MongoDB Vs Cassandra? which one can help you identify the features. Let’s discuss the difference between MongoDB and Cassandra
MongoDB and Cassandra are two juggernauts of the NOSQL world with unique features. MongoDB is a document-oriented NoSQL database that stores the data in BSON documents (a binary representation of JSON-like documents). In contrast, Cassandra is a highly scalable, distributed, and high-performance NoSQL database system that excels at handling large amounts of data across many commodity servers.
In this article, we will discuss what MongoDB is, what Cassandra is, and the difference between them based on different parameters.
So, let’s start with the tabular difference between MongoDB and Cassandra.
Table of Content
- MongoDB vs Cassandra: Difference Between MongoDB and Cassandra
- What is MongoDB?
- What is Cassandra?
- Similarities Between MongoDB and Cassandra
What is the difference between MongoDB and Cassandra?
|Architecture||Document-Oriented Data Model.Data is stored in BSON (Binary JSON) format.||Wide Column Store Model. Data is Stored in tables with rows and columns.|
|Data Schema||Schema less.i.e., it can store any form of JSON, like documents, that provides high flexibility in data representation.||Schema Orientedi.e., it ensures data consistency but lacks flexibility compared to MongoDB.|
|Performance(Read & Write Operations)||Excels in read-heavy applications due to efficient retrieval of entire documents.||Shines in write-intensive applications due to its wide-column store model enabling swift write operations.|
|Scalability||Provides horizontal scalability through sharding but requires more resources and management effort than Cassandra.||Superior in horizontal scalability due to its distributed architecture, handling large amounts of data across many commodity servers with no single point of failure.|
|Consistency||Prioritizes consistency and partition tolerance (CP) over availability.||Depending on the application requirements, offers tunable consistency, ranging from eventual to strong consistency.|
|Community Support||A large, active community backs it and offers enterprise-grade support through MongoDB Inc.||Robust community and professional support by DataStax.|
|Language Support||C, C++, C#, Java, Node.js, Perl, PHP, Python, Ruby, Scala, Go, and Erlang.||Java, Python, C++, C#, Node.js, Ruby, and Go.|
|Transactional Support||It provides multi-document ACID transactions.||It lacks multi-row ACID transactions but offers lightweight transactions (Compare-and-set).|
|Use Cases||It is ideal for content management systems and real-time analytics due to its document-oriented nature and ease of use.||It is suitable for high availability, fault tolerance, and scalability applications like the Internet of Things (IoT) and time-series data.|
What is MongoDB?
MongoDB is a document-oriented NoSQL database with high performance, availability, and easy scalability. It works on the concept of collections and documents. Unlike relational databases, which use tables to store data, MongoDB stores data in BSON documents (a binary representation of JSON-like documents) that can have varying structures.
Features of MongoDB
- Document-Oriented Storage: Data is stored in JSON-style documents, allowing it to store semi-structured data more effectively than a relational database.
- Indexing: Any field in a MongoDB document can be indexed, facilitating quicker data search.
- Replication: MongoDB provides high availability with replica sets, essentially a group of MongoDB servers that maintain the same data set, providing redundancy and increasing data availability.
- Automatic Sharding: It allows horizontal scalability by partitioning data across many servers.
- Ad hoc Queries: MongoDB supports search by field, range queries, and regular expression searches.
- GridFS: Store and retrieve large files such as images, videos, etc.
Advantages and Disadvantages of MongoDB
- Schemaless: It is a document database in which one collection holds different documents.
- Ease of scale-out: It is easy to scale out by adding new servers and partitioning the data across these servers.
- The structure of a single object is clear: No complex joins are needed as in a relational database, making the structure of a single object clear.
- Deep Query-ability: It uses a document-based query language to support dynamic document queries.
Disadvantages of MongoDB:
- No Support for Transactions: Unlike SQL databases, It doesn’t support transactions that might hinder certain applications.
- Memory Usage: It uses more memory for data storage due to its document-oriented data model.
- Lack of Standard Interfaces: It does not support SQL, so it can take time for developers to learn and adapt to its programmatic interface.
- Not Suitable for Small Data Sets: Given the high memory usage and scale-out architecture, MongoDB is less efficient for small data sets.
What is Cassandra?
Cassandra is a highly scalable, distributed, and high-performance NoSQL database system that excels at handling large amounts of data across many commodity servers. Cassandra can handle big data workloads across multiple nodes without any single point of failure.
Features of Cassandra
- Distributed: Data is distributed across the cluster (but can be replicated across multiple clusters), so each node can execute any request.
- Scalability: It is highly scalable; it allows adding more hardware to accommodate more customers and data per requirement.
- Fault-tolerant: Data is automatically replicated to multiple nodes for fault-tolerance. Replication across multiple data centres is supported.
- MapReduce support: Cassandra is integrated with Hadoop to leverage its MapReduce capabilities.
- CQL (Cassandra Query Language): An SQL-like language that makes interacting with Cassandra easier and more intuitive.
Advantages and Disadvantages of Cassandra
Advantages of Cassandra:
- Proven scalability and performance: It can handle petabytes of data spread across many commodity servers.
- Active Everywhere Design: All nodes can accept reads/writes, offering continuous availability and resilience to certain failures.
- Flexible Data Storage:It can accommodate data structured as a simple key/value, wide column, graph, or more complex structures.
Disadvantages of Cassandra:
- No Aggregation Operations: Cassandra does not support operations like GROUP BY, ORDER BY, or JOIN, making some queries more complex or even impossible.
- Complicated to Use: Cassandra’s data model is unique, and developers can have a steep learning curve.
- Limited Consistency: While Cassandra offers tunable consistency, achieving strong consistency can be more complex than traditional SQL databases.
- Inefficient for Small Datasets: Cassandra’s distributed nature can be overkill for small datasets and may lead to unnecessary overhead.
Similarities Between MongoDB and Cassandra
- NoSQL Databases: Both