tasks without allocating the complete memory to it.   

Let us code and understand. 

Node.js provides a createReadStreammethod to stream the file. createReadStreamreturns a fs.ReadStreamobject. The readStream events like data, end, or error. To read the file, we have to listen to these events.  

  • The data event will return the content of the file in chunks.  
  • The end event will notify that, there is no data left in the file to read. 
  • The error event returns an error. 
// import the fs module 
constfs = require("fs"); 
// read file using stream 
const streamRead = async () => { 
return new Promise((resolve, reject) => { 
try { 
// create a read stream 
const streamRead = fs.createReadStream("./data.txt", { 
encoding:"utf8", 
      }); 
// listen to data event 
streamRead.on("data", (data) => { 
console.log(data); 
      }); 
// listen to end event 
streamRead.on("end", () => resolve("File read complete"); 
      }); 
// listen to error event 
streamRead.on("error", (error) => { 
throw Error"Something went wrong while streaming the file."error.message 
        ); 
      }); 
    } catch (error) { 
      reject(error); 
    } 
  }); 
}; 
streamRead().then((res, err) => { 
if(!err) console.log(res); 
else console.error(err); 
}); 

Run the code.

$ node index.js
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
File read complete

Experiment time! 

For the purpose of the experiment, I created a file of 20MB to check the performance of readFileSync, readFileand createReadStream 

The average memory consumption of VS Code is 600-700MB in my machine. 

This is a screenshot before running any of the commands. 

How to read files with Node.js

readFileSync 

Check the screenshot. This operation took 373MB and it went on to upto ~450MB

readFileSync

readFile (Asynchronous)

Check the screenshot. This operation took 506MB and it went on to upto ~600MB.

readFile (Asynchronous)

createReadStream 

Check the screenshot. This operation took 287MB and it reached upto ~300MB.  

createReadStream

Conclusion 

Node.js provides multiple methods to read a file. It completely depends on the file size and the requirement, which method to choose. From our performance tests, streaming turns out to be the most efficient method to use.  

The performance may vary from machine to machine and file size. One must note that what we have demonstrated is just for the sake of a demo, so this must not be taken to be the final words. There are other npm packages available for file system, for example,fs-extra.  

Hope this tutorial has helped. Taking a formal training course in Node.js would be a great way to fast-track your career.  

Happy coding!

Tired of fixing bugs? Find easy solutions to your programming queries with our live, interactive workshops. Explore now.

Shubham

Shubham Chadokar

Author

I am a Software Engineer who loves to write articles and tutorials on the latest technologies. I write majorly on Nodejs and Golang. 

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Highlighted in bold                      Import React from ‘...react’                         function clickAppHandler() {                                function clickHandler() {                                        console.log(‘clicked’)                                         }                                  return (                                                                                  Click                                                                          )                         }                       export default clickAppHandler  Passing Arguments to Event HandlerThere are two ways arguments are passed to event handler  Arrow function                    this.handleClick(id,e)}>Click                onClick is the event                e is the event object                 id can be state or props or some data Bind method      Click  In this case event object is automatically passed In both methods e represents the react event and its passed after the ID as second argument,With an arrow function this event e is passed explicitly but with bind method its automatically passed.                                     Import React,{ Component } from “react”;                                         class TestApp extends Component {                                           state = {                                                       id: 2,                                                      Name: “TestApp Dummy”                                                };                                                             //arrow function                                                 handleClick = (id,e) => {                                                       console.log(id);                                                       console.log(e);                                                  };                                               handleArg = (e) => { this.handleClick(this.state.id,e);}                                                          render() {     return (                    TestApp,{this.state.name}            onClick={this.handleArg}>Display            );   }  }  The react event is an object and obtained from react. Instead of creating a separate function for passing argument, you can directly pass the anonymous arrow function as shown in the render function below:     render() {        return (                                                                                                       TestApp,{this.state.name}                                                {                           this.handleClick(this.state.id,e);                                                               }}>Display                                                                                                         );                                                 }                                            }    Output:   click on button  “TestApp Dummy “                   Let’s see only how bind method looks like in the render function    render() {                                         return (                                                                                                 TestApp,{this.state.name}                                                   Display                                                                                                       );                                                  }                                              } Output: this will display the h1 tag and when you click the button handleClick function gets invoked and the console will display id of the state object as shown above. Building a Practice to Thoroughly Understand Events This blog focuses on event handling, which in turn teaches about event handlers declared in JSX markup.This approach helps in tracking down the element mapped with events in an easy way.  We also learned how to handle multiple event handlers in a single element by using JSX attributes.we also learned about ways to bind event handler and  parameter values. Then we learned about synthetic events which are abstractions around native events. The best way you can retain this learning is by practicing more and tackling the complexities that may arise as you practice. You can find several tutorials on the internet or share your questions with us here. Happy learning! 
5355
Handling React Events - A Detailed Guide

