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Elasticsearch vs. Mongo: Which One Is Better?

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Elasticsearch vs. Mongo: Which One Is Better?

Relational databases can be quite powerful when dealing with data in separate tables that need to find connections to make that data useful to an application. This is why they are called relational databases — because there are common threads that show the relationship among the data used in the application. However, you may find some situations call for a more flexible type of database that gives you much more scalability as your needs grow.

This is precisely where NoSQL databases shine. Where relational databases use SQL syntax to perform queries to return data from its many tables, NoSQL databases don’t use tables at all. They are document-based databases that can offer more control and flexibility over data storage and retrieval. However, don’t let the name deceive you. NoSQL does not literally mean “no SQL.” It simply stands for “not only SQL.”

Two of the more popular NoSQL databases are Elasticsearch and MongoDB. Today’s article will be a deep dive into these two, so you can determine which one is best for your specific needs. Let’s get started!

Elasticsearch vs. Mongo: Side-By-Side Comparison

FeatureElasticsearchMongo
Developed byShay BanonDwight Merriman, Eliot Horowitz, and Kevin Ryan
Year released20102007
Licensing modelELv2, SSPLSSPL, commercial
OS supportedMacOS, Windows, LinuxMacOS, Windows, Linux
Easy to install?yesyes
Commonly used with programming languageJava, C#/.NET, PHP, Python, RubyJavaScript, Ruby, Python, C#/.NET
Latest version8.7.06.0
Type of databaseNoSQLNoSQL
Data storageJSONBSON
Database supportnonrelational, document-basednonrelational, document-based
Use casestext search enginegeneral purpose

Elasticsearch vs. Mongo: What’s the Difference?

Built on Apache Lucene, Elasticsearch is a NoSQL database that’s a part of the ELK stack, which includes Elasticsearch, Logstash, and Kibana. It stands at the foundation of this powerful stack that provides tools for data ingestion, enrichment, storage, analysis, and visualization. On its own, Elasticsearch is a search engine that analyzes the following types of data: textual, numerical, geospatial, structured, and unstructured. This data is stored in JavaScript Object Notation (JSON) documents, connecting keys with their corresponding values.

Written in the C++ language, MongoDB is also a NoSQL database and is a part of the MEAN stack, which includes MongoDB, Express, Angular, and Node. The architecture of the MEAN stack is designed to streamline building JavaScript applications, which often have a need for handling JSON data.

C++ vs. JavaScript
MongoDB is a NoSQL database written in the C++ language.

If you’re not familiar with JSON yet, it represents data in a text-based format that uses JavaScript object syntax to design structured data. It is lightweight and makes it easy to work with temporary data that gets sent to a web application through user interaction or connected APIs. While Elasticsearch uses JSON, MongoDB does not. That is one of their most notable differences.

Instead of storing data in JSON documents, MongoDB stores them in what they refer to as “JSON-like” documents. Specifically, their data is stored in BSON documents, which use a binary form of JSON to represent data. (We’ll explore more what this looks like below when we discuss the differences in syntax.)

Syntax

Like we talked about above, Elasticsearch uses JSON syntax to represent data. Once you understand the structure of JSON data, it’s fairly easy to understand what’s happening. Here is an example of what JSON looks like:

{
	“_id”: 1,
	“username”: “janesmith”,
	“name”: {“first”: “Jane”, “last”: “Smith”}
}

In this basic example, this syntax is setting up an entry of a database with an ID of 1, and it also sets values for both the username and the first and last name of the user. If you’re familiar with Python and other programming languages, this syntax probably looks familiar to you because it looks precisely like a dictionary of key and value pairs. If you’re not familiar with dictionaries, the first thing you likely noticed is that this form of storing data is fairly easy to read. BSON, on the other hand, looks much different because it’s written in a language the computer understands—binary.

MongoDB uses BSON to store data because it’s much faster than working with JSON documents. Here is an example of what BSON looks like:

{“home”:”page”}
\x32\x00\x00\x00
\x05
web\x00

(Note: this isn’t the full document, but this gives you a good idea of how it looks.)

One of the reasons why BSON is faster is because it allows for more information about the data to be stored in the document, which makes it easier to search through. However, the BSON documents tend to take up more space than JSON, so you have to decide what benchmark would make or break your application.

Performance

The differences in performance between Elasticsearch and MongoDB lie in their use cases. Because these two NoSQL databases are intelligently designed for different functions within a web application, how you perceive their performance differences will depend on what you’re using the database for. Both these options perform well for many use cases that you’ll learn about below.

