Elasticsearch vs MongoDB: Which Document Database Is Better?

Elasticsearch and MongoDB are two of the most popular NoSQL, document-oriented databases used today for a variety of use cases. But with similarities in being non-relational and using JSON-like data models, how do you choose which one is the best fit for your application?

In this comprehensive comparison guide, we’ll analyze the differences between Elasticsearch and MongoDB across critical factors like query performance, scalability, ease of use and more. We’ll also offer examples of ideal use cases where each database shines based on their respective capabilities.

By the end, you’ll have the knowledge to confidently select between Elasticsearch and MongoDB for your next project.

Defining NoSQL, Document Stores and Differences from SQL

Before we dive into the Elasticsearch vs. MongoDB comparison, it‘s helpful to level set what exactly NoSQL document databases are and how they differ from traditional relational SQL databases.

NoSQL stands for “non-SQL” or “not only SQL." NoSQL databases are non-tabular and store data in more flexible formats compared to the predefined schema of SQL tables. They also use horizontal scaling for high availability instead of SQL’s vertical scaling approach.

Document databases are a type of NoSQL database that stores data in documents rather than rows or columns. These documents use standard formats like JSON, XML or BSON (in MongoDB) to represent data as key-value pairs, making them highly flexible and ideal for semi-structured data.

For context, here‘s a quick comparison of SQL vs NoSQL databases:

SQL Databases

• Relational model with predefined schemas
• Scale vertically on bigger hardware
• ACID compliant with transactions
• Better support for complex queries
• Examples: MySQL, Oracle, SQL Server

NoSQL Document Stores

• Flexible schemas without relations
• Scale horizontally on commodity hardware
• Eventually consistent
• Fast reads/writes for simple data
• Examples: MongoDB, Elasticsearch, CouchDB

Now that we’ve defined the NoSQL document database landscape, let’s explore how Elasticsearch and MongoDB fit into this category.

Overview of Elasticsearch and MongoDB

Elasticsearch is a highly scalable, distributed RESTful search and analytics engine built on Apache Lucene. It excels at text search, real-time analytics and complex data exploration across massive document collections.

Here are some key capabilities:

• Distributed search and analytics engine
• REST APIs with JSON documents
• Schema-free with JSON documents
• Fast and advanced full-text search
• Real-time analytics and complex queries
• Horizontal scalability to petabytes of data
• Often used with Kibana for visualization

MongoDB is a popular general purpose, document-based distributed database built for scalability, flexibility and high availability. It’s become very popular as a core database technology for web and mobile applications due to its ease of use and JSON-like documents.

Here are some of its main features:

• Document-oriented NoSQL database
• JSON-like BSON document model
• Indexes for faster queries
• Tunable consistency and tradeoffs
• Horizontal scaling with automatic sharding
• Multi-datacenter replication
• Popular for modern web and mobile apps

Now that have a basic orientation of Elasticsearch and MongoDB, let‘s analyze how they compare across key technical factors.

Feature Comparison: Elasticsearch vs. MongoDB

In reviewing this high-level comparison, we can see a few major differences emerge:

• Elasticsearch uses JSON documents while MongoDB uses BSON, a binary JSON format
• Query syntax differs with Elasticsearch using Query DSL while MongoDB relies on an Aggregation Pipeline
• But the largest differences lie in typical use cases and performance strengths which we’ll explore in-depth next.

Now let’s do a deeper dive on query performance and scalability benchmarks.

Performance and Scalability Comparison

Elasticsearch and MongoDB actually have some structural similarities – both are document stores that can scale horizontally and offer high availability configurations.

However, there are inherent performance differences depending on the workload patterns of reading, writing and updating data.

Query Performance

Elasticsearch is architected specifically for high performance text search and analytics. By default, it indexes all fields in JSON documents which enables extremely fast full-text queries across massive datasets.

Here are some key Elasticsearch performance benchmarks with 1 billion documents on 50 nodes:

• 50,000 search queries per second
• Sub-second response for searches
• 1-10 millisecond aggregations on terabytes of data

This query performance reveals why over 75% of Elasticsearch usage focuses on search-related use cases.

MongoDB isn‘t built primarily as a search engine – in fact, it doesn‘t index any fields by default without specific configuration. However, it offers extensive query functionality through an Aggregation Pipeline for transforming and filtering data sets.

Here are some key MongoDB performance benchmarks on commonly used hardware:

• Over 100,000 reads/writes per second
• Millisecond response times for simple queries
• Handles high volumes of concurrent CRUD operations and complex aggregation queries

So for queries involving defined indexes or relational-style aggregations, MongoDB performance remains very fast. But open-ended full-text search is not where it shines.

Scalability

Elasticsearch and MongoDB both scale horizontally using sharding, meaning they distribute data across clusters for greater throughput. This allows seamlessly adding capacity by deploying new servers.

Elasticsearch has been tested to handle hundreds of terabytes of text data and searches at petabyte scale. With tuning and large server deployments, there’s effectively no limit to scaling search volume and performance.