Event handling essentially allows the user to inte... Read More

MongoDB Query Document Using Find() With Example

MongoDB's find() method selects documents from a collection or view and returns a cursor to those documents. There are two parameters in this formula: query and projection.Query – This is an optional parameter that specifies the criteria for selection. In simple terms, a query is what you want to search for within a collection.Projection – This is an optional parameter that specifies what should be returned if the query criteria are satisfied. In simple terms, it is a type of decision-making that is based on a set of criteria.MongoDB's Flexible SchemaA NoSQL database, which stands for "not only SQL," is a way of storing and retrieving data that is different from relational databases' traditional table structures (RDBMS).When storing large amounts of unstructured data with changing schemas, NoSQL databases are indeed a better option than RDBMS. Horizontal scaling properties of NoSQL databases allow them to store and process large amounts of data.These are intended for storing, retrieving, and managing document-oriented data, which is frequently stored in JSON format (JavaScript Object Notation). Document databases, unlike RDBMSs, have a flexible schema that is defined by the contents of the documents.MongoDB is one of the most widely used open-source NoSQL document databases. MongoDB is known as a 'schemaless' database because it does not impose a specific structure on documents in a collection.MongoDB is compatible with a number of popular programming languages. It also offers a high level of operational flexibility because it scales well horizontally, allowing data to be spread or 'sharded' across multiple commodity servers with the ability to add more servers as needed. MongoDB can be run on a variety of platforms, including developer laptops, private clouds, and public clouds.Querying documents using find()MongoDB queries are used to retrieve or fetch data from a MongoDB database. When running a query, you can use criteria or conditions to retrieve specific data from the database.The function db.collection is provided by MongoDB. find() is a function that retrieves documents from a MongoDB database.In MongoDB, the find method is used to retrieve a specific document from the MongoDB collection. In Mongo DB, there are a total of six methods for retrieving specific records.find()findAndModify()findOne()findOneAndDelete()findOneAndReplace()findOneAndUpdate()Syntax:find(query, projection)We can fetch a specific record using the Find method, which has two parameters. If these two parameters are omitted, the find method will return all of the documents in the MongoDB collection.Example:Consider an example of employees with the database of employee_id and employee_name and we will fetch the documents using find() method.First, create a database with the name “employees” with the following code:use employeesNow, create a collection “employee” with:db.createCollection("employee")In the next step we will insert the documents in the database:db.employee.insert([{employee_id: 101, employee_name: "Ishan"}, {employee_id: 102, employee_name: "Bhavesh"}, {employee_id: 103, employee_name: "Madan"}])Find all Documents:To get all the records in a collection, we need to use the find method with an empty parameter. In other words, when we need all the records, we will not use any parameters.db.employee.find()Output in Mongo ShellThe pretty() method can be used to display the results in a formatted manner.Syntax:db.COLLECTION_NAME.find().pretty()Let’s check our documents with pretty() method:Query FiltersWe will see examples of query operations using the db.collection.find() method in mongosh.We will use the employee collection in the employees database.db.employee.insert([{employee_id: 101, employee_name: "Ishan", age: 21, email_id: "ishanjain@gmail.com"}, {employee_id: 102, employee_name: "Bhavesh", age: 22, email_id: "bhaveshg@gmail.com"}, {employee_id: 103, employee_name: "Madan", age: 23, email_id: "madan@gmail.com"}])As we have seen earlier that to select all the documents in the database we pass an empty document as the query filter parameter to the find method.db.employee.find().pretty()Find the first document in a collection:db.employee.findOne()Find a document by ID:db.employee.findOne({_id : ObjectId("61d1ae0b56b92c20b423a5a7")})Find Documents that Match Query Criteriadb.employee.find({“age”: “22”})db.employee.find({"employee_name": "Madan"}).pretty()Sort Results by a Field:db.employee.find().sort({age: 1}).pretty()order by age, in ascending orderdb.employee.find().sort({age: -1}).pretty()order by age, in descending orderAND Conditions:A compound query can specify conditions for multiple fields in the documents in a collection. A logical AND conjunction connects the clauses of a compound query indirectly, allowing the query to select all documents in the collection that meet the specified conditions.In the following example, we will consider all the documents in the employee collection where employee_id equals 101 and age equals 21.db.employee.find({"employee_id": 101, "age": "21" }).pretty()Querying nested fieldsThe embedded or nested