Both Cassandra and MongoDB are NoSQL databases that offer enterprises reliable scalability for modern data needs. The two database systems have similar launch periods. Cassandra came into being in 2008 and MongoDB followed, a year later. They are both open-source with a sizeable community for support. But that's where the similarity ends between the two. Here is a comprehensive look at Cassandra vs MongoDB, and how the two databases stack up against each other.
Cassandra vs MongoDB: Key Differences
- Cassandra uses columns and tables to store data, while MongoDB stores data in JSON-like documents
- Cassandra does not completely support secondary indexes, while MongoDB mostly relies on indexes for fetching data
- Cassandra has its own query language (CQL), while MongoDB supports popular third-party languages, such as Python and Java
- Cassandra relies on third-party tools for aggregation, while MongoDB has a built-in aggregation framework
- Cassandra uses a distributed architecture, which makes it highly available, while MongoDB relies on a master-slave architecture, which gives it lower fault tolerance
- Cassandra is more suited for people looking for MySQL style databases with more scalability, while MongoDB is best for storing unstructured data
Cassandra vs MongoDB: Everything You Need to Know
Both databases have high-profile customers. Behemoths such as Netflix, Instagram, and Hulu rely on Cassandra for their data storage needs. Similarly, giants such as Google, Adobe, and Paypal use MongoDB. However, the two databases differ in terms of how they store data, replication of data, and other functionalities. Here are some key differences between Cassandra and MongoDB.
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Table of Contents:
- Data Structure
- Secondary Indexes
- Query Language
Cassandra is closer to a relational database in terms of how it stores data. It is a column-oriented database that stores data in tables. However, unlike relational databases, you can create columns and tables on the fly. Moreover, every row in Cassandra does not need to have the same columns. The tabular database relies on the primary key to fetch data.
MongoDB, on the other hand, is an object-oriented database. It uses BSON (Binary JSON) to store data. MongoDB can support varied object structures, and you can even create nested object structures. Since you don't need a schema for JSON, MongoDB is much more flexible compared to Cassandra. However, you can create a schema in MongoDB, if needed.
Secondary indexes are useful for accessing data that is a non-key attribute. Cassandra does not fully support secondary indexes. It relies on primary keys to fetch information.
MongoDB prefers indexes for querying. It fully supports secondary indexes that can enhance query speeds. It is possible to query any property of an object, including nested objects, really quickly.
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Cassandra employs Cassandra Query Language (CQL) for fetching data. CQL is very similar to SQL. Database administrators who are familiar with SQL should find it very easy to pick up CQL.
MongoDB gives a lot more options in query languages since it stores data in JSON-like documents. Administrators can query MongoDB using the Mongo shell, PHP, Perl, Python, Node.js, Java, Compass, and Ruby.
Cassandra allows for multiple master nodes, which greatly enhances its write-scalability. You can specify the number of nodes you want in a cluster. The more the number of nodes, the more scalable your database is.
MongoDB only allows a single master node. All the other nodes in a cluster are slaves. While data is being written to the master node, you can only perform read operations on the slave nodes. Due to its master-slave architecture, MongoDB is not as scalable as Cassandra. However, you can improve the scalability of MongoDB through sharding techniques. Those might require some setting up, though.
The difference in how the two interpret master nodes also decide their fault tolerance. Since Cassandra allows multiple masters, you can write to a cluster even when a node fails. With MongoDB, you might have to wait 10 to 40 seconds for write operations in case of a node failure since it only allows a single master. In effect, Cassandra beats MongoDB when it comes to availability.
Aggregation allows you to run complex queries. Cassandra does not have an aggregation framework. Administrators need to use third-party tools such as Hadoop and Spark for aggregation.
MongoDB, in comparison, has a built-in aggregation framework. It can run an ETL pipeline to aggregate stored data and return results. However, the database's built-in aggregation is only efficient for medium traffic. As you scale, handling the aggregation framework becomes more complex.
A lot of factors go into how a database performs. For example, the kind of schema you use plays a pivotal role in query speeds. Similarly, input and output load characteristics influence the performance of a database. According to a 2018 benchmark report comparing Cassandra vs MongoDB, Cassandra shines in write-intensive operations.
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Both the databases are available as open-source, free software. Third-party vendors, such as Datastax, offer enterprise-grade Cassandra. MongoDB, on the other hand, is overseen by its namesake software company. Both of them are available on subscription models in different tiers, starting from basic to more advanced. You can also find Cassandra and MongoDB in the AWS marketplace and host them on public clouds.
Both the databases have their pros and cons. The database that you should choose depends on your priorities. In terms of availability, Cassandra has the upper hand. It's highly distributed architecture means you can continue writing to a cluster even when nodes fail. MongoDB, on the other hand, is great for storing unstructured data. The schema-free architecture makes it well-suited for high-speed caching and logging. Real-time analytics and streaming applications rely on high-speed caching and logging operations. MongoDB is also great for fast query times since it supports secondary indexes. If you are expecting your data operations to scale rapidly, though, Cassandra will be a better fit.
Whatever the Database, Xplenty Can Help
Whichever database you pick for your use case, Xplenty can quickly integrate it with your other data sources for fast data analysis. Our drag-and-drop interface lets you build ETL pipelines within minutes, without any complex coding involved. See for yourself how we can make data integration such a breeze. Contact us to book your demo and experience Xplenty for yourself.