How to add React to an existing website?
10K- by KnowledgeHut
- 15th Dec, 2020
- Last updated on 17th Mar, 2021
- 6 mins read
React, originally created by Facebook, is a popular user interface JavaScript framework which is used for creating front-end applications. It is one of the most popular frameworks used by developers to create fast scalable applications using a declarative approach that ties up JavaScript with an HTML-like syntax known as JSX.
Building up or setting up React applications requires many dependencies, such as using a compiler or setting up the node and versioning etc.In order to get rid of the above complications and set up SPAs (Single Page applications) in simple steps, a CLI tool named create-react-app was made.
In this article we will learn how to set up your React app in no time, with an easier approach and without getting into any complications. And what’s more, here, we will not need to configure or install any tools like webpack or Babel.
Create-React-APP will take care of such things with its preconfigured schema, so that you can only focus on the code and business logic.
So let’s get started!
Prerequisites
Command Line First
In order to use the React CLI (create-react-app), it is good to grasp the command line first. It is used in many ways such as starting the application with create-react-app or using development scripts or building projects.
Installing Node
We will be using the Node package manager (npm) to start or create an application, so we will need to have Node installed on our computer.
To check if you have Node installed in your computer, run the below command in your terminal:
node -vIf you have Node installed already in your system, this command will return a version number, like v12.18.2, and if it’s not installed already, kindly follow the mentioned steps in Setting Up Node Locally before moving on the next step.
What comes up with create-react-app?
So many developers use create-react-app as an easy starting kit for creating their React app. But surprisingly, many of them do not know about what comes in the starter kit, or how we can utilize it, or why it is even there!
Let’s take a deep dive and see what’s under the hood!
It provides us with a recommended starting folder structure.
my-app/ //Dummy App Name
README.md
node_modules/
package.json
public/
index.html
favicon.ico
src/
App.css
App.js
App.test.js
index.css
index.js
Logo.svg- Scripts to run our React application
These scripts are normally used to start, run or test the React application.
(We will learn about these scripts later in our article)
Extensibility
Ability to extend with Sass, TypeScript, Charts, and more other libraries.
- A solid build setup with webpack and Babel
- Scripts to bundle for production
- ...and much more!
Steps To Run the Application:
Now that we have some context about the Create React App and its usage, let’s not waste more time and get started by installing it.
There are only 3 steps to create and run the application:
- npm init react-app my-new-app
- npm start
- npm run build
Let’s discuss each one of them in detail.
Step-1: Installing and Creating a create-react-app
Though it is quite possible to manually create a new React app also, the Facebook community had already created a Node package for create-react-app to generate a boilerplate version of a React application to make our life easier.
There are many ways to install create-react-app. We can use any of them as per our ease.
# using npm (npm 6+) # using npx (npm 5.2+) or, # using yarn (yarn 0.25+)
Installing create-react-app
What are you waiting for…? Let’s open up the terminal and run the below command:
// Using npm npm init react-app my-new-app // my-new-app is the app name
We can choose any name in the place of “my-new-app”, with only lowercase letters.
Note: If you’ve already installed create-react-app globally via npm install -g create-react-app, it is recommended that you uninstall the package first. Run the below command to uninstall the same.
npm uninstall -g create-react-app
Once the above step is completed, you will get some quick generic tips on how to use this application:
# npm start # npm run build # npm test # npm run eject
Let's now explore what all we can do with our new application.
Step 2: Running the Application Using React Scripts
In our React project we can run some scripts which are useful for the following tasks:
- Running our application for development
- Building our application for production
- Testing our application if we've created any tests in our project
Scripts are located inside the package.json, which you can see inside the project folder.
//package.json file showing scripts block
"scripts": {
"start": "react-scripts start",
"build": "react-scripts build",
"test": "react-scripts test",
"eject": "react-scripts eject"
},All the above scripts can be run by using the following commands.
// To start our app for development
npm start
npm run start
yarn start
// To build our app for production
npm run production
yarn production
// To test our application
npm run test
// To Eject our application
npm run ejectStarting the React App
We can use the “start” command to start our application for development. Use the below command to run the application.
npm run start This command will start a local server and will open our application for the development in the browser, as shown below:
Say Hello World
Let’s modify this text a bit, and display hello world. Open the file “App.js” from src folder in your editor.
Delete the div named App-header! And instead add a new element like Hello world in App.js and save the file.
It should look like this.
And that’s it. You have just now created your first React application.
No Build Setup
In order to transpile our ES6 code to something that all browsers can read, a webpack or compiler is required.
Fortunately, Create React App is all configured with webpack and Babel so you don't have to manually set up anything.
Bundling for Production
Now let’s host our application for users. In order to do so, we need to bundle up all of our code into a few JavaScript files that the browser can load and users can see the running application. There is again a convenient script given by Create-react-App.
Let's run the following command and see how build files get created.
// (Bundling this app for production for external servers) npm run build
Once build is finished, there we have a new directory called build/ inside our project.
Inside the build folder, there is a file named index,html, and we can use the same file to host on the server.
To be Noted: In order to build the project, these files must exist with the same filenames:
- public/index.html is the page template;
- src/index.js is the JavaScript entry point.
Good To Know about your Build folder
- Inside the build directory, you will find all the compiled and minified code which is put into the smallest usable state.
- It doesn’t matter if it is readable or not, since this is not a public-facing piece of code.
- Inside the build/static directory are all the JavaScript and CSS files.
- In each file inside of build/static there is a unique hash of the file contents.
Conclusion
Create React App is an awesome toolkit for React developers. I hope you now understand the power of Create React App better.
As you have seen, there are lots of goodies that come with this toolkit. We can easily configure our own webpack and Babel setups as per our requirement. And if we want more support in terms of UI or to fulfill some business requirements we can easily add support for things like Sass or TypeScript.
What are you waiting for? Fire that terminal up, install create-react-app, and start building your awesome ideas with this solid base!
Happy Learning!
KnowledgeHut
KnowledgeHut is an outcome-focused global ed-tech company. We help organizations and professionals unlock excellence through skills development. We offer training solutions under the people and process, data science, full-stack development, cybersecurity, future technologies and digital transformation verticals.
Website : https://www.knowledgehut.com
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MongoDB Query Document Using Find() With Example
By Abhresh Sugandhi
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. 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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.
6498
- by Abhresh Sugandhi
- 20 Jan 2022
- 8 mins read
MongoDB's find() method selects documents from a c... Read More
Implementing MongoDb Map Reduce using Aggregation
By Abhresh Sugandhi
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
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- by Abhresh Sugandhi
- 19 Jan 2022
- 9 mins read
Algorithms and applications in today's data-driven... Read More
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