document feature in MongoDB is a useful feature. Embedded documents, also known as nested documents, are documents that contain other documents.You can simply embed a document inside another document in MongoDB. Documents are defined in the mongo shell using curly braces (), and field-value pairs are contained within these curly braces.Using curly braces, we can now embed or set another document inside these fields, which can include field-value pairs or another sub-document.Syntax:{ field: { field1: value1, field2: value2 } }Example:We have a database “nested” and in this database we have collection “nesteddoc”.The following documents will insert into the nesteddoc collection.db.nesteddoc.insertMany([ { "_id" : 1, "dept" : "A", "item" : { "sku" : "101", "color" : "red" }, "sizes" : [ "S", "M" ] }, { "_id" : 2, "dept" : "A", "item" : { "sku" : "102", "color" : "blue" }, "sizes" : [ "M", "L" ] }, { "_id" : 3, "dept" : "B", "item" : { "sku" : "103", "color" : "blue" }, "sizes" : "S" }, { "_id" : 4, "dept" : "A", "item" : { "sku" : "104", "color" : "black" }, "sizes" : [ "S" ] } ])Place the documents in the collection now. Also, take a look at the results:As a result, the nesteddoc collection contains four documents, each of which contains nested documents. The find() method can be used to access the collection's documents.db.nesteddoc.find()Specify Equality Condition:In this example, we will select the document from the nesteddoc query where dept equals “A”.db.nesteddoc.find({dept: "A"})Querying ArraysUse the query document {: } to specify an equality condition on an array, where is the exact array to match, including the order of the elements.The following query looks for all documents where the field tags value is an array with exactly two elements, "S" and "M," in the order specified:db.nesteddoc.find( { sizes: ["S", "M"] } )Use the $all operator to find an array that contains both the elements "S" and "M," regardless of order or other elements in the array:db.nested.find( { sizes: { $all: ["S", "M"] } } )Query an Array for an Element:The following example queries for all documents where size is an array that contains the string “S” as one of its elements:db.nesteddoc.find( { sizes: "S" } )Filter conditionsTo discuss the filter conditions, we will consider a situation that elaborates this. We will start by creating a collection with the name “products” and then add the documents to it.db.products.insertMany([ { _id: 1, item: { name: "ab", code: "123" }, qty: 15, tags: [ "A", "B", "C" ] }, { _id: 2, item: { name: "cd", code: "123" }, qty: 20, tags: [ "B" ] }, { _id: 3, item: { name: "ij", code: "456" }, qty: 25, tags: [ "A", "B" ] }, { _id: 4, item: { name: "xy", code: "456" }, qty: 30, tags: [ "B", "A" ] }, { _id: 5, item: { name: "mn", code: "000" }, qty: 20, tags: [ [ "A", "B" ], "C" ] }])To check the documents, use db.products.find():$gt$gt selects documents with a field value greater than (or equal to) the specified value.db.products.find( { qty: { $gt: “20” } } )$gte:$gte finds documents in which a field's value is greater than or equal to (i.e. >=) a specified value (e.g. value.)db.products.find( { qty: { $gte: 20 } } )$lt:$lt selects documents whose field value is less than (or equal to) the specified value.db.products.find( { qty: { $lt: 25 } } )$lte:$lte selects documents in which the field's value is less than or equal to (i.e. =) the specified value.db.products.find( { qty: { $lte: 20 } } )Query an Array by Array Length:To find arrays with a specific number of elements, use the $size operator. For example, the following selects documents with two elements in the array.db.products.find( { "tags": {$size: 2} } )ProjectionIn MongoDB, projection refers to selecting only the data that is required rather than the entire document's data. If a document has five fields and you only want to show three of them, select only three of them.The find() method in MongoDB accepts a second optional parameter, which is a list of fields to retrieve, as explained in MongoDB Query Document. When you use the find() method in MongoDB, it displays all of a document's fields. To prevent this, create a list of fields with the values 1 or 0. The value 1 indicates that the field should be visible, while 0 indicates that it should be hidden.Syntax:db.COLLECTION_NAME.find({},{KEY:1})Example:We will consider the previous example of products collection. Run the below command on mongoshell to learn how projection works:db.products.find({},{"tags":1, _id:0})Keep in mind that the _id field is always displayed while executing the find() method; if you do not want this field to be displayed, set it to 0.Optimized FindingsTo retrieve a document from a MongoDB collection, use the Find method.Using the Find method, we can retrieve specific documents as well as the fields that we require. Other find methods can also be used to retrieve specific documents based on our needs.By inserting array elements into the query, we can retrieve specific elements or documents. To retrieve data for array elements from the collection in MongoDB, we can use multiple query operators.
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MongoDB Query Document Using Find() With Example

