MongoDB vs Elasticsearch
MongoDB for primary application databases with ACID transactions. Elasticsearch for full-text search and analytics. Many organizations use both… See pricing, features & verdict.
Quick Comparison
| Feature | MongoDB | Elasticsearch |
|---|---|---|
| Primary Use | Application database | Search & analytics |
| Transactions | Multi-document ACID | None |
| Search | Atlas Search | Best-in-class |
| Consistency | Strong | Eventual |
| License | SSPL | AGPL 3.0 |
MongoDB
- Primary Use:
- Application database
- Transactions:
- Multi-document ACID
- Search:
- Atlas Search
- Consistency:
- Strong
- License:
- SSPL
Elasticsearch
- Primary Use:
- Search & analytics
- Transactions:
- None
- Search:
- Best-in-class
- Consistency:
- Eventual
- License:
- AGPL 3.0
Interface Preview
MongoDB
Elasticsearch
Feature Comparison
| Feature | MongoDB | Elasticsearch |
|---|---|---|
| Core Capabilities | ||
| Full-text Search | 3 | 5 |
| ACID Transactions | 5 | 1 |
| Aggregations | 3 | 5 |
| Real-time Analytics | 2 | 5 |
| Document CRUD | 5 | 3 |
| Operations | ||
| Horizontal Scaling | 5 | 4 |
| Managed Cloud | 5 | 4 |
| Schema Flexibility | 5 | 3 |
| Vector Search | 4 | 4 |
| Ecosystem | 5 | 5 |
Core Capabilities
Full-text Search
ACID Transactions
Aggregations
Real-time Analytics
Document CRUD
Operations
Horizontal Scaling
Managed Cloud
Schema Flexibility
Vector Search
Ecosystem
Legend:
Our Verdict
MongoDB for primary application databases with ACID transactions. Elasticsearch for full-text search and analytics. Many organizations use both together — MongoDB as system of record, Elasticsearch as search layer.
💡 This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Frequently Asked Questions
Which tool is better suited for primary application databases requiring ACID transactions?
MongoDB is ideal for primary application databases due to its support for multi-document ACID transactions and strong consistency. Elasticsearch lacks transaction capabilities, making it unsuitable for systems requiring strict data integrity in write operations.
How do MongoDB and Elasticsearch differ in search capabilities for a data warehouse?
Elasticsearch excels in full-text search and analytics with best-in-class search features, while MongoDB offers Atlas Search for basic querying. For complex search use cases in a data warehouse, Elasticsearch is typically preferred over MongoDB's more limited search capabilities.
What licensing considerations should be evaluated when choosing between MongoDB and Elasticsearch?
MongoDB uses the SSPL license, which may impact cloud deployment options, while Elasticsearch uses AGPL 3.0. Both licenses require careful evaluation for compliance, especially in enterprise environments where open-source usage policies are critical.
Can MongoDB and Elasticsearch be used together in a data warehouse architecture?
Yes, many organizations use MongoDB as the system of record for transactional data and Elasticsearch as the search layer for analytics and full-text queries. This combination leverages MongoDB's consistency and Elasticsearch's search strengths for comprehensive data warehouse solutions.