MongoDB Atlas is a data platform that includes a general-purpose document database with powerful search functionalities. Elasticsearch, on the other hand, is a search engine that you can use to store documents. There is a lot of overlap between the two products, so how do they compare to each other. This article will explain what both products are and how they compare when it comes to general database usage, advanced search features, and analytics.
What is Elasticsearch?
Elasticsearch is a RESTful search engine and analytics tool used to store and retrieve information from a database. It is part of a large collection of tools by Elastic and is usually used as part of the ELK (Elasticsearch - Logstash - Kibana) stack.
Elasticsearch is written in Java and is distributed with the SSPL license. It was first released in 2010. A cloud offering called Elastic Cloud is now also available.
Data is stored in Elasticsearch in the form of JSON documents. When needed, the data can be sharded across multiple nodes.
Uses of Elasticsearch
As a search engine, Elasticsearch is often used to provide full-text search capabilities to web applications. It is most of the time used as an extension of existing databases to provide additional capabilities to existing infrastructure.
It is also often used to detect anomalies or trends in large log files. When combined with Kibana and Logstash, it can offer fast search capabilities through billions of lines of log files.
What is MongoDB?
MongoDB is a general-purpose document database that can be used as a replacement for any other database. It can be used by itself or as part of MongoDB Atlas, a whole data platform that provides many additional features, including full-text search.
MongoDB is written in C++ and was built with scalability in mind. It can be scaled both horizontally and vertically to ensure the best performance possible. It was first released in 2007 and is now available under an SSPL license.
Data is stored in MongoDB using BSON, a binary form of JSON that optimizes disk space and supports advanced data types.
What is Atlas Search?
MongoDB Atlas offers a collection of tools to manage data. One of the available services is Atlas Search, the full-text search engine built by MongoDB.
Atlas Search adds full-text search capabilities to existing MongoDB databases. It is built on Lucene and uses the same MongoDB Query API as any other MongoDB service.
Elasticsearch vs MongoDB Comparison
Elasticsearch and MongoDB are similar on multiple points, and your choice of using one or the other depends on your use cases. Some of the main points are compared in the following table.
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Elasticsearch
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MongoDB with Atlas Search
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Storage type
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JSON documents.
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BSON documents with advanced data types.
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Supported languages
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Node.js, PHP, Python and Ruby.
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C, C++, C#, Go, Java, Node.js, PHP, Python, Ruby, Rust, Scala, Swift.
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Support for full-text search
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Yes, backed by Lucene.
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Yes, backed by Lucene.
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Scalability
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Horizontal scaling can be done, but reindexing shards requires many resources.
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Can be scaled both horizontally and vertically. The number of shards can be increased or decreased dynamically.
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Deployment
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In-premise or on Elastic Cloud.
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In-premise, on any major cloud provider, on multiple clouds via Atlas.
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Transaction guarantees
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On single documents only.
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Supports multi-document ACID transactions.
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Backup and management automation
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Backup and server administration need to be managed manually.
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Comprehensive management automation via the Atlas UI or the Atlas Admin API.
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You can use both Elasticsearch and MongoDB for different contexts. Depending on your use case, you might want to use one or even both together.
Comparing MongoDB and Elasticsearch as a database
Both MongoDB and Elasticsearch can be used as a document database. However, there are certain differences between the two products.
MongoDB was designed to be used as a document database from the start. It supports the BSON (Binary JSON) format, which enables its engine to optimize disk space, and supports more data types than regular JSON (JavaScript Object Notation). It also has a flexible schema design, but schemas can be enforced when needed. Elasticsearch only supports JSON with limited data types and cannot enforce schemas.
When it comes to fast read and write operations, MongoDB is faster because of the way it handles the indexes. Elasticsearch needs to refresh the indexes with the new data while only the affected record is changed in MongoDB on any update or create operation.
Because MongoDB is a database at its core, it is generally faster and more efficient at storing and retrieving data.
Comparing MongoDB and Elasticsearch as a search engine
Elasticsearch is a search engine by design. It aims at making it easier for software developers to implement full-text search capabilities to collections of data. Up until the release of Atlas Search, MongoDB only supported native text indexes. However, it now supports full-text search. Atlas Search, just like Elasticsearch, is built on top of Lucene, the open-source search engine library by Apache.
With Atlas Search, MongoDB can now provide quick search results with support for multiple data types, fuzzy searches, autocomplete, and text highlights. It also supports all the major analyzers from Lucene.
The main benefit of Atlas Search when it comes to full-text search capabilities is its integration with MongoDB Atlas. The advanced search functions use the same querying language as any other MongoDB product, and it doesn't need any additional software to be installed or maintained.
You can use Elasticsearch with MongoDB to provide even more flexibility on search results for specific advanced use cases when custom scripting is needed. However, you will need to implement a data synchronization mechanism to ensure that both databases are in sync.
Comparing MongoDB and Elasticsearch as an analytics tool
When it comes to data analytics, MongoDB provides all the necessary tooling to perform real-time analytics on data. It also has various BI connectors that you can use to export and manipulate data outside of Atlas. To visualize the data, MongoDB Charts is another tool part of the Atlas data platform that can help you create stunning dashboards.
Elasticsearch, when combined with Logstash and Kibana, forms the ELK stack. This stack is great at processing large data sets and providing insights on data trends and anomalies. Used together, they create a powerful monitoring solution that you can use as an additional monitoring solution for a MongoDB cluster.
Advantages of MongoDB over Elasticsearch
MongoDB is a fast and flexible database that you can use for just about any use case where a database is needed. In addition to the database itself, it also provides a suite of tools that constitute a full data platform. As part of this data platform is Atlas Search, which provides almost all the same capabilities as Elasticsearch.
The main advantage of using MongoDB Atlas for full-text search is the integration with the rest of the MongoDB ecosystem. The query language is already familiar for the software developers, and it does not require additional infrastructure to maintain.
For those edge cases where you might still need Elasticsearch, you must synchronize data across the two solutions to ensure the best possible performance from a database while getting those advanced scripting possibilities that Elasticsearch offers.
Summary
For a long time, external products needed to exist parallel to the existing database to add full-text search capabilities. With Atlas Search, this is no longer true. Searching through large data sets and advanced features such as fuzzy search, autocomplete, and custom scoring can now be done directly with the familiar MongoDB Query API. You can try it out now for free on MongoDB Atlas.
FAQ
Does Elasticsearch use MongoDB?
Elasticsearch has its own document storage mechanism and does not use MongoDB. However, you can use MongoDB alongside Elasticsearch.
What is Elasticsearch vs MongoDB?
Elasticsearch is a search engine, while MongoDB is a general-purpose document database. MongoDB Atlas, the cloud offering by MongoDB, is a complete data platform that includes Atlas Search for full-text search capabilities similar to Elasticsearch.
Is Elasticsearch like MongoDB?
Both have different use cases. Elasticsearch is generally used as a search engine, while MongoDB is a NoSQL document database.
How do I add data to Elasticsearch from MongoDB?
When Elasticsearch is used with MongoDB, data needs to be synchronized between the two databases. Some drivers exist to facilitate the task. Atlas Search does not require any synchronization.