Use Case: Search Engine

Elasticsearch was designed as a NoSQL search engine—hence its name—which allows for better performance when you’re executing a full-text search. This provides a great foundation for your web application with the following tasks:

  • Working with and storing unstructured or semi-structured data
  • Document ranking by relevance to each search performed
  • Analysis tool for logs generated by the system
  • Extracting metrics in real-time for data
  • Infrastructure monitoring

Elasticsearch achieves all this through inverse indexing. This works like an index in a textbook, where you see a list of terms in the back with corresponding page numbers. Elasticsearch builds these inverse indexes with keywords that link back to particular documents that reference those terms. 

So, in theory, it would look something like this when dealing with searching through a list of websites that deal with writing resumes:

KeywordDocument Location
resume3, 5, 6, 9
CV2, 3, 4
writing1, 3, 6
cover letter2, 9

Use Case: Application Data

On the other hand, if you need a database that can handle your application data well, MongoDB offers an excellent solution for what you’re building. MongoDB provides excellent support for the following tasks:

  • Real-time views of your data
  • Working with IoT (Internet of Things) data
  • Saving and accessing data on mobile applications
  • Options to personalize data for each user
  • CRUD operations for data and content management

One thing that makes all these tasks perform faster than they might with Elasticsearch is the ability to work with binary data. Like we discussed above, MongoDB’s documents use BSON notation, which is a binary version of JSON. This type of representation of data is written in language the computer can read directly — those 1s and 0s that likely don’t mean a lot to the human eye. Because this type of data storage allows for more information to be stored, MongoDB can be more precise with its data retrieval, which is absolutely critical when dealing with CRUD operations that require data to be saved and accessed without error.

yottabyte
One advantage of MongoDB is the ability to work with binary data. Its documents use a binary version of JSON.

Elasticsearch vs. Mongo: Performance Comparison

In the table below, we’ve marked the option that provides the best performance in each scenario. 

Performance factorElasticsearchMongoDB
Full-text search
Speed
Data storage
Scalability
Analytics

Elasticsearch vs. Mongo: 6 Must-Know Facts

  • Elasticsearch is written with Java, while MongoDB is with C++. Both these languages are object-oriented, which means their architecture is designed around objects over methods or functions. These objects are typically set up through classes, which makes them ideal for working with databases. 
  • Though MongoDB’s BSON documents allow for faster traversing of data, they often take up more space than JSON documents because of their binary notation. However, since this binary language can be easily read by machines, this doesn’t slow down MongoDB’s performance.
  • Both Elasticsearch and MongoDB provide horizontal scalability, meaning you would need to add more machines to the server to increase computing power. Relational databases, on the other hand, require horizontal scalability, which means the server hardware must be upgraded when the database needs to scale. Horizontal scalability allows for less downtime when upgrading and allows you to better manage increased traffic on your server.
  • The syntax in Elasticsearch’s documents can be read by both human and machine, while MongoDB’s can only be read by machine since it is written in binary code.
  • Elasticsearch is better used strictly as a search engine; however, you can use both Elasticsearch and MongoDB alongside each other if you also need to implement a database for your application. MongoDB also has a search engine, known as MongoDB Atlas, if you prefer to use their recommended products.
  • MongoDB is typically used in gaming, web, IoT, and desktop applications where precise matches to your data are essential. 

Elasticsearch vs. Mongo: Which Is the Best Choice?

As with many programming languages and other technologies, it depends heavily on your use case and what type of resources your application needs most. If your application has a stronger need for search engine capabilities, we’d recommend considering Elasticsearch over MongoDB. However, if you have a stronger need for document storage rather than search, MongoDB will provide many benefits over Elasticsearch because of its speed, storage performance, and scalability options. 

Frequently Asked Questions

What’s one similarity between Elasticsearch and MongoDB?

The biggest similarity between the two NoSQL options is they are both document-based databases. This makes them both extremely flexible options compared to their SQL counterparts, such as PostgreSQL and MySQL, allowing for easier update operations and improved performance. Though they have very different use cases, they also both work with either JSON or JSON-like documents.

What notable companies use Elasticsearch?

Walmart, Adobe, T-Mobile, Audi, Cisco, Procter & Gamble.

What notable companies use MongoDB?

Forbes, Toyota, Bosch, Volvo, Verizon, Humana, and Shutterfly.

Which is easier to learn Elasticsearch or Mongo?

This depends primarily on your experience as a developer. Because Elasticsearch stores its data in JSON documents, JavaScript experts will have an easier time learning its syntax. However, if you are a beginner to the world of development, we recommend starting with MongoDB, as it is easy to install and implement in a wide variety of applications.

What’s the main difference between Elasticsearch and MongoDB?

MongoDB is primarily a database, whereas Elasticsearch is primarily a search and analytics engine, although both have similar use cases. While Elasticsearch uses JSON, MongoDB uses BSON, a “binary” form of JSON. A big difference between Elasticsearch and MongoDB lies in their query results. MongoDB has more precision with its queries because it returns exact matches to them, while Elasticsearch returns the most probable results based on the relevance to each search.

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