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Cassandra vs MongoDB: A Detailed Comparison

The Explosive Growth of NoSQL

Over that last decade, NoSQL databases have seen massive adoption with global revenues increasing from $286 million in 2013 to over $4.2 billion by 2022. A key driver of this rapid growth has been the increasing amounts of unstructured and semi-structured data from web and mobile applications. These novel data types highlighted limitations around rigid schemas and join-heavy workloads of relational databases like MySQL and Postgres. NoSQL databases stepped in to meet many of these new challenges especially with their ability to scale out affordably on commodity infrastructure.

NoSQL database growth chart

According to an IDC whitepaper on the data age, global data is expected to grow from 33 ZB in 2018 to 175 ZB by 2025. A majority of this data is unstructured data like images, video files, log files and sensor data. This deluge of data coupled with need for always-on availability drove the creation of distributed NoSQL databases that could scale horizontally. Apache Cassandra and MongoDB emerged as two most popular options to fulfill many modern application requirements.

Diving Deep into Cassandra

Apache Cassandra was open-sourced by Facebook engineers to power the inbox search feature requiring low latency reads/writes on user data distributed across the world. A few key architectural choices and implementations empower Cassandra with tremendous scalability and resilience:

Peer to Peer Distributed System

Cassandra is built as a peer to peer distributed system without master nodes. All nodes are identical with the same responsibilities. With a gossip protocol, nodes stay in continuous communication about state changes. This avoids single points of failure and allows linear scalability.

Data Replication Across Nodes

Data is replicated across nodes using tunable consistency levels like Quorum, One, Local Quorum etc. Based on chosen consistency and replication factor, commits succeed if they meet the specified durability constraints. Network timeouts use "hinted handoff" to retry writes and read repairs handle any consistency issues.

Column Storage Model

Within a keyspace, related data is stored in column families (tables)containing rows of related data. This model allows applications to specify what data to read and write minimizing overfetches. Column names, values and timestamps allow evolving schemas.

Commit Log and Memtables

For durability, writes first get appended to the commit log before updating memtables in memory. Once threshold is crossed, memtables get flushed to SSTables on disk. This balances durability with performance.

Replication Strategies

The NetworkTopologyStrategy allows sophisticated replication models across one or more data centers balancing factors like consistency, fault tolerance and data locality.

Cassandra architecture

With innovations like these, Cassandra powers some of the largest deployments in the world including Apple, Netflix and eBay managing petabytes of data distributed globally.

MongoDB Under the Hood

While Cassandra was engineered from the start for scale, availability and partition tolerance, MongoDB sought to make a general purpose database well suited for an agile, iterative developer workflow. Some key aspects include:

JSON Documents

MongoDB stores schema flexible JSON documents which map naturally to code allowing developers to focus on app logic rather than database. Documents can contain nested objects and arrays to capture complex, polymorphic data relationships.

Ad-hoc Queries

The powerful MQL (MongoDB Query Language) supports sophisticated manipulation and queries over the document structure and data without needing to declare schemas upfront. Indexes speed up queries over frequently used fields.

Tuning for Performance

Several tuning knobs allow optimizing MongoDB for target workloads. Configuring indexes, sizing clusters, isolating collections, leveraging in-memory engines etc help achieve high performance and throughput.

Auto Sharding

As datasets grow, MongoDB can automatically split and rebalance data across shards allowing horizontal scalability across clusters and geographies while abstracting complexities away from developers.

MongoDB architecture

Together these capabilities have made MongoDB an extremely versatile backend for modern applications powering use cases in retail, technology, financial services, media & entertainment and several other industries.

Cassandra vs MongoDB Architecture

While both Cassandra and MongoDB offer high availability through replication, the implementation contrasts:

Cassandra vs MongoDB architecture

Cassandra relies on peer to peer distributed model where all nodes are identical and use a gossip protocol to detect signals about topology changes. This avoids bottleneck seen in master-slave or primary-secondary models.

MongoDB leverages primary/secondary replication with automatic failover to secondaries in the event of primary going down. Sharded clusters also have config servers which store metadata and manage sharding.

Comparing Write Paths and Consistency

The write path implementation affects consistency guarantees as well as performance and availability:

Cassandra vs MongoDB write paths

In Cassandra, the co-ordinator node that receives the write forwards it to all replica nodes based on consistency level chosen. A response is sent back once the required acknowledgements are received. Tunable consistency gives flexibility.

In MongoDB, the primary node handles all writes which then get replicated asynchronously to secondary nodes. Writes are fast but reads may not always reflect most recent state.

Sample Use Cases

To highlight how some real applications may leverage Cassandra and MongoDB, lets compare two sample use cases:

Social Media Platform

schema with heavy nested structures makes MongoDB a great fit to model complex social graphs and hierarchies. Flexible documents can capture diverse entities like users, posts, comments, media etc. Ad-hoc queries also simplify analytics use cases.

IoT Data Pipeline

With massive amounts of sensor data requiring efficient storage and retrieval, Cassandra‘s column families provide an optimized model matching IoT access patterns. Linear scalability also handles ingest spikes as device deployments grow. Timeseries data pairs nicely with cluster spanning partition schemes.

As evident, factors like data models, query patterns, scale needs etc should guide the decision between the two databases.

Performance Benchmarks

While both databases can achieve impressive throughput and low latency, some key benchmarks provide data points:

Cassandra vs MongoDB benchmarks

A few key takeaways:

  • Structured Data: For more structured access patterns, Cassandra benchmarks faster especially on writes
  • Increased Scale: With more nodes, Cassandra shows greater concurrency with near linear gains
  • Tunability: MongoDB can be optimized significantly for reads or writes using indexing, caching etc

So based on application profiles around queries, concurrent users, data size etc, both databases can be tuned to hit performance service levels.

Additional Key Differences

Beyond the core architecture and capabilities, some other notable differences:


Cassandra allows per table compression with options like LZ4 and Deflate. MongoDB supports wiredTiger and zStandard compression for storage efficiency.

Multi Data Center Support

Cassandra has built-in constructs like NetworkTopologyStrategy to optimize replication across one or more data centers. MongoDB achieves this through zone sharding.


Cassandra integrates well with Apache Spark for analytics. MongoDB has connectors to Spark and BigQuery as well as Atlas Search and Charts integrations.


Both provide role based access, SSL encryption etc. Cassandra has lighter weight auth while MongoDB offers more granular control and auditing.


Cassandra works with Grafana, Prometheus, DataStax etc. MongoDB has first-party tools like Compass GUI and free monitoring with Atlas. Broader ecosystem support.

The Bottom Line

While both Cassandra both MongoDB have proven their mettle across mission critical deployments, understanding their background and technical nuances helps match them to appropriate application scenarios playing to their strengths.

Cassandra shines where absolute scale, performance and availability are critical like internet scale web apps, telecom systems or financial trading platforms. MongoDB provides a versatile backend for far more complex data modeling use cases like content management, inventory or analytical apps.

As leading NoSQL technogies, both continue to evolve by adopting capabilities from the other with MongoDB adding tunable consistency and transactions while Cassandra incorporates features like JSON support.

This exploration of their key similarities and differences provides a perspective for engineers to make an informed choice driven by application specific priorities and tradeoffs.