Apache Cassandra vs. MongoDB

Apache Cassandra and MongoDB are widely adopted NoSQL databases designed for storing and managing large amounts of data.

The popularity of these two database systems is due in part to their high scalability and availability. Both also have been in use for well over a decade: Cassandra was released as an open source project in 2008; the release of MongoDB occurred the following year.

Similarities notwithstanding, Apache Cassandra and MongoDB differ significantly with respect to their data models, architecture and other components. These foundational differences impact their performance regarding key characteristics and can influence which data management use cases they serve best.

Before comparing Apache Cassandra and MongoDB, it’s helpful to establish an understanding of NoSQL databases.

NoSQL databases, also referred to as “not only SQL” or “non-SQL” databases, are distributed databases. This means that the information within them undergoes replication to various nodes (individual servers that store data). This distributed architecture supports high availability and durability; if one or more nodes go offline, the rest of the database can continue to run.

Most notably, however, NoSQL databases are designed to store and query data outside the traditional structures found in relational database management systems (RDBMS). Rather than adhering to a strict tabular structure inherent in traditional relational databases, non-relational database design does not require a rigid schema. This allows for rapid scalability to manage large data sets, including structured, semi-structured and unstructured data sets.

(It’s important to note that the scalability prized in NoSQL databases, including Cassandra and MongoDB, is horizontal scalability or “scaling out.” In horizontal scalability, workloads can be divided among servers, in contrast to the vertical scalability or “scaling up” associated with SQL databases, which requires the addition of memory to existing hardware.)

Due to their performance, scalability and flexibility, NoSQL databases have emerged as the go-to choice for supporting big data applications and real-time workloads. In addition to Apache Cassandra and MongoDB, other popular NoSQL databases include DynamoDB (provided by AWS), Redis and CouchDB.

Though both originated just a few years after the turn of the millennium, Apache Cassandra and MongoDB have distinct histories.

Apache Cassandra dates back to Facebook circa 2007, when engineers sought a system that could store data for the company’s growing messaging platform. By combining established NoSQL database models, they created a system with efficient data structures and eventual consistency—where updates propagate until all replicas match over time. The engineers released Cassandra as an open source project in 2008. A year later, Apache Software Foundation took over stewardship. 

MongoDB began as part of a platform-as-a-service project from the company 10Gen in 2007. The company pivoted to focus on MongoDB—its name a play on the word “humongous”—and developed it as a document-oriented database that worked quickly and was easy to use. ¹

10Gen, which eventually changed its name to MongoDB Inc., released MongoDB as an open-source project in 2009. The most recent versions of MongoDB, however, are published under the Server Side Public License v1.

The foundational differences between Apache Cassandra and MongoDB impact their performance and ideal use cases. Key elements include:

• Data models
• Architecture and storage
• Query and other languages

NoSQL databases rely on one of four kinds of data models:

• Document model: Data is stored as structured documents, typically in JSON (JavaScript Object Notation) or BSON (Binary JSON).
• Wide-column model: Data is stored in tables with sparse columns, meaning every row in a table can have different numbers of columns.
• Key-value model: Data is stored as key-value pairs (identifiers or labels paired with specific values).
• Graph model: Data is stored as nodes and edges, representing entities and relationships.

Cassandra’s data model is a wide-column model, also known as a wide-column store. Each row in a Cassandra table has a collection of columns and a unique partition key that is used to distribute data across nodes and data centers. Rows are identified by primary keys, which can be made up of partition keys and, optionally, clustering keys (columns that can uniquely identify rows within a partition, or related group).

This approach is more flexible than that of relational databases, which have space allocated to a set number of columns. Through Cassandra’s data model, using columns only as necessary results in more efficient storage and faster queries. ²

In contrast, MongoDB uses a document model. Data is primarily stored as BSON, a binary representation of JSON developed by MongoDB.

BSON helps address the obstacles that standard JSON presented for databases: supporting limited data types, a lack of fixed length for objects (which slows the speed of traversal), and a lack of metadata (which slows document retrieval). BSON was designed to optimize for speed and efficiency by encoding format and length information. It also supports some non-native JSON data types, such as dates and binary data. ³

As NoSQL databases, both Apache Cassandra and MongoDB support distributed systems, with data storage across multiple computing resources to mitigate downtime. But, as with their data models, the architecture underlying this distribution is fundamentally different.

Apache Cassandra relies on a peer-to-peer architecture. Every node in a Cassandra cluster is equal, with no reliance on a master node. When data is put into a cluster, a hash function is applied to the row’s partition key and the output is used to assign data to specific nodes. The data is also copied to other nodes.

The replication factor of a Cassandra database describes the number of copies of data stored in the database. Cassandra’s storage engine uses a step-by-step flow (or write path) consisting of a commit log, an in-memory table (memtable) and sorted string table (SSTable) files.