MongoDB also automatically shards databases as needed allowing massive horizontal scalability. There are documented cases of MongoDB managing over 500TB of data across clusters while maintaining performance.

So in practice, both these document NoSQL databases provide excellent scalability through distributed architectures – they only diverge when needing more specialized search performance at scale.

Use Cases: When Should You Use Elasticsearch or MongoDB?

Given the comparisons above, in what scenarios should you use Elasticsearch vs MongoDB? Below we’ll cover the most common usage scenarios where each database excels.

Use Elasticsearch For:

Advanced Search Engine – With fast full-text search, hit highlighting and geo-search, Elasticsearch enables powerful search experiences. Over 75% of users report deploying it for search use cases.

Real-time Analytics – The ability to quickly aggregate and process streaming data makes Elasticsearch perfect for metrics monitoring, log analysis and operational intelligence.

Text Analysis and NLP – Many organizations use Elasticsearch for sophisticated text analytics using machine learning capabilities like neural networks and vector search.

Use MongoDB For:

General Purpose Database – Thanks to flexible schemas, high availability and horizontal scaling, MongoDB serves as a popular database for modern applications.

Content Management – The JSON document model makes MongoDB a great choice for content focused use cases like catalogs, user profiles and web content management.

Mobile and IoT Apps – With tunable data consistency and robust replication, MongoDB is widely used as the database layer for global mobile and internet-of-things applications.

So in summary, opt for Elasticsearch for search and analytics workloads. Choose MongoDB for general data storage and management needs across devices and systems.

Ease of Use Comparison

Ease of use can encompass developer experience when querying databases as well as overall deployment and administration complexity.

In terms of languages and drivers, both Elasticsearch and MongoDB support all major coding languages like Java, Python, JavaScript, C#, PHP and more. There are some syntactical differences in their query DSLs between Elasticsearch and the MongoDB aggregation framework, but nothing prohibitive.

For deployment and administration, both also provide cloud-hosted database as a service options along with on-prem packages that make setup relatively painless. MongoDB does offer a few more user-friendly data administration features for non-technical users.

In general, both these NoSQL document stores are considered user-friendly compared to highly relational SQL databases that are notoriously trickier for developers to use.

So ease of use comes down primarily to how complex your queries are – full-text search syntax in Elasticsearch has more flexibility which adds some initial learning curve compared to simpler CRUD statements with MongoDB.

Case Studies: Notable Companies Using Elasticsearch and MongoDB

To highlight real-world adoption, here some examples of well-known companies using Elasticsearch and MongoDB:

Elasticsearch

Wikimedia – Hosts Elasticsearch on >300 servers to power search across Wikipedia content databases

Netflix – Uses Elasticsearch for managing metadata and assets when developing original content

Cisco – Leverages text analytics and data visualizations powered by an Elasticsearch backend

MongoDB

Expedia – Uses MongoDB as its standard database across thousands of globally distributed services

The Guardian – Stores over 2 billion documents and manages heavy read loads via MongoDB multi-datacenter configuration

Forbes – Architected its content management system and main web properties using microservices with MongoDB

There are thousands more global companies relying on these highly scalable NoSQL document stores as core parts of their technology stacks. Their production adoption demonstrates the power and maturity of both Elasticsearch and MongoDB.

Which Is Easier to Learn Overall?

For developers who are new to NoSQL databases, MongoDB tends to have a gentler initial learning curve. Its document schema reads similarly to JSON while the query language uses JavaScript-like syntax for simple CRUD operations.

Elasticsearch utilizes more complex DSL structure for search and analysis queries which can pose a steeper ramp-up beyond basic keyword lookups. However, development teams specifically needing search analytics functionality will gain that expertise over time.

So MongoDB enables faster onboarding for brand new users, while Elasticsearch unlocks deeper search functionality once query skills are developed. Both ecosystems have rich documentation and cloud-hosted options to smooth onboarding.

Summary: Choosing Between Elasticsearch and MongoDB

As highlighted in this guide‘s extensive feature analysis and benchmarks, Elasticsearch outperforms MongoDB for text search and analytics use cases while MongoDB excels at general purpose database workloads.

Choose Elasticsearch when your applications need to enable powerful search experiences, real-time data insights or text analysis. It‘s lightning fast queries, scalable indexes and time-series data handling are ideal for these advanced analytic workloads.

Opt for MongoDB when you simply require a flexible, high performance operational database. It‘s dynamic schemas, mature replication capabilities and tunable consistency model serve incredibly well across cloud-native applications.

For organizations with combined needs, it‘s absolutely possible to deploy Elasticsearch and MongoDB together on infrastructure like containers and leverage their respective strengths.

We hope this comprehensive feature comparison has provided clarity to navigate the differences between Elasticsearch and MongoDB. Leverage the key takeaways and best practices highlighted here to make the optimal database decision for your next project.