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Learn MoreWhat are the Basic Differences Between Amazon DynamoDB vs MongoDB?
DynamoDB vs MongoDB are NoSQL databases with different design elements, architectures, and optimal use cases:
DynamoDB
DynamoDB is a fully managed service. The multi-region Amazon web services (AWS) platform offers widespread scaling capabilities and high availability across Amazon’s infrastructure.
AWS handles the operational aspects of DynamoDB’s database management systems such as hardware provisioning, configuration, software patching, and scaling, making it suitable for users who want a turnkey solution with minimal administrative overhead.
DynamoDB supports simple and complex data types, from strings and numbers to sets, lists, and maps. The overall data model organizes all information into tables, each with a primary key, to sustain complex queries.
Amazon DynamoDB uses its own query language, but this limits support for complex queries and indexing.
In terms of consistency, Amazon DynamoDB offers both eventual and strong consistency models. DynamoDB provides eventual consistency for writes by default, and strongly consistent reads can be requested.
Amazon DynamoDB uses primary keys (partition key and optional sort key) for indexing data. It additionally supports global secondary indexes (GSI) and Local Secondary Indexes (LSI). GSIs allow querying on non-primary key attributes and provide eventual consistency. LSIs support additional sort keys and offer strong consistency.
DynamoDB scales horizontally across multiple nodes, on demand. Its pay-per-use model is often more cost-effective for workloads with predictable usage patterns.
Amazon DynamoDB supports a wide range of programming languages, including Java, JavaScript (Node.js), Python, Ruby, PHP, .NET, Go, and more.
Amazon DynamoDB performance for reads and writes depends in part on its two eventual and strong read consistency models. Strongly consistent reads are typically slower than eventually consistent reads, and the database itself is designed for high-speed write operations, particularly with the use of provisioned or on-demand throughput settings.
DynamoDB performance is optimized for high-throughput and low-latency, but the ability to select primary keys and partition effectively can impact outcomes.
MongoDB
MongoDB stores data in flexible, JSON-like documents that form complex, nested structures. MongoDB is suitable for users who need more control over their database configurations and deployments and is ideal for content management, catalogs, analytics, mobile apps, and applications requiring flexible data modeling.
Indexing in MongoDB supports single field, compound, multi-key, text, geospatial, hashed, and wildcard options. The rich MongoDB Query Language (MQL) supports complex queries, text search, and geospatial queries.
MongoDB ensures strong consistency for reads and writes within a replica set by default. It supports replica sets for fault tolerance, and eventual consistency for secondary replicas.
MongoDB scales horizontally through sharding, and can handle large-scale data this way, but shards demand manual configuration and management.
Comparing DynamoDB vs MongoDB pricing depends in large part on whether the deployment is self-hosted or a managed service like MongoDB Atlas.
MongoDB is available as an open-source, self-hosted database under the Server Side Public License (SSPL). MongoDB Atlas offers pay-as-you-go pricing based on instance size, storage, and data transfer; additional security features; and integrated access controls.
MongoDB supports Java, Python, Ruby, C++, Go, Rust, and other programming languages.
MongoDB design can support strong consistency in both read and write operations. Write operations are designed to be high-speed and handle large volumes of data, while read performance can be optimized with appropriate indexing and replica set configurations.
MongoDB scales horizontally through sharding, which can distribute read/write loads across multiple nodes.
How Do MongoDB vs DynamoDB Compare for Real-Time Applications?
Why use DynamoDB vs MongoDB for processing real-time data?
Amazon DynamoDB strengths include:
- Low latency. DynamoDB is optimized for high-speed read and write operations
- Scalability. Automatically handles large volumes of data and high request rates, ensuring consistent performance
- DynamoDB Streams. These capture modifications to data in real-time, which can be processed using AWS Lambda for event-driven architectures.
Potential weaknesses include:
- Complexity. Real-time processing often requires integration with other AWS services (such as Lambda, Kinesis) to build complete solutions.
- High throughput costs. These can rise in tandem with requirements, and provisioning needs careful management to avoid throttling.
MongoDB strengths include:
- Change Streams. These monitor real-time changes in the data, suiting MongoDB for real-time data processing and reactive applications.
- Flexibility. Schema-less design allows for easy adaptation to changing data structures and varying formats. Document-oriented data model (BSON/JSON) fits naturally with content management systems, as content can be stored in rich, hierarchical documents.
- Aggregation pipeline. Supports powerful real-time analytics using the aggregation pipeline, which can process and transform data as it’s ingested.
Potential weaknesses include:
- Resource management. Handling high volumes of real-time data might require careful sharding and indexing to maintain performance.
- Operational overhead. If self-hosted, managing and scaling infrastructure to handle real-time workloads can require significant operational effort.
Pros and Cons of MongoDB vs DynamoDB
Here are the various advantages of DynamoDB vs MongoDB in terms of performance benchmarks, structure/design and how it affects performance, and cost:
DynamoDB vs MongoDB benchmarks
Amazon DynamoDB is optimized for high-speed read and write operations with consistent, low response times, offering high throughput and low latency. However, its performance relies heavily on primary key schema design; poor design causes hot partitions and throttling. Amazon DynamoDB may therefore be efficient for key-value access patterns, but perform less effectively for complex queries and be more difficult to implement compared to MongoDB.
DynamoDB vs MongoDB performance and structure
MongoDB’s flexible document-oriented format allows for storing and querying complex, nested data structures, which can lead to more intuitive data modeling. This supports complex queries, aggregations, and indexing, enabling real-time analytics directly within the database.
However, write-heavy workloads may experience slower performance due to the need to maintain data consistency across replica sets. And scaling with sharding in MongoDB demands careful planning and management to avoid performance bottlenecks.
DynamoDB vs MongoDB cost
The cost of high throughput performance in Amazon DynamoDB can escalate rapidly in high-traffic applications based on read/write capacity units.
Other DynamoDB vs MongoDB Comparisons
Here is how DynamoDB vs MongoDB compare to a few other popular alternatives in this space:
DocumentDB vs DynamoDB vs MongoDB
DynamoDB and DocumentDB are both offerings from Amazon Web Services; here is how they compare to MongoDB:
- DynamoDB is ideal for high-throughput, low-latency needs within the AWS ecosystem
- DocumentDB is better suited for users who need managed MongoDB compatibility
- MongoDB offers the most flexibility and features for complex applications, but typically demands the most management effort of the three
DynamoDB vs Cassandra vs MongoDB
This choice is focused on scalability requirements, performance needs, query complexity, and operational preferences:
- Cassandra excels in high-write, large-scale environments
- DynamoDB offers seamless managed service with high throughput
- MongoDB delivers rich functionality and flexibility for complex data handling
MongoDB vs DynamoDB vs Firebase
Firebase offers a different approach to data management compared to NoSQL databases like DynamoDB and MongoDB:
- Firebase is focused on real-time data synchronization, simplicity, and integration with mobile and web applications
- Firestore is a NoSQL document database managed by Google Cloud Platform (GCP) and used for real-time data synchronization, mobile and web applications, and rapid development
- The Firestore data model is document-oriented, and supports strings, numbers, booleans, arrays, maps, timestamps, and other data types
- Firestore offers high throughput with automatic scaling and millisecond latency for reads and writes
- The Firebase Realtime Database offers low latency for real-time synchronization and uses a schema-less JSON tree structure and JSON objects
MongoDB vs DynamoDB vs ScyllaDB
ScyllaDB offers improved performance at scale vs MongoDB and lower costs than DynamoDB – without throttling.
- MongoDB is a document-oriented NoSQL database, while DynamoDB is a key-value and document database, and ScyllaDB is a wide-column NoSQL database
- MongoDB offers flexible schema design and rich query language, DynamoDB provides seamless scalability with AWS integration, and ScyllaDB focuses on high throughput and predictable low latency
- MongoDB uses BSON (Binary JSON) for data storage, DynamoDB uses tables with primary keys, and ScyllaDB offers a DynamoDB compatible API in addition to supporting CQL (Cassandra Query Language)
- MongoDB offers strong consistency by default with tunable consistency levels, DynamoDB provides eventual consistency by default with an option for strong consistency, and ScyllaDB offers tunable consistency levels
- MongoDB is open-source and can be self-hosted or used as a managed service, DynamoDB is a proprietary AWS service, and ScyllaDB is an open-source NoSQL database with both self-hosted and managed options
- MongoDB excels in complex queries and aggregations, DynamoDB and ScyllaDB are optimized for high-throughput and low-latency operations; ScyllaDB is better optimized for write-heavy workloads
- In terms of scalability, MongoDB requires manual sharding, DynamoDB offers automatic, seamless scaling, and ScyllaDB provides automated sharding and rebalancing