MongoDB's find() method selects documents from a c... Read More

Implementing MongoDb Map Reduce using Aggregation

Algorithms and applications in today's data-driven market collect data about people, processes, systems, and organisations 24 hours a day, seven days a week, resulting in massive amounts of data. The problem is figuring out how to process this massive amount of data efficiently without sacrificing valuable insights.What is Map Reduce? The MapReduce programming model comes to the rescue here. MapReduce, which was first used by Google to analyse its search results, has grown in popularity due to its ability to split and process terabytes of data in parallel, generating results faster. A (Key,value) pair is the basic unit of information in MapReduce. Before feeding the data to the MapReduce model, all types of structured and unstructured data must be translated to this basic unit. The MapReduce model, as the name implies, consists of two distinct routines: the Map-function and the Reduce-function.  MapReduce is a framework for handling parallelizable problems across huge files using a huge number of devices (nodes), which are collectively referred to as a cluster (if all nodes are on the same local network and use similar hardware) or a grid (if the nodes are shared across geographically and administratively distributed systems, and use more heterogeneous hardware).  When data stored in a filesystem (unstructured) or a database(structured) is processed, MapReduce can take advantage of data's locality, processing it close to where it's stored to reduce communication costs. Typically, a MapReduce framework (or system) consists of three operations: Map: Each worker node applies the map function to local data and saves the result to a temporary storage. Only one copy of the redundant input data is processed by a master node. Shuffle: worker nodes redistribute data based on output keys (produced by the map function), ensuring that all data associated with a single key is stored on the same worker node. Reduce: each group of output data is now processed in parallel by worker nodes, per key. This article will walk you through the Map-Reduce model's functionality step by step. Map Reduce in MongoDB The map-reduce operation has been deprecated since MongoDB 5.0. An aggregation pipeline outperforms a map-reduce operation in terms of performance and usability. Aggregation pipeline operators like $group, $merge, and others can be used to rewrite map-reduce operations. Starting with version 4.4, MongoDB provides the $accumulator and $function aggregation operators for map-reduce operations that require custom functionality. In JavaScript, use these operators to create custom aggregation expressions. The map and reduce functions are the two main functions here. As a result, the data is independently mapped and reduced in different spaces before being combined in the function and saved to the specified new collection. This mapReduce() function was designed to work with large data sets only. You can perform aggregation operations like max and avg on data using Map Reduce, which is similar to groupBy in SQL. It works independently and in parallel on data. Implementing Map Reduce with Mongosh (MongoDB Shell)  The db.collection.mapReduce() method in mongosh is a wrapper for the mapReduce command. The examples that follow make use of the db.collection.mapReduce(). Example: Create a collection ‘orders’ with these documents: db.orders.insertMany([     { _id: 1, cust_id: "Ishan Jain", ord_date: new Date("2021-11-01"), price: 25, items: [ { sku: "oranges", qty: 5, price: 2.5 }, { sku: "apples", qty: 5, price: 2.5 } ], status: "A" },     { _id: 2, cust_id: "Ishan Jain", ord_date: new Date("2021-11-08"), price: 70, items: [ { sku: "oranges", qty: 8, price: 2.5 }, { sku: "chocolates", qty: 5, price: 10 } ], status: "A" },     { _id: 3, cust_id: "Bhavesh Galav", ord_date: new Date("2021-11-08"), price: 50, items: [ { sku: "oranges", qty: 10, price: 2.5 }, { sku: "pears", qty: 10, price: 2.5 } ], status: "A" },     { _id: 4, cust_id: "Bhavesh Galav", ord_date: new Date("2021-11-18"), price: 25, items: [ { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" },     { _id: 5, cust_id: "Bhavesh Galav", ord_date: new Date("2021-11-19"), price: 50, items: [ { sku: "chocolates", qty: 5, price: 10 } ], status: "A"},     { _id: 6, cust_id: "Madan Parmar", ord_date: new Date("2021-11-19"), price: 35, items: [ { sku: "carrots", qty: 10, price: 1.0 }, { sku: "apples", qty: 10, price: 2.5 } ], status: "A" },     { _id: 7, cust_id: "Madan Parmar", ord_date: new Date("2021-11-20"), price: 25, items: [ { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" },     { _id: 8, cust_id: "Abhresh", ord_date: new Date("2021-11-20"), price: 75, items: [ { sku: "chocolates", qty: 5, price: 10 }, { sku: "apples", qty: 10, price: 2.5 } ], status: "A" },     { _id: 9, cust_id: "Abhresh", ord_date: new Date("2021-11-20"), price: 55, items: [ { sku: "carrots", qty: 5, price: 1.0 }, { sku: "apples", qty: 10, price: 2.5 }, { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" },     { _id: 10, cust_id: "Abhresh", ord_date: new Date("2021-11-23"), price: 25, items: [ { sku: "oranges", qty: 10, price: 2.5 } ], status: "A" }  ]) Apply a map-reduce operation to the orders collection to group them by cust_id, then add the prices for each cust_id: To process each input document, define the map function: this refers the document that the map-reduce operation is processing in the function. For each document, the function maps the price to the cust_id and outputs the cust_id and price. var mapFunction1 = function() {emit(this.cust_id, this.price);}; With the two arguments keyCustId and valuesPrices, define the corresponding reduce function: The elements of the valuesPrices array are the price values emitted by the map function, grouped by keyCustId. The valuesPrice array is reduced to the sum of its elements by this function. var reduceFunction1 = function(keyCustId, valuesPrices) {return Array.sum(valuesPrices);};Apply the mapFunction1 map function and the reduceFunction1 reduce function to all documents in the orders collection: db.orders.mapReduce(mapFunction1,reduceFunction1,{ out: "map_reduce_example" }) The results of this operation are saved in the map_reduce_example collection. If the map_reduce_example collection already exists, the operation will overwrite its contents with the map-reduce operation's results. Check the map_reduce_example collection to verify: db.map_reduce_example.find().sort( { _id: 1 } ) Aggregation Alternative:You can rewrite the map-reduce operation without defining custom functions by using the available aggregation pipeline operators: db.orders.aggregate([{$group: { _id:"$cust_id",value:{$sum: "$price" } } },{ $out: "agg_alternative_1" }]) Check the agg_alternative_1 collection to verify: db.agg_alternative_1.find().sort( { _id: 1 } )Implementing Map Reduce with Java Consider the collection car and insert the following documents in it. db.car.insert( [ {car_id:"c1",name:"Audi",color:"Black",cno:"H110",mfdcountry:"Germany",speed:72,price:11.25}, {car_id:"c2",name:"Polo",color:"White",cno:"H111",mfdcountry:"Japan",speed:65,price:8.5}, {car_id:"c3",name:"Alto",color:"Silver",cno:"H112",mfdcountry:"India",speed:53,price:4.5}, {car_id:"c4",name:"Santro",color:"Grey",cno:"H113",mfdcountry:"Sweden",speed:89,price:3.5} , {car_id:"c5",name:"Zen",color:"Blue",cno:"H114",mfdcountry:"Denmark",speed:94,price:6.5} ] ) You will get an output like this:  Let's now write the map reduce function on a collection of cars, grouping them by speed and classifying them as overspeed cars.  var speedmap = function (){  var criteria;  if ( this.speed > 70 ) {criteria = 'overspeed';emit(criteria,this.speed);}}; Based on the speed, this function classifies the vehicle as an overspeed vehicle. The term "this" refers to the current document that requires map reduction. var avgspeed_reducemap = function(key, speed) {       var total =0;       for (var i = 0; i 
7344
Implementing MongoDb Map Reduce using Aggregation

Algorithms and applications in today's data-driven... Read More