Elasticsearch MongoDB NodeJS Integration: 4 Easy Steps

Most enterprises collect vast volumes of data over time. This data usually contains important information regarding the business, customers, etc. Storing this data in a stable database is advisable to ensure data security and integrity. MongoDB provides a simple document-type data storing and analyzing environment for your data.

Given the vast amount of data stored in MongoDB document-like files, it can be challenging to pull out some required data portions. In such situations, engines like Elasticsearch come to use.

With Elasticsearch’s prompt indexing and analytical capabilities, you can draw filtered information from your stored data within a few minutes. All you need to do is connect the Elasticsearch cloud to MongoDB. 

The Elasticsearch MongoDB NodeJs Integration is handy for replicating data from the database to Elasticsearch for indexing and filtering.

There are many ways to connect Elasticsearch and MongoDB. This article will walk you through the Elasticsearch MongoDB NodeJs Integration. 

Prerequisites

• Knowledge of database.

What is Elasticsearch?

Elasticsearch is an open-source search and analytics engine for various data types, including numerical, textual, geospatial, structured, and unstructured.

It was built on Apache Lucene and released in 2010. Elasticsearch is a central component of the Elastic Stack, also known as the ELK (Elasticsearch, Logstash, and Kibana), a set of tools for data analysis, ingestion, enrichment, and visualization. 

To use Elasticsearch, you just need to ingest raw data into the platform, where it is automatically parsed, normalized, and enriched before indexing.

Once indexed in Elasticsearch, you can search and retrieve complex summaries of their data.

It is known for its simple REST APIs, scalability, speed, and distributed nature. Elasticsearch indexes several data types for many use cases like application search, logging and log analytics, business and security analytics, infrastructure metrics, container monitoring, etc.

Understanding MongoDB with Node.JS

MongoDB is a simple document model NoSQL database that stores data in flexible, JSON-like documents. The fields can vary from document to document, and users can change the data structure over time.

It is well renowned for its load balancing and horizontal scaling features, which have provided application developers with flexibility and scalability.

MongoDB has a long history of working with Node.JS, an open-source server environment built on Chrome’s JavaScript. The combination is perfect as the schema need not be well-structured.

Due to MongoDB’s document-like data collection, it becomes convenient to deal with varied data types over the internet to be stored and accessed in several web applications using NodeJS. 

The official MongoDB Node.JS driver enables Node.JS applications to integrate with MongoDB. The driver provides an asynchronous JavaScript API and implements the network protocols to read and write from MongoDB databases.

In the next section, we will deep dive into the steps required for Elasticsearch MongoDB NodeJs Integration.

Steps for Elasticesearch MongoDB NodeJS Integration

The Elasticsearch MongoDB NodeJs Integration is beneficial for replicating data from the database to Elasticsearch for indexing and filtering.

Let’s look at the steps to Elasticsearch MongoDB NodeJs connection. Data synchronization is done in real-time via a Node-based MongoDB Elasticsearch connector.

Step 1: Installing and Setting up MongoDB

• Download the MongoDB MSI installer package from here.
• Install MongoDB with the installation wizard.

• Accept the license agreement, then click Next.
• Select the Complete setup > Run Service as Network Service user.
• Click Install.

• Create a Data Folder in C Drive and create another folder inside it as db.
• Set up Alias Shortcuts for Mongo and Mongod by opening your Hyper terminal running Git Bash.
• Change to your home directory via the cd~ command.
• Create a file called .bash_profile via the touch .bash_profile command.
• Open the file using the vim .bash_profile command. Hit the I key and enter the insert module. 
• Exit the module and type: wq! To save and exit. Relaunch Hyper and type mongo –version. You should see something like this.

• You have successfully installed and set up MongoDB.

