DynamoDB vs DocumentDB vs MongoDB
NoSQL databases, short for “Non-SQL” or “Not Only SQL,” emerged in the early 2000’s to address the limitations of traditional relational databases. They offer flexibility, scalability, and performance for modern applications, especially in handling large-scale, unstructured, or semi-structured data.
Advantages of NoSQL Databases
If you are an architect or developer, chances are you’re already pretty familiar with NoSQL databases. But for those of you who are newer to the concept, there are several types of NoSQL databases:
- Key-Value Stores: Store data as key-value pairs. Example: DynamoDB.
- Document Stores: Store data in document formats like JSON or BSON. Examples: MongoDB and DocumentDB.
- Column Stores: Organize data into columns rather than rows.
- Graph Databases: Designed for data with complex relationships.
DynamoDB vs DocumentDB vs MongoDB
In order to help you choose which one to use in your AWS hosted application, let’s compare three popular NoSQL document databases: Amazon DynamoDB, Amazon DocumentDB, and MongoDB.
Amazon DynamoDB
Overview: Amazon DynamoDB is a fully managed, serverless, key-value, NoSQL database service known for its high performance and scalability. It offers single-digit millisecond latency for simple operations like GetItem and PutItem. With in-memory caching, latency can drop to microseconds, making it suitable for applications requiring real-time data access.
Key Features:
- Data Model: Supports key-value data models.
- Scalability: Automatically scales to handle large amounts of read and write throughput.
- Consistency: Offers both eventual and strong consistency for read operations.
- Security: Integrated with AWS Identity and Access Management (IAM) for access control.
- Pricing: Offers on-demand and provisioned capacity modes.
Strengths:
- High Throughput: DynamoDB can handle over 10 trillion requests per day, with peak throughput rates exceeding 20 million requests per second.
- Scalability: It automatically scales to accommodate varying workloads, ensuring consistent performance during traffic spikes.
- Low Latency: Designed for applications that require fast and predictable response times.
Considerations:
- Complex Queries: DynamoDB’s query capabilities are limited compared to MongoDB. Complex queries may require additional AWS services, increasing latency and cost.
- Data Model Constraints: Supports a key-value and document data model with a maximum item size of 400KB, which may not be suitable for all use cases.
Best Suited For:
- Applications requiring low-latency data access.
- Serverless architectures.
- Integration with other AWS services.
Amazon DocumentDB
Overview: Amazon DocumentDB is a fully managed NoSQL database service designed to be compatible with MongoDB workloads. It is built to handle JSON data models and is optimized for performance and scalability.
Key Features:
- Data Model: Stores data in JSON format, similar to MongoDB.
- Scalability: Automatically scales storage up to 64 TiB.
- Availability: Replicates data across multiple availability zones for high availability.
- Compatibility: Supports MongoDB APIs, making migration from MongoDB straightforward.
- Pricing: Charges based on instance hours, I/O requests, and storage.
Strengths:
- Read Performance: DocumentDB outperforms MongoDB in read-heavy workloads, achieving a throughput of 1.4 million read operations per second compared to MongoDB’s 900,000.
- Low Latency: Optimized storage layer and caching mechanisms enable lower latency, beneficial for real-time applications.
- High Availability: Data is automatically replicated across multiple availability zones, ensuring 99.99% availability.
Considerations:
- Write Performance: DocumentDB’s write performance may decrease as data volume increases, making it less suitable for write-heavy workloads.
- Compatibility Limitations: While DocumentDB supports MongoDB APIs, it lacks full compatibility, which may require application modifications.
Best Suited For:
- Organizations already using MongoDB looking to migrate to AWS.
- Applications requiring high availability and scalability.
MongoDB
Overview: MongoDB is a popular open-source NoSQL database that stores data in flexible, JSON-like documents. It is known for its rich query language and ease of use.
Key Features:
- Data Model: Stores data in BSON (Binary JSON) format.
- Querying: Supports ad-hoc queries, indexing, and real-time aggregation.
- Scalability: Supports horizontal scaling through sharding.
- Deployment: Can be deployed on-premises or in the cloud using MongoDB Atlas.
- Licensing: Available under the Server Side Public License (SSPL).
Strengths:
- Complex Queries: Supports a rich query language with complex aggregations, making it suitable for applications with flexible, semi-structured data.
- Horizontal Scalability: Utilizes sharding to distribute data across multiple servers, improving performance for large-scale applications.
- Indexing: Offers robust indexing options, including compound, unique, and geospatial indexes, to optimize query performance.
Considerations:
- Latency Variability: Performance can vary depending on query complexity, server cluster health, and dataset size.
- Operational Complexity: Managing a MongoDB cluster can be more complex compared to fully managed services like DocumentDB.
Best Suited For:
- Developers seeking flexibility and control over their database.
- Applications requiring complex queries and aggregations.
- Teams preferring an open-source solution.
Feature Comparison
| Feature | DynamoDB | DocumentDB | MongoDB |
| Data Model | Key-value, Document | Document (MongoDB-compatible) | Document (BSON) |
| Scalability | Auto-scaling | Auto-scaling | Horizontal scaling (sharding) |
| Consistency | Eventual and strong consistency | Eventual consistency | Eventual consistency |
| Querying | Limited querying capabilities | MongoDB-compatible queries | Rich query language and aggregation |
| Security | Integrated with AWS IAM | Integrated with AWS IAM | Self-managed or via MongoDB Atlas |
| Deployment | AWS Cloud | AWS Cloud | On-premises or MongoDB Atlas |
| Pricing | On-demand and provisioned capacity | Instance hours, I/O, storage | Free (Community Edition), Paid (Atlas) |
| Database | Strengths | Considerations |
| DynamoDB | High throughput, low latency, fully managed, automatic scaling | Limited query capabilities, data model constraints, requires additional AWS services for complex queries |
| DocumentDB | Optimized for read-heavy workloads, low latency, high availability, MongoDB API compatibility | Write performance may decrease with data volume, partial MongoDB compatibility |
| MongoDB | Rich query language, complex aggregations, horizontal scalability, flexible data model | Performance variability, operational complexity, requires careful cluster management |
Which should you use?
The choice between DynamoDB, DocumentDB, and MongoDB depends on your application’s specific requirements:
- Choose DynamoDB if you need a fully managed, high-performance database with low latency and can work within its query and data model constraints.
- Choose DocumentDB if you’re migrating from MongoDB and require a fully managed service with high read performance and low latency, but can accommodate its write performance limitations.
- Choose MongoDB if your application demands complex queries, aggregations, and a flexible data model, and you’re prepared to manage the operational aspects of the database.
Each database has its strengths and is suited to different use cases. Assess your application’s needs to make an informed decision.
References
- AWS NoSQL Databases Overview
- MongoDB NoSQL Explained
- Integrate.io: DynamoDB vs MongoDB
- Kinsta: DynamoDB vs MongoDB
- Logicata: DynamoDB vs MongoDB
As always, please leave a comment below or send me a message to connect!