In contrast to Cassandra, MongoDB uses a primary/secondary model for its distributed architecture. In MongoDB, a replica set (a group of instances) consists of a primary node that handles all write operations (data additions or modifications) and secondary nodes that reflect the data in the primary node.

Large datasets in MongoDB can also be distributed to multiple machines through a process known as sharding. Information is divided into sharded clusters—multiple replica sets and a router that transmits queries from applications to the replica sets—to improve the system’s capacity to handle data requests.

The databases also employ different indexing methods. In Apache Cassandra, the primary index is the partition key, although Cassandra documentation cites storage-attached indexing (which features indexes for non-partition columns) as appropriate for most use cases. ⁴ Cassandra also has secondary indexes, which are local indexes stored in tables separate from the values being indexed. MongoDB supports several different index types for different use cases, including geospatial indexes, multikey indexes and text indexes.

By definition, NoSQL databases don’t use Structured Query Language (SQL) the standardized programming language for relational databases. However, both Apache Cassandra and MongoDB have their own query languages.

Cassandra uses a customized version of SQL called Cassandra Query Language (CQL). While CQL resembles SQL to a large degree, there are key differences between the two. For instance, SQL operates on normalized tables, while CQL is designed for denormalized Cassandra data aligned with partition keys. In addition, SQL is optimized for transactions, while CQL is designed for real-time queries and high-volume write operations.

MongoDB uses MongoDB Query Language (MQL). Designed for querying document models, MQL shares the same syntax as documents—marking a greater departure from SQL than Cassandra Query Language. MQL is touted for enabling a range of queries and data manipulation capabilities, including complex queries, aggregation pipelines and queries of geospatial data ⁵

In addition to their respective querying languages, the databases differ in programming support. MongoDB provides official drivers for over a dozen programming languages, such as Java, Python, Ruby and Node.js. These and other languages are also compatible with Cassandra, but the drivers are largely offered by third-party providers.

The foundational differences between Apache Cassandra and MongoDB give rise to some variations in characteristics associated with their performance. These variations can also be explained by the CAP theorem.

CAP is an abbreviation representing three desired characteristics of distributed systems: consistency (all clients see the same data at the same time), availability (any client making a data request receives a response, even if one or more nodes are down) and partition tolerance (a cluster of nodes continues to function even amid communications breakdowns between two or more nodes).

The CAP theorem dictates that a distributed system can deliver only two of three desired characteristics. Apache Cassandra is generally categorized as an “AP” database, delivering high performance primarily on availability and partition tolerance.

Meanwhile, MongoDB is known as a “CP” database, excelling on the partition tolerance and consistency fronts. But for both databases, measures also exist to improve performance on purportedly compromised characteristics—that is, consistency for Cassandra and availability for MongoDB.

Let’s take a closer look at the three desired characteristics.

Cassandra supports high availability because, as a decentralized system with data replicated to multiple nodes, it features high fault tolerance and no single point of failure. If one node experiences downtime, others with copies of the same data can fulfill a data request. In addition, the replication of data to data centers around the world allows for low latency for local users.

Since MongoDB’s architecture is based on a primary/secondary model, a single point of failure can occur when a primary node goes down. However, MongoDB’s failover is considered robust: During what are known as replica set elections, nodes belonging to a replica set select a new primary node to replace the unavailable primary node. This process allows MongoDB to also offer high availability, albeit with a brief delay—performance resumes only after the new primary node is chosen.

MongoDB inherently delivers high consistency because all clients are writing to a single source of truth—each replica set can have only one primary node that receives all the write operations. In contrast, Apache Cassandra provides eventual consistency: clients can write to any nodes at any time, and then inconsistencies are reconciled as quickly as possible.

Cassandra also allows users to optimize for consistency (while deprioritizing availability) through what’s known as tunable consistency. Users can select a consistency level, which sets how many replicas must acknowledge a read or write before confirming it to the client application. Higher levels of consistency require more replicas to respond with acknowledgements, but this also increases latency and decreases availability.

Both Apache Cassandra and MongoDB deliver partition tolerance because each is designed to continue functioning even when a communications breakdown occurs in one part of the system.

In Apache Cassandra, nodes remain available in the event of a communication problem, but some nodes may not deliver the most up-to-date versions of data (in response to data requests) until the partition is resolved. In MongoDB, availability is limited to ensure data consistency while the partition is addressed.

Apache Cassandra is often recommended for high-throughput, globally distributed, write-heavy workloads where availability and scalability are critical, such as streaming and entertainment. For example, streaming services like Netflix use Cassandra to handle global user activity.

MongoDB is ideal for document-centric, flexible-schema use cases that benefit from developer agility and strong consistency. Companies often rely on MongoDB to support their content management systems because MongoDB stores and serves an array of content assets.

Despite the differences between the two databases, use cases for Apache Cassandra and use cases for MongoDB can overlap. Case studies for each database demonstrate their effectiveness for Internet of Things (IoT) applications, e-commerce and more.