Step 2: Installing Elasticsearch

• Download and install the .zip package for Elasticsearch 8.3.2 from here. Ensure that you install the MSI package.
• Unzip it to create a folder called elasticsearch-8.3.2.
• cd to the folder with cd C:\elasticsearch-8.3.2.
• Enable automatic creation of system indices by configuring action.auto_create_index in elasticsearch.yml.
• Run Elasticsearch from the command line using .\bin\elasticsearch.bat.
• Configure Elasticsearch on the command line using: .\bin\elasticsearch.bat -Ecluster.name=my_cluster -Enode.name=node_1.
• Check that Elasticsearch is running by sending an HTTPS request to port 9200 on localhost via 

• Enter the password for the elastic user you generated during installation to receive a running response.
• You can also install and run Elasticsearch as a service. Check here to know how.

Step 3: Installing Node.js

• Download the installer from here. Choose the latest version to include the NPM package manager.
• Install Node.js and NPM from the wizard.

• Make sure to choose the npm package manager in the next step, not the Node.js runtime.
• Click Install.

Step 4: Downloading and Using Github node-mongodb-es-connector

• Use the npm install es-mongodb-sync command.
• To use this connector, you must create a JSON file named ElasticsearchIndexName.json or any name .json in the crawlerData folder.
• You need to specify some required configurations:
  • m_database – MongoDB database.
  • m_collectionname – MongoDB collection.
  • m_filterfilds – MongoDB filterQuery for simple filtering support (null default value).
  • m_returnfilds – MongoDB needs it to return to the field (null default value).
  • m_connection :
    1. m_servers – MongoDB servers (array).
    2. m_authentication – username, password, authsource, replicaset, and ssl (false default value).
  • m_documentsinbatch – an integer specifies the number of documents to send to Elasticsearch in batches.
  • m_delaytime – milliseconds between batches (default value is 1000ms).
  • e_index – Elasticsearch index where documents are saved.
  • e_type – Elasticsearch type given to documents.
  • e_connection :
    1. e_server – URL of a running Elasticsearch cluster. 
• To start, open the CMD command window and use the node app.js command.
• To use the Elasticsearch pipeline, install the Ingest Attachment Processor Plugin. It allows Elasticsearch to extract files in standard formats like PPT, XLS, and PDF using the Apache text extraction library Tika. Use:

sudo bin/elasticsearch-plugininstall 
ingest-attachment

• Create a pipeline to Elasticsearch as follows:

PUT _ingest/pipeline/mypipeline
{
  "description" : "Extract attachment information from arrays",
  "processors" : [
    {
      "foreach": {
        "field": "attachments",
        "processor": {
          "attachment": {
            "target_field": "_ingest._value.attachment",
            "field": "_ingest._value.data"
          }
        }
      }
    }
  ]
}

• Modify the node data by using “e_pipeline”: “mypipeline”.
• Results will be displayed in both MongoDB and Elasticsearch data.

Kudos to you for completing the Elasticsearch MongoDB NodeJS connection.

Conclusion

In this article, you learned about an analytics and search engine, Elasticsearch, and a simple document-based database MongoDB. Moreover, you have acquired knowledge about Elasticsearch MongoDB NodeJS Integration.

You also learned about connecting the two to be able to retrieve filtered sections from the stored data via Elasticsearch indexing.

Amongst other ways of combining the two, the article discusses the Node.js framework to use Elasticsearch services on data stored in MongoDB databases. In a nutshell, you have walked through Elasticsearch MongoDB NodeJS Connection steps.

Don’t forget to drop your comment on the Elasticsearch MongoDB NodeJS connection. Apart from MongoDB, you would use several applications and databases across your business for Marketing, Accounting, Sales, Customer Relationship Management, etc.

It is essential to consolidate data from all these sources to get a complete overview of your business performance. To achieve this, you need to assign a portion of your Engineering Bandwidth to Integrate Data from all sources, Clean & Transform it, and finally, Load it to a Cloud Data Warehouse or a destination of your choice for further Business Analytics.

All of these challenges can be comfortably solved by a Cloud-Based ETL tool such as Hevo Data.

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