Charles Cooper

Charles Cooper

1564732591

Fetching Data in React using React Async

React Async is a promised-based library that makes it possible for you to fetch data in your React application. Let’s look at various examples using components, hooks and helpers to see how we can implement loading states when making requests.

You’re probably used to fetching data in React using axios or fetch. The usual method of handling data fetching is to:

  • Make the API call.
  • Update state using the response if all goes as planned.
  • Or, in cases where errors are encountered, an error message is displayed to the user.

There will always be delays when handling requests over the network. That’s just part of the deal when it comes to making a request and waiting for a response. That’s why we often make use of a loading spinner to show the user that the expected response is loading.

All these can be done using a library called React Async.

React Async is a promised-based library that makes it possible for you to fetch data in your React application. Let’s look at various examples using components, hooks and helpers to see how we can implement loading states when making requests.

For this tutorial, we will be making use of Create React App. You can create a project by running:

npx create-react-app react-async-demo

When that is done, run the command to install React Async in your project, using yarn or npm:

## yarn
yarn add react-async

## npm
npm install react-async --save

Example 1: Loaders in components

The library allows us to make use of <Async> directly in our JSX. As such, the component example will look like this;

// Let's import React, our styles and React Async
import React from 'react';
import './App.css';
import Async from 'react-async';

// We'll request user data from this API
const loadUsers = () =>
 fetch("https://jsonplaceholder.typicode.com/users")
   .then(res => (res.ok ? res : Promise.reject(res)))
   .then(res => res.json())

// Our component
function App() {
 return (
   <div className="container">
     <Async promiseFn={loadUsers}>
       {({ data, err, isLoading }) => {
         if (isLoading) return "Loading..."
         if (err) return `Something went wrong: ${err.message}`

         if (data)
           return (
             <div>
               <div>
                 <h2>React Async - Random Users</h2>
               </div>
               {data.map(user=> (
                 <div key={user.username} className="row">
                   <div className="col-md-12">
                     <p>{user.name}</p>
                     <p>{user.email}</p>
                   </div>
                 </div>
               ))}
             </div>
           )
       }}
     </Async>
   </div>
 );
}

export default App;

First, we created a function called loadUsers. This will make the API call using the fetch API. And, when it does, it returns a promise which gets resolved. After that, the needed props are made available to the component.

The props are:

  • isLoading: This handles cases where the response has not be received from the server yet.
  • err: For cases when an error is encountered. You can also rename this to error.
  • data: This is the expected data obtained from the server.

As you can see from the example, we return something to be displayed to the user dependent on the prop.

Example 2: Loaders in hooks

If you are a fan of hooks, there is a hook option available when working with React Async. Here’s how that looks:

// Let's import React, our styles and React Async
import React from 'react';
import './App.css';
import { useAsync } from 'react-async';

// Then we'll fetch user data from this API
const loadUsers = async () =>
 await fetch("https://jsonplaceholder.typicode.com/users")
   .then(res => (res.ok ? res : Promise.reject(res)))
   .then(res => res.json())

// Our component
function App() {
 const { data, error, isLoading } = useAsync({ promiseFn: loadUsers })
 if (isLoading) return "Loading..."
 if (error) return `Something went wrong: ${error.message}`
 if (data)
 
  // The rendered component
 return (
   <div className="container">
     <div>
       <h2>React Async - Random Users</h2>
     </div>
     {data.map(user=> (
       <div key={user.username} className="row">
         <div className="col-md-12">
           <p>{user.name}</p>
           <p>{user.email}</p>
         </div>
       </div>
     ))}
   </div>
 );
}

export default App;
 

This looks similar to the component example, but in this scenario, we’re making use of useAsync and not the Async component. The response returns a promise which gets resolved, and we also have access to similar props like we did in the last example, with which we can then return to the rendered UI.

Example 3: Loaders in helpers

Helper components come in handy in making our code clear and readable. These helpers can be used when working with an useAsync hook or with an Async component, both of which we just looked at. Here is an example of using the helpers with the Async component.

// Let's import React, our styles and React Async
import React from 'react';
import './App.css';
import Async from 'react-async';

// This is the API we'll use to request user data
const loadUsers = () =>
 fetch("https://jsonplaceholder.typicode.com/users")
   .then(res => (res.ok ? res : Promise.reject(res)))
   .then(res => res.json())

// Our App component
function App() {
 return (
   <div className="container">
     <Async promiseFn={loadUsers}>
         <Async.Loading>Loading...</Async.Loading>
         <Async.Fulfilled>
           {data => {
             return (
               <div>
                 <div>
                   <h2>React Async - Random Users</h2>
                 </div>
                 {data.map(user=> (
                   <div key={user.username} className="row">
                     <div className="col-md-12">
                       <p>{user.name}</p>
                       <p>{user.email}</p>
                     </div>
                   </div>
                 ))}
               </div>
             )
           }}
         </Async.Fulfilled>
         <Async.Rejected>
           {error => `Something went wrong: ${error.message}`}
         </Async.Rejected>
     </Async>
   </div>
 );
}

export default App;


This looks similar to when we were making use of props. With that done, you could break the different section of the app into tiny components.

Conclusion

If you have been growing weary of going the route I mentioned in the opening section of this tutorial, you can start making of React Async in that project you are working on. The source code used in this tutorial can be found in their different branches on GitHub.

#reactjs #javascript #web-development

What is GEEK

Buddha Community

Fetching Data in React using React Async
Autumn  Blick

Autumn Blick

1598839687

How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

  • Performance: It delivers optimal performance.
  • Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
  • UI design: React Native enables you to design simple and responsive UI for your mobile app.
  • 3rd party plugins: This framework supports 3rd party plugins.
  • Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

  • Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
  • Near-native user experience: React Native provides several native components, and these are platform agnostic.
  • The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Data Lake and Data Mesh Use Cases

As data mesh advocates come to suggest that the data mesh should replace the monolithic, centralized data lake, I wanted to check in with Dipti Borkar, co-founder and Chief Product Officer at Ahana. Dipti has been a tremendous resource for me over the years as she has held leadership positions at Couchbase, Kinetica, and Alluxio.

Definitions

  • A data lake is a concept consisting of a collection of storage instances of various data assets. These assets are stored in a near-exact, or even exact, copy of the resource format and in addition to the originating data stores.
  • A data mesh is a type of data platform architecture that embraces the ubiquity of data in the enterprise by leveraging a domain-oriented, self-serve design. Mesh is an abstraction layer that sits atop data sources and provides access.

According to Dipti, while data lakes and data mesh both have use cases they work well for, data mesh can’t replace the data lake unless all data sources are created equal — and for many, that’s not the case.

Data Sources

All data sources are not equal. There are different dimensions of data:

  • Amount of data being stored
  • Importance of the data
  • Type of data
  • Type of analysis to be supported
  • Longevity of the data being stored
  • Cost of managing and processing the data

Each data source has its purpose. Some are built for fast access for small amounts of data, some are meant for real transactions, some are meant for data that applications need, and some are meant for getting insights on large amounts of data.

AWS S3

Things changed when AWS commoditized the storage layer with the AWS S3 object-store 15 years ago. Given the ubiquity and affordability of S3 and other cloud storage, companies are moving most of this data to cloud object stores and building data lakes, where it can be analyzed in many different ways.

Because of the low cost, enterprises can store all of their data — enterprise, third-party, IoT, and streaming — into an S3 data lake. However, the data cannot be processed there. You need engines on top like Hive, Presto, and Spark to process it. Hadoop tried to do this with limited success. Presto and Spark have solved the SQL in S3 query problem.

#big data #big data analytics #data lake #data lake and data mesh #data lake #data mesh

Cyrus  Kreiger

Cyrus Kreiger

1618039260

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt