What Is Elasticsearch?
Elasticsearch is a real-time, distributed, and open-source search and analytics engine. It’s built on top of Apache Lucene, a robust full-text search library, and is often used for complex enterprise search scenarios.
Elasticsearch is not just a search engine; it is also a document-oriented NoSQL database that stores, retrieves, and manages document-oriented or semi-structured data. It is schema-free, meaning that you can index and search your data without worrying about its structure. This makes Elasticsearch flexible and adaptable to various types of data.
Elasticsearch is known for its scalability and resilience. It is designed to be deployed on clusters of servers where it can scale horizontally. This means you can start with a single server and then add more servers to the cluster as your data and query load increases. In other words, Elasticsearch grows with your needs.
What Is MongoDB?
MongoDB is a source-available cross-platform document-oriented database program. As a NoSQL database, MongoDB avoids traditional table-based relational database structures in favor of JSON-like documents with dynamic schemas, making the data integration easier and faster for certain types of applications.
MongoDB is designed to handle a large amount of data. It uses a powerful document data model that is much faster than traditional relational databases, especially when dealing with large volumes of data. The document data model presents data in a way that is natural and intuitive for developers to work with, thereby increasing developer productivity.
MongoDB also provides high availability with its replica sets. A replica set in MongoDB is a group of MongoDB processes that maintain the same data set. This ensures that your data is always available and safe. Moreover, MongoDB offers horizontal scalability with its sharding feature, allowing for data distribution across multiple servers.
In this article:
Elasticsearch vs. MongoDB: 5 Key Differences
1. Data Structure and Data Model
Both databases are document-oriented, but there are key differences. Elasticsearch stores data in a semi-structured JSON document. This format allows for flexible data representation, accommodating complex data relationships and hierarchies.
MongoDB, however, uses BSON (Binary JSON) format, a binary representation of JSON-like documents. BSON extends the JSON model to provide additional data types and to be efficient for encoding and decoding within different languages. This means MongoDB can handle more data types than Elasticsearch, including date and binary.
Learn more in our detailed guide to Elasticsearch architecture (coming soon)
2. Query Capabilities
Elasticsearch’s primary function is as a search engine. It provides powerful full-text search capabilities and boasts a wide array of search features such as customized splitting text into words, customized word stemming, faceted search, and more.
MongoDB, on the other hand, offers rich query capabilities with full CRUD (Create, Read, Update, Delete) operations. It supports secondary indexes, complex aggregations, and various types of querying such as range queries, regular expression queries, and geospatial queries. While MongoDB does provide full-text search capabilities, they are not as comprehensive or powerful as those of Elasticsearch.
3. Performance and Scalability
Elasticsearch is built on Apache Lucene and is designed to handle large volumes of data, making it particularly well-suited for search and analytics purposes. It can quickly index and search through vast amounts of data, and its distributed nature allows it to scale horizontally.
On the other hand, MongoDB, a NoSQL database, is known for its high performance in handling diverse data types. It uses a document-based data model, which allows for great flexibility. MongoDB can scale horizontally by sharding data across many servers, thus increasing read and write throughput. However, it’s important to note that while MongoDB excels in operational workload performance, it may not provide the same level of performance for heavy analytical workloads as Elasticsearch.
4. Backup and Recovery
Elasticsearch provides snapshot and restore modules for backing up and restoring your data. You can take snapshots of individual indices or an entire cluster, and these snapshots can be stored in a remote repository.
In contrast, MongoDB offers a variety of backup solutions, including mongodump, a utility for creating binary export of the contents of a database, and MongoDB Cloud Manager or Ops Manager, which provides continuous, online backup with point-in-time recovery. MongoDB’s backup solutions offer more flexibility and options compared to Elasticsearch.
5. Use Cases and Applications
Elasticsearch’s strength lies in search and analytics. It is widely used in developing search engines, logging and log analysis systems, business analytics, and more.
MongoDB, with its flexibility and scalability, is a good fit for a wide range of applications. It is often used in content management systems, mobile applications, real-time analytics, and IoT applications. It’s also worth mentioning that MongoDB can handle complex queries and diverse data types, which makes it a versatile choice.
Choosing Between Elasticsearch and MongoDB
Data Storage and Analysis Needs
When choosing between Elasticsearch and MongoDB, you should consider your data storage and analysis needs. If your project involves handling large volumes of structured and unstructured data and requires extensive search functionality and analytics, Elasticsearch could be the better choice.
On the other hand, if your project involves handling diverse data types, complex queries, and requires a flexible data model, MongoDB may be more suitable. MongoDB’s document-based model provides a high level of flexibility, allowing you to store data in various formats.
Data Structure Requirements
The structure of your data is another important consideration. Elasticsearch is schema-less, which means it can handle unstructured data. This makes Elasticsearch particularly useful when you need to index and search through large volumes of unstructured data.
MongoDB, while also schema-less, uses a document-based model. This model allows you to store data in a semi-structured format, which can be beneficial when dealing with diverse data types and complex hierarchies.
Real-Time Processing
Real-time data processing is another factor that can influence your choice between Elasticsearch and MongoDB. Elasticsearch is known for its real-time search and analysis capabilities. It provides near real-time search, which can be a significant advantage in applications that require quick response times.
MongoDB, while not designed for real-time analytics, does offer strong performance for operational workloads and can handle real-time processing for specific use cases.
Integration
Lastly, integration with other tools and systems could be a deciding factor. Elasticsearch integrates well with other tools in the Elastic Stack, like Logstash and Kibana, and offers robust APIs for integration with other systems.
MongoDB also offers robust APIs for integration and has a broad ecosystem of tools and services, including MongoDB Atlas, a fully-managed cloud database service.
Learn more in our detailed guide to:
Elasticsearch vs mongodb
Elasticsearch on AWS
Elasticsearch on docker
Elasticsearch tutorial