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How to build the search for applications with React and Elastic



When a user searches for something, he wants to receive relevant results. But the relevant results are only a part of what a visitor will win; their research should also feel good. Research should be quick, react to input and feel intelligent and effective.

This tutorial will teach you the research of applications demonstrating how to build a smooth and robust research experience using React and the Elastic app search JavaScript client. In the end, you'll look good, relevant, React-ified application that will allow you to search on various packages of npm in real time with the ordering by facets and the status maintained as part of the URI.

The completed code can be found on GitHub.

The live sample application can be found at http://packagehunt.swiftype.info/.

Requirements

To proceed, you must have the following …

  1. A recent version of Node.js
  2. A recent version of npm.
  3. An Elastic App Search Service account or a 14-day free trial.
  4. About 30 minutes.

Search app, an & # 39; introduction

Applications are built around data. Facebook has exploded in our social circles presenting the data "friends" in an interesting way. eBay started as the easiest way to find and buy used goods. Wikipedia has made it easy for readers to know … well, everything!

There are applications to solve data problems. In this effort, research is an essential companion. If this is an important application, a fundamental part of its offer will be the search: find friends, products, conversations or articles. The bigger and more interesting the data set, the more popular the application will be, especially if the research is relevant and rewarding.

Elastic App Search is based on Elasticsearch, a RESTful open source, distributed search engine. With Elastic App Search, developers receive access to a robust set of API endpoints that are optimized for managing premium application search cases.

Start your engines

To start, create a Engine within the app search.

An engine ingests objects for indexing. An object is your data; it's the friend profile, the product or the wiki page. Once the data is entered in the search for apps, they are indexed on a flexible and optimized for search. From there, we can take advantage of several client libraries to create a pleasant search experience.

For this example, we will call our engine: modules node.

Once the Engine has been created, we will need three things from the Credentials page:

  1. The Host identifier, with area code guest-
  2. A Private API key, with area code private-
  3. A Key to public research, with area code search-

With that, we could clone the project, enter the directory, check the starter branch, then run a installation of npm:

$ git clone https://github.com/swiftype/app-search-demo-react.git
$ cd react-tutorial && git checkout starter && npm install

Great – we have a ready and ready application. But to search requires data …

Ingestion ~

In most cases, your objects would be inside a database or a back-end API. Since this is an example, we will use a static one .json file. The repository contains two scripts: init-data.js is -indication data.js. The first is a scraper which was used to acquire good formatting node-module data from npm. The data exist within the node-modules.json file. This is a last one indexer that will ingest that data into your search engine for indexing.

To run the indexer script, we will need to pass ours Host identifier is Private API key together with it.

$ REACT_APP_HOST_IDENTIFIER = {Your host identifier} 
REACT_APP_API_KEY = {Your private API key} 
npm executes index-data

Objects are quickly sent to ours Search engine app in batches of 100 and the index is built.

We should now have a dashboard for our newly created engine with ~ 9.500 npm packets indexed as documents. It may be useful to deepen the data in order to become familiar with its contents.

app_search_engine_overview.png

Reactivity

With our full and ready engine, we can start building our main application.

start $ npm

Starting npm the React boilerplate will open from within the project directory. From it takes its style App.css – let us customize it better to meet our needs.

In the near future, we will need a search box where we can type in our search queries. Users will look for these useful rectangles because search engines and browsers have trained them well: write here, find what you want!

// App.css

...

.App-search-box {
height: 40px;
width: 500 px;
font size: 1em;
margin-top: 10px;
}

We will also need to enter login credentials for app search in a safe place, such as .env file.

Create one within the project's root directory and compile it like this:

// .env

REACT_APP_HOST_IDENTIFIER = {your host ID, with area code guest-}
REACT_APP_SEARCH_KEY = {your public search key, with area code search-}

With safe variables, we can start writing our search logic.

Start the search

The App.js the file is where the basic logic will live. This file, along with most of the other startup files, was created by create-react-app, a tool to help restart React applications without any configuration. Before writing a logic to test the search, you need to install the Swiftype Search Search JavaScript client library:

$ npm install --save swiftype-app-search-javascript

Enter the following code in App.js. It will perform a basic search.

We will do a hard-code foo as our search term example:

import * as SwiftypeAppSearch from "swiftype-app-search-javascript";

const client = SwiftypeAppSearch.createClient ({
hostIdentifier: process.env.REACT_APP_HOST_IDENTIFIER,
apiKey: process.env.REACT_APP_SEARCH_KEY,
engineName: "node-modules"
});

// We can interrogate for anything - foo it is our example
const query = "foo";
const options = {};
client.search (query, options)
.then (resultList => console.log (resultList))
.catch (error => console.log (error))

The browser will update and create a resulting a array away console.log. To explore the array, we can open the browser console for developers. We can try some other query by replacing the foo interrogate with another string. Once the query has changed and the page is updated, we can see how the result set has been adapted.

Fantastic ~ with that, we are already looking through ours modules node.

Resulting goodness

We have a simple model down to look for, but the results are of little use hidden in a console.log. We are going to remove the basic React style and our previous code, then extend things.

We will create …

  1. A state variable that will hold a answer property.
  2. A performQuery method that will query Search app using client.search. It will store the results of the query inside answer property.
  3. A componentDidMount life cycle hook, which will be executed once the application is loaded. We will interrogate foo again, but we can use any query we would like.
  4. Structured HTML to contain the resulting data output and the total number of results.
// App.js

// ... Truncated!

the App class extends the component {
state = {
// A new state property, which contains the answer to the most recent query
Answer: null
};

componentDidMount () {
/ * Calling this in componentDidMount ensures that the results are displayed on
the screen when the app loads for the first time * /
this.performQuery ("foo");
}

// Method for executing a query and storing the response
performQuery = queryString => {
client.search (queryString, {}). then (
answer => {
// Add this for now so you can check the complete answer
console.log (answer);
this.setState ({response});
},
error => {
console.log (`error: $ {error}`);
}
);
};

render () {
const {response} = this.state;
if (! response) returns null;

return (
      

Node module search

{/ * Show the total number of results for this query * /}

{response.info.meta.page.total_results} Results

{/ * Iterate on the results and show their name and description * /} {response.results.map (result => (

Name: {result.getRaw ("name")}

Description: {result.getRaw ("description")}

))}
); } } // ... Truncated!

When you press save, the results will appear inside http: // localhost: 3000 – 27 results and some elegant sound modules. If something went wrong, we can take a look at the console because we have two console.logs nested in the code.

Fancy Boxing

We have been hard-coding foo in our questions What makes the search more valuable is that it starts with a free expression. After developing a great research experience, you will be able to optimize for the most common expressions, taking care of the most relevant result sets. It all starts with a blank canvas: the search box.

To create an able search box, we will add a property to state named question string. To hold question string updated with new strings, we will create a updateQuery method; we will leverage on one onChange manager to be updated question string and start a new search every time a user changes the text in the box.

Our full App the class now looks like this:

// src / App.js

// ... Truncated!

the App class extends the component {
state = {
// A new status property, which tracks the value from the search box
string of the question: "",
Answer: null
};

componentDidMount () {
// Remove the hard-coded search for "node"
this.performQuery (this.state.queryString);
}

// Manage the onChange event every time the user types in the search box.
updateQuery = e => {
const queryString = e.target.value;
this.setState (
{
queryString // Save the query string entered by the user
},
() => {
this.performQuery (queryString); // Activate a new search
}
);
};

performQuery = queryString => {
client.search (queryString, {}). then (
answer => {
this.setState ({
answer
});
},
error => {
console.log (`error: $ {error}`);
}
);
};

render () {
const {response, queryString} = this.state;
if (! response) returns null;

return (
      

Node module search

{/ * A search box, linked to the query string value and aChange manager * /}

{response.info.meta.page.total_results} Results

{response.results.map (result => (

Name: {result.getRaw ("name")}

Description: {result.getRaw ("description")}

))}
); } } // ... Truncated!

Debounce!

Within this iteration, whenever a change is detected inside the box, a search will occur, which may intensify in our systems. To remedy this, we will apply a _debounce_ function, courtesy of Lodash.

Installation $ npm --save lodash

Debounce is a method of limiting the speed of the number of incoming requests based on a defined number of milliseconds. A user is thinking about how to express his query, make typos or type very quickly … and so it is not necessary to query on every change detected.

Wrapping ours performQuery internal method of a bounce function from Lodash, we can specify a 200ms speed limit – 200ms must pass without input before the next search query starts:

// App.js
// ... Truncated!
import {debounce} from "lodash"; // Import bounce

// ... Truncated!

performQuery = debounce (queryString => {
client.search (queryString, {}). then (
answer => {
this.setState ({
answer
});
},
error => {
console.log (`error: $ {error}`);
}
);
}, 200); // 200 milliseconds.

// ... Truncated!

In addition to pausing our servers, speed limitation can help smooth users' queries. It goes very far! Feeling is important.

Next one…

This is the beginning of a quality, React-ified research experience. Going forward, there are many great things to add. We could add a style or implement dynamic app search capabilities like Facets, Curation or Relevance Tuning. Or, we could explore the Analytics Suite API to bring valuable information about user search activity to light.

If we wanted to get really deep, the README inside the master branch extends the tutorial to create URI-based status management, add slick styles, pagination, filtering, and multifaceted search. With some stylistic personalizations, it could become the basis for a high quality research experience.

The fully developed example is available here: http://packagehunt.swiftype.info/.

package_search.png

Summary

In the past, the construction of a relevant and rewarding appication search has been complicated. Elastic app search is a quick and managed way to write useful and tunable searches in web applications. The best part? Both engineers and less technical stakeholders can manage key features from a clear and intuitive dashboard. You can go directly to App search with a free 14-day trial without credit cards.


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