Cassandra Vs MongoDB - Top Differences You Must Know
Overview
In this modern era, data is of utmost importance. Mobile phones, wireless systems, etc., generate loads of data daily, and this must be managed and organized. Database management systems are used to do this. There are various DBMSs present nowadays.
What is Cassandra?
Cassandra is a free, open-source distributed database management system that can handle enormous amounts of structured and semi-structured data on a variety of commodity servers while maintaining high availability and eliminating single points of failure. It was created initially by Facebook and then made available as an Apache Software Foundation project.
As a NoSQL database, Cassandra does not make use of the conventional relational data model. Data is instead stored as key-value pairs, where each key denotes a collection of values. This makes it especially well-suited for applications that demand high performance and availability since it enables flexible and scalable data storage.
What is MongoDB?
Open-source MongoDB is a document-oriented NoSQL database that is made to manage and store unstructured and partially structured data. It was created by MongoDB Inc.
MongoDB is more adaptable and scalable than conventional relational databases because it stores data in documents that resemble JSON and have dynamic schemas. MongoDB is built on a distributed architecture that allows it to scale horizontally across multiple servers, enabling high availability and fault tolerance.
Cassandra Vs MongoDB: Key Difference
The main difference between Cassandra and Mongodb is Cassandra and MongoDB offer distinct advantages depending on specific project requirements. Cassandra excels in scenarios where scalability and fast write performance are paramount. MongoDB, on the other hand, prioritizes consistency and is a solid choice when strict accessibility is crucial. MongoDB’s schema-free document-oriented architecture makes it an ideal choice for projects requiring a richer data model, providing flexibility in organizing data hierarchies. While both databases lack strict schemas, MongoDB stands out due to its enhanced flexibility. Both support multiple programming languages, but Cassandra effectively fulfills query language needs through its native CQL. MongoDB supports aggregation, whereas Cassandra does not. In terms of index usage, Cassandra suits primary indexes, while MongoDB shines with secondary indexes. The choice between Cassandra and MongoDB ultimately hinges on your specific use case and priorities.
Cassandra Vs MongoDB: Comparative Analysis
Let us see a comparative analysis of Cassandra and MongoDB:
| Cassandra | MongoDB |
|---|---|
| Developed by Apache Foundation | Developed by MongoDB Inc. |
| Developed using Java | Developed using C++ |
| Limited support for secondary indexes | Supports secondary indexes |
| Peer-to-peer architecture | Master-slave architecture |
| No single point of failure | Single point of failure exists |
| AP (Highly Available and Partition Tolerant) | CP (Consistency and Partition Tolerant) |
| Data stored in Tabular or Wide-Column form | Data stored in Binary JSON (BSON) |
| Selectable replication factor | Yes, Master-slave replication |
| No aggregation | Built-in aggregation |
| Because numerous masters can accept requests simultaneously, writes are lightning-fast, while complicated reads take longer. | Reads very quickly but writes slowly because of the master-slave design. |
| Dynamic schema (semi-structured data) | Schema-free (flexible data structure) |
| Relatively simple data modeling | High complexity for data modeling |
| Uses Cassandra Query Language(CQL), similar to SQL. | Uses MongoDB Query Language(MQL). |
| Companies like Hulu, Instagram, Intuit, Netflix, Reddit, etc., use Cassandra. | Companies like Adobe, Amadeus, Lyft, ViaVarejo, Craftbase, etc., use MongoDB. |
Advantages of Cassandra
- Cassandra is made to be extremely scalable and is capable of handling massive volumes of data over several servers, allowing it to scale linearly with no downtime.
- Cassandra guarantees high availability and fault tolerance even in the event of node failures thanks to its distributed architecture and replication across several nodes.
- Cassandra is a good choice for applications that need low-latency data access because of its architecture, which is intended for fast read-and-write performance.
- Cassandra's data model enables dynamic and flexible data storage, making it suitable for applications that need semi-structured or schema-less data.
- Cassandra offers configurable consistency levels that let you control the availability/consistency trade-off depending on your application needs.
- Cassandra is relatively easy to manage compared to other distributed databases.
- Cassandra is an open-source project that offers an affordable substitute for expensive database products.
Drawbacks of Cassandra
- Cassandra's distributed architecture can be complex to set up and manage.
- Unlike SQL, Cassandra’s query language does not offer joins and subqueries.
- Cassandra does not fully comply with ACID(atomicity, consistency, isolation, and durability).
- Cassandra's flexible data model can also make data modeling complex, requiring careful consideration of data access patterns and use cases.
- The same information is kept more than once because data is modeled around queries rather than structure.
- Reading tends to be slower because Cassandra was designed from the beginning for quick writing.
Advantages of MongoDB
- Because MongoDB is made to scale horizontally across several servers, it can manage high data and traffic volumes.
- MongoDB supports advanced indexing and querying features, including support for geospatial and text search.
- Having high availability and fault tolerance is made possible by MongoDB's distributed design and automatic failover.
- The dynamic schema of MongoDB enables scalable and adaptable data storage.
- Application objects need not be mapped or transformed into database objects.
- As a result of its master-slave architecture, consistency is built in.
Drawbacks of MongoDB
- It does not provide full ACID compliance, and there is no support for transactions.
- MongoDB takes up more storage as compared with other databases.
- MongoDB does not support join.
- You need to ensure that indexes are implemented appropriately or in the proper order for desired performance.
Cassandra Vs MongoDB: Code Syntax
Code Syntax for Cassandra:
Code Syntax for MongoDB:
Cassandra Vs MongoDB: Top Use Cases
It is important to know about the difference in use cases for Cassandra and MongoDB.
For write-intensive tasks where data is anticipated to be constantly added but sparingly changed, Cassandra is ideal. These could include time-series data, inventory tracking, web analytics, transaction logging in the banking and finance industry, etc.
MongoDB outperforms Cassandra when working with big data and for real-time analytics. A single view of the data can be obtained by pulling data into a central database using the built-in aggregation capability. MongoDB finds its use in IoT, content management systems, mobile apps, etc.
Cassandra Vs MongoDB: Which One to Choose?
After reading the article, the question arises which one of the DBMS should you choose?
The decision to use Cassandra or MongoDB wholly depends upon the needs of the project. Cassandra is a good option where there is a need for scaling regularly, and maintenance and setup are to be done easily.
On the other hand, MongoDB comes in handy where real-time analytics, scaling, and caching are required. Mobile applications and content management are a few examples.
Similarities Between MongoDB and Cassandra
- Both of these are NoSQL databases.
- Both of these are open source.
- Both of these are non-compliant with ACID.
- Data replication, as well as sharding, is supported by both of these databases.
- Neither database can take the place of RDBMS.
- Both these databases can be scaled horizontally. However, the method is different in both of them.
Conclusion
In this Cassandra vs MongoDB tutorial we have covered the following points:
- Cassandra and MongoDB are both open-sourced NoSQL DBMSs.
- Cassandra uses CQL, whereas MongoDB uses MQL.
- Cassandra is optimized for write-heavy workloads and offers strong consistency.
- MongoDB is optimized for read-heavy workloads and offers eventual consistency.
- Which database should be chosen is strictly based upon the needs of the project.
FAQs
Q. What are the key differences between Cassandra and MongoDB?
A. Cassandra is a column-family store NoSQL database, while MongoDB is a document-oriented NoSQL database. Cassandra is optimized for write-heavy workloads and offers strong consistency, while MongoDB is optimized for read-heavy workloads and offers eventual consistency. MongoDB provides built-in aggregation, but Cassandra does not.
Q. Which database is more scalable, Cassandra or MongoDB?
A. Both Cassandra and MongoDB are designed to scale horizontally across multiple servers, making them highly scalable. However, Cassandra's automatic partitioning and replication make it more efficient at handling large volumes of data and traffic than MongoDB.
Q. When is it appropriate to go from Cassandra to MongoDB?
A. When a schema-free design is required, both NoSQL databases MongoDB and Cassandra make excellent candidates. MongoDB, as opposed to Apache Cassandra, a columnar database, is a preferred option if a document-centric database is required. The use of MongoDB rather than Cassandra is further encouraged by the need for consistency, secondary indexes, aggregation, and a rich data schema.