Elasticsearch is a robust search database built with Javascript for querying and indexing data. Integrating the elasticsearch function into an application greatly improves the platform's search experience and overall user experience, making it such a helpful tool. Elasticsearch is also quite versatile such that it can also be used to perform tasks that involve data search, analysis, and visualization.
This article provides a general overview of elasticsearch, its importance, some common use cases, and a step-by-step guide for incorporating it into a Node Js application.
Elasticsearch is one of the most efficient and well-known search engines that allow users to search through large amounts of data. Also, Elasticsearch can add advanced search functionality to a web application and significantly improve the user experience.
If you're building an application with Node.js, integrating Elasticsearch is a great feature that can enhance the search functionality of that platform.
In this article, we'll discuss how to integrate Elasticsearch into your Node.js application, but first, let's define Elasticsearch.
What is Elasticsearch?
Elasticsearch is a NoSQL search database with a detailed JSON-centric REST API for querying and indexing data. It is built via Java and on top of Apache Lucene to provide a scalable and high-performing search experience.
Over time, Elasticsearch and all that pertains to it have drastically evolved, transforming it into a group of software components known as Elastic Stack, or ELK Stack. Some examples of the stack include the Elasticsearch service, Kibana (a tool used to manage and visualize data), and Logstash (a tool used for indexing and processing data into Elasticsearch).
Use Cases of Elasticsearch
Elasticsearch is a highly versatile tool for various use cases involving data search, analysis, and visualization. Some common uses of Elasticsearch include:
1. Full-text Search: It is commonly used as a search engine for large datasets, allowing users to search and retrieve relevant information from unstructured data quickly.
2. Log analysis: You can use Elasticsearch to index and Search log files, making it easier for IT teams to troubleshoot issues and identify trends in application or system logs.
3. Business intelligence: It can be used to build interactive dashboards and visualizations that enable organizations to gain insights into their data and make better-informed decisions.
4. E-commerce search: Elasticsearch can power search functionality on e-commerce websites, allowing customers to easily find products that meet their criteria.
5. Geographic search: Another common use case is building location-based search functionality, allowing users to search for data within a specific geographic area.
6. Security Analytics: Users can analyze security data from various sources, allowing security teams to detect and respond to security threats in real time.
7. Machine learning: Elasticsearch can be integrated with machine learning frameworks to activate advanced analytics and prognostic modeling.
At Fetchly Labs, developers use Elasticsearch to provide solutions for many use cases like application search, website search, business search, logging and log analysis, metrics and container monitoring, and business analytics.
Some of our projects have seen comprehensive optimizations through the use of Elasticsearch for precise, fast search operations as opposed to performance while using traditional SQL databases such as MySQL.
Integrating Elasticsearch into a Node.js App
Before developers at Fetchly attempt to integrate Elasticsearch into a Node.js app, Node.js must be installed because the backend of the demo app will run on the tech stack. Doing so will make it possible to code the backend aspect of the platform in Javascript.
Pair programming can also be encouraged when more than one developer works on a client’s application.
Remember that Elasticsearch should NOT be used as a complete NoSQL database. Instead, it is strictly recommended to be used only for the searchable parts of your data. So ensure you're indexing the necessary data and fetching the bulk of your content from another proper database solution such as PostgreSQL or MySQL.
Here is the link to install Node.js:
https://nodejs.org/en/download/, which comes with npm (the node package manager) that can be used to install javascript libraries.
Steps
Step 1: Install Elasticsearch
First, you need to install Elasticsearch. You can download Elasticsearch from the official
websiteor make use of a package manager like
Homebrew (on Mac) or
apt-get. Remember that the last two options involve a few extra steps, as you must manually add their repositories.
Step 2: Install the Elasticsearch Node.js Client
Next, you need to install the Elasticsearch Node.js client. Run the command below into your terminal:
npm install elasticsearch
This will install the Elasticsearch Node.js client into your project.
Step 3: Connect to Elasticsearch
To connect to Elasticsearch, you must create a new instance of the Elasticsearch client. Start by creating a new instance of the Elasticsearch class first, then input the URL of your Elasticsearch server.
For example Javascript
const { Client } = require('@elastic/elasticsearch')
const client = new Client({ node: 'http://localhost:9200' })
Doing so creates a new instance of the Elasticsearch client connected to the Elasticsearch server running on localhost:9200.
Step 4: Index Data
To search for data in Elasticsearch, you first need to index your data. Indexing is the process of adding your data to Elasticsearch so that it can be searched. You can index data using the index method of the Elasticsearch client.
For example
client.index({
index: 'myindex',
body: {
title: 'My Document',
content: 'This is my document'
}
})
This indexes a new document with the title "My Document." Also, indexing data includes the content of "This is my document" in the "myindex" index.
Step 5: Search Data
Once your data is indexed, you can search for it using the search method of the Elasticsearch client, as depicted in this example:
client.search({
index: 'myindex',
body: {
query: {
match: { title: 'document'}
}
}
})
This searches the "myindex" index for documents with a title that contains the word "document".
Step 6: Handle Results and errors
When you search for data, Elasticsearch returns a Promise Object that contains information about the search results. Depending on your choice, you can handle the results in your Node.js using a standard then clause or async-await.
Finally, to handle any errors, you can chain a .catch to the previous result for proper diagnosis.
client.search({
index: 'myindex',
body: {
query: {
match: { title: 'document'}
}
}
}).then((result) => {
console.log(result.hits.hits)
}).catch((error) => {
console.error(error)
})
This logs an array of search results into the console, and in the event that there are any errors, those results are displayed instead.
Final Thoughts
Integrating Elasticsearch into your Node.js application can significantly enhance your search functionality. With the way Elasticsearch indexes data, it is easier to access many options that can be crucial for fine-tuning search performance.
Depending on the nature of a business, the due diligence team at Fetchly can guide the integrating of Elasticsearch into an app for effectively managing data. This is important because only some applications created for a business require the Elasticsearch feature.
Some of the most suitable applications that require Elasticsearch include e-commerce apps, media and publishing, healthcare, financial services, and log analysis apps.
Overall, businesses and individuals can effortlessly search through large amounts of data in a Node.js app and enjoy a better user experience.
*This is not the official Fetchly opinion but the opinion of the writer who is employed by Fetchly*
