Introduction

Cassandra and MongoDB are among the most famous NoSQL databases used by large to small enterprises and can be relied upon for scalability. Cassandra was launched in late 2008, and after a year, MongoDB was also launched. Apart from the fact that both are open-source, there are multiple contrasting factors between them.

Let’s have a look at them one by one.

Structuring Of Data 

This data architecture supports flexible column addition and updates without needing schema modifications. Cassandra also supports collections, letting users save sophisticated data types such as lists, sets, and maps. Including these aspects would offer a complete picture of Cassandra’s data modelling versatility.

Cassandra is more like RDBMS if we talk about the way it stores information. It stores data structured in tables and follows columns based arrangement. However, in contrast to RDBMS, you can make columns and tables very quickly.

Also, every line in Cassandra doesn’t need to have a similar column. The database depends on the essential key to bring information. 

Whereas, MongoDB can be considered as an object-oriented database. It utilises BSON (Binary JSON) to store information. MongoDB can uphold different object structures, and even gives the option of creating nested structures.

If we compare with Cassandra, MongoDB is much more flexible as the user must not have a JSON schema. It also gives the option to deal with schemas if necessary in some cases.

Query Language

Cassandra utilises Cassandra Query Language (CQL) for getting the required data. CQL is fundamentally the same as SQL. CQL is very easy to learn for any data professional who is well acquainted with SQL.

MongoDB gives significantly better alternatives in this scenario majorly because it stores information in JSON-like records. Overseers can request MongoDB data via the Mongo shell, PHP, Perl, Python, Node.js, Java, Compass, and Ruby.

While comparing Cassandra vs MongoDB, don’t forget that MongoDB’s query language offers a comprehensive collection of operators and functions that are particularly built for JSON-like documents. These enhancements provide greater functionality and eloquent queries, allowing developers to alter and retrieve information contained in MongoDB more easily.

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Secondary Indexes

Secondary Indexes are valuable for getting the data that is primarily a non-key attribute. Cassandra doesn’t ultimately uphold secondary indexes. It depends on the primary keys to fetch data. 

MongoDB leans towards indexes for fetching the required data. The compatibility with secondary indexes helps MongoDB in improving the inquiry speeds.

It is conceivable to inquiry any property of an item, including nested objects, that too with few moments.

Cassandra does not intrinsically give secondary indexes, it does offer an alternative known as “materialized views”. By building indexes on individual columns, materialized views encourage the denormalization of information and fast querying of non-key properties. All this additional data would help distinguish the indexing features of both databases.

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Scalability

To enhances the write-scalability of system, Cassandra enables the administrator to have multiple master nodes. An administrator can define the total number of nodes that will be required in a cluster. One can judge the level of scalability of a database on the number of full nodes.

On the other hand, MongoDB has enabled only one master node. Rest of the nodes act as slaves within the cluster. Even though data will be defined in the master node, the slave nodes are configured as read-only.

The scalability of MongoDB gets hit as compared to Cassandra, primarily because of this master-slave architecture. One can enhance the scalability of MongoDB through sharding techniques. Learn about Cassandra vs hadoop.

The underlying contrasting feature in the interpretation process between the two is the way they handle fault tolerance. Cassandra can define a cluster even when a particular node fails because it allows multiple masters.

On the other hand, MongoDB forces the administrator to wait 10 to 40 seconds if a node fails and wants to enter some information.

It is because of the single master behavior of MongoDB. All in all, Cassandra is much better than MongoDB in terms of availability.

It is crucial to note that while MongoDB defaults to a master-slave design, it also allows sharding. Sharding shares data over numerous servers, enabling horizontal scalability, bigger collections of data, and higher performance. This feature demonstrates how, despite maintaining a single master node, MongoDB can achieve significant scalability using sharding.

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Aggregation

To run complex queries, most of the users use aggregation these days. In Cassandra, there is no built-in support for any aggregation system. Most of the users have to find a workaround for utilising the benefits of aggregation.

For this, multiple third-party tools are used by administrators like Hadoop and Spark.

Unlike Cassandra, MongoDB comes packed with an aggregation framework. To aggregate stored data, it can make use of the ETL pipeline and give out results.

Although it is an easy way to do this, the built-in aggregation method runs only on low to medium traffic. Hence, it becomes challenging to scale as the aggregation framework keeps on becoming complex.

Cassandra supports complicated aggregations utilizing third-party tools like Apache Spark or Apache Flink. These technologies work well with Cassandra and offer substantial backing for the large-scale processing of information and analytics. This knowledge contributes to a deeper comprehension of Cassandra’s aggregation features.

Performance

Performance evaluation of these requires the analysis of a lot of factors. Everything is taken into account from the type of schema you use (which directly affects the query speeds) to the input and output load characteristics (responsible for a database’s performance). 

Cassandra was a clear winner in write-oriented operations as per a 2018 benchmark report on Cassandra vs MongoDB.

Licensing

Licensing is not a significant issue in both databases as they open as open-source, free software. If someone wants to opt for enterprise-grade Cassandra, third-party vendors like Datastax offer individual plans. Whereas, its namesake software company usually overlooks MongoDB.

Subscription plans are available for both of them at different levels. On top of it, anyone can also use AWS to host the database on public clouds as AWS gives built-in support. Learn More About Top 5 Big Data Tools 

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Conclusion

Organisations are continuously searching for new and creative innovations to chip away at, and databases like MongoDB and Cassandra is one of them. These new-age abilities are valuable to flourish in a competitive environment where needs change with a new technology’s arrival.

It is important to mention that while comparing the difference between Cassandra and MongoDB, both are emerging databases with merits and shortcomings. Mentioning that organizations should carefully analyze their specific needs and consider variables such as data modelling demands, query patterns, adaptability, fault tolerance, and environment interoperability would offer readers a well-rounded view.

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