Close-to-the-metal architecture handles millions of OPS with predictable single-digit millisecond latencies.
Learn MoreHow Do the Features of MongoDB vs Cassandra Database Management Systems Compare?
MongoDB stores data in a flexible document-based JSON-like data model. It works well for applications with complex querying needs and dynamic schemas with evolving data structures.
Cassandra stores data in a column-based data model. It uses Cassandra query language (CQL) which is similar to SQL. Cassandra offers high availability, scalability, and faster writes, making it suitable for time-series data, IoT data, and similar applications.
Cassandra vs MongoDB Performance
At a small scale, MongoDB can provide high performance for read and write operations, especially when properly indexed. It also offers tunable strong and eventual consistency options. MongoDB’s replica sets deliver high availability.
Cassandra is optimized for write-heavy workloads and can sustain high throughput for both reads and writes, particularly in distributed setups. It offers eventual consistency by default, but is also tunable.
Cassandra vs MongoDB Latency
MongoDB supports ad-hoc queries, secondary indexes, and aggregation pipelines. It typically offers lower latency for read-heavy workloads than for write-heavy ones.
Cassandra is designed for high write throughput and can handle low-latency writes, especially in environments that distribute data. However, because it primarily supports key-based lookups and range queries, its querying capability is more limited.
Cassandra vs MongoDB Scalability
In terms of MongoDB vs Cassandra scalability, the former scales vertically. This allows users to add more resources such as RAM or CPUs to a single server.
In contrast, Cassandra scales horizontally. This allows users to add more nodes to a cluster so it can expand outward from the primary node as data volume increases. It does not scale linearly though.
Cassandra vs MongoDB Pricing
MongoDB offers tiered pricing models. These include a free option for small-scale deployments and a subscription-based enterprise option with more features and support.
Apache Cassandra is open-source software from the Apache software foundation, which reduces the initial implementation price. However, there are costs for hardware, maintenance, and support, particularly as the cluster grows.
Cassandra vs DynamoDB vs MongoDB
To compare Cassandra and MongoDB to DynamoDB, another common choice, consider their basic features:
As mentioned, Cassandra offers a wide-column-family based model and MongoDB offers a document-based model, while DynamoDB offers two different key-value and document-based models.
Cassandra offers horizontal scaling, while MongoDB scales vertically by adding more resources to a single server or horizontally via sharding. However, DynamoDB is a fully-managed service with scalability controlled by AWS.
In terms of consistency, Cassandra is eventually consistent by default, with tunable consistency levels. MongoDB offers tunable consistency, allowing a choice between strong and eventual consistency. DynamoDB is eventually consistent by default.
Querying options are also different for these platforms, with DynamoDB supporting queries by primary key, secondary indexes, and conditional queries. Cassandra supports more limited querying capabilities including key-based lookups and range queries, while MongoDB’s capabilities are richer, including ad-hoc queries, indexes, and aggregation pipelines.
Deployment is most intensive for Cassandra, which requires manual setup and management, but can then be deployed either on-premises or in the cloud, assuming the user has the capability to do it. MongoDB offers a choice: self-hosting or a fully managed option through Atlas, a cloud database service. DynamoDB offers only the fully managed AWS service, with automated provisioning, scaling, and maintenance.
As explained above, Cassandra is best-suited for write-heavy workloads such as time-series data and messaging systems while MongoDB is often used in e-commerce platforms and social networks. In contrast, DynamoDB is used for applications with predictable workloads, requiring low-latency access to most basic types of data, and seamless scalability. It is commonly used in web and mobile applications.
Other NoSQL Databases to Consider Based on the Use Case
There are several other NoSQL databases worth considering based on the specific use case:
ScyllaDB
ScyllaDB is a common alternative to Cassandra, MongoDB, and DynamoDB among organizations with use cases that require ultra-low latency, even with workloads exceeding 1M ops/sec. It’s commonly used for social media, media streaming, IoT, fraud detection, AI/ML, and personalization use cases. Compared to DynamoDB, ScyllaDB achieves 40x lower cost, 4x better latency, and single-digital ms latencies with no external cache. Compared to MongoDB, ScyllaDB achieves 20x higher throughput, 68x lower P99 latencies, 19x better price-performance. Compared to Cassandra, ScyllaDB achieves 1/8 the cost, 8x better latency, 5x throughput.
Apache HBase
This column-oriented database built on Hadoop provides low-latency random read/write access for big data analytics and real-time data processing.
Redis
This in-memory data store supports strings, hashes, tokens, and sorted sets for live-streaming, messaging, and high-speed transactions.
Neo4j
This database specializes in querying graph data structures, suiting it for applications with complex relationships and network analysis. It is commonly used in social networks, recommendation systems, fraud detection, and network management.