Blog Choosing the Right Database: Elasticsearch vs. MongoDB
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Choosing the Right Database: Elasticsearch vs. MongoDB

In the ever-changing landscape of database technologies, we are spoilt for choices, often confusing us. We are hung up on the question, “Which is a better solution for my project?”. This blog post will discuss two prominent players in the database field—Elasticsearch and MongoDB. Analyze and review their features, pros, and cons to make an informed decision. Before we jump into it, if you have a project and are trying to visualize your data, look at our Elasticsearch Analytics and MongoDB Analytics pages.

Elasticsearch: Database with the Power of Search

Elasticsearch is a powerful and open-source search and analytics engine. It’s part of the Elastic Stack, including Logstash and Kibana. It helps you search, analyze, and visualize your structured and unstructured data quickly and in a scalable way. 

Elasticsearch uses a distributed, JSON-based document store to search, aggregate, and analyze large volumes of data efficiently in real time.

Elasticsearch automatically stores the original document and adds a searchable reference to the document in the cluster’s index. You can then search and retrieve the document using the Elasticsearch API. 

MongoDB: Document Store Database

MongoDB is a popular open-source NoSQL database management system. Unlike Elasticsearch, MongoDB stores data in a flexible, schema-less format using a document-oriented model. Instead of tables and rows, MongoDB uses collections and documents.

The MongoDB environment provides the users with a server to create databases that store data as records of collections and documents. Documents are composed of field and value pairs. They are the basic unit of data in MongoDB. The documents are similar to JSON but use a variant called Binary JSON (BSON). 

Feature Comparison Between Elasticsearch and MongoDB

Now that we know what Elasticsearch and MongoDB are, let’s compare their key features.

Use Cases

Elasticsearch is ideal for applications requiring powerful search functionality, such as content discovery, log analysis, and real-time analytics. Elasticsearch will always be the better choice when full-text search is a requirement. Elasticsearch also wins the race regarding log analytics since it offers a wide range of aggregation queries and supports products like Kibana and Logstash—all of which make log analysis much more effortless.

MongoDB is well-suited for various use cases, including content management, blogging platforms, and applications with evolving schemas. When the data is in NoSQL format, you need a highly scalable database that requires CRUD operations without full-text search support. MongoDB also supports full-text queries with the help of text-based indexes, but its search speed is slow, and it needs the tokenizers and analyzers that come with a search server.

Query Language and Schema Flexibility

Elasticsearch uses a powerful and expressive DSL (Domain Specific Language) query that allows complex queries, full-text search, and aggregations. Regarding Schema Flexibility, Elasticsearch is schema-less, meaning you can index data without predefining a schema. It dynamically maps fields based on the data.

MongoDB uses a JSON-like query language. It supports various query operators and allows flexible querying. MongoDB is schema-less, enabling you to insert documents without a predefined schema. However, it promotes validation for more structured data if needed.

Search Capabilities

Elasticsearch is built explicitly for search and offers robust full-text search capabilities. It supports features like relevance scoring, fuzzy matching, and geospatial search out of the box.

MongoDB falls a little behind in search capabilities. It provides basic text search capabilities but may not be as advanced or specialized as Elasticsearch.

Scalability

Elasticsearch is known for its horizontal scalability. It can quickly scale out by adding more nodes to a cluster. As discussed in the intro, it uses a distributed architecture, and data is automatically distributed across nodes.

MongoDB also supports horizontal scalability through sharding. It allows you to distribute data across multiple servers or clusters. Sharding in MongoDB requires careful planning of shard keys for optimal distribution. It is safe to say either could be a great choice if scalability is your primary concern.

Real-Time Analytics

Elasticsearch wins the race here since it is well-suited for real-time analytics. It provides near real-time indexing and supports complex aggregations and analytics queries. MongoDB can handle real-time analytics to some extent, especially with the Aggregation Pipeline. However, it may not be as specialized for analytics as Elasticsearch is.

Community

It’s a tie when it comes to community and support. Both the database platforms have a well-established community base. Elasticsearch has a large and active open-source community. It’s widely adopted, and plenty of resources, plugins, and community support are available.

MongoDB also has a strong community. It has been around for a while and is widely used, with a good ecosystem of tools and libraries.

At Knowi

Knowi is a Business Intelligence (BI) platform that unifies analytics across structured, unstructured, and semi-structured data. Knowi has firmly established itself as a leader in the elastic search and MongoDB domain. Born from a vision to transform data into actionable insights, Knowi has consistently delivered innovative solutions tailored to the evolving needs of businesses worldwide. With a global clientele that includes industry leaders and dynamic startups, Knowi’s commitment to excellence and innovation remains unwavering.

As the business landscape continues evolving, so does Knowi, ensuring our offerings remain cutting-edge and relevant. If you want to elevate your data-driven decision-making, there’s no better time to explore what Knowi offers.

Ready to experience the power of Knowi firsthand? Start a free trial today and embark on a transformative analytics journey. We provide native integration for MongoDB analytics and ElasticSearch analytics, eliminating the need for an ETL tool and delivering a high-level intuitive UI, allowing users to generate queries and maneuver the data with a simple drag-and-drop functionality.

Conclusion

Both Elasticsearch and MongoDB are powerful in their own right. The decision between Elasticsearch and MongoDB ultimately hinges on your project’s specific requirements. If your project heavily emphasizes search functionality and real-time analytics, Elasticsearch might be the ideal choice. On the other hand, if you prioritize flexibility in data modeling and seamless development, MongoDB could be the perfect fit.

Want to see how Knowi can help?

Sign up here for a 15-minute demo where we will learn more about your use case and recommend a few features that may be a good fit for what you're trying to accomplish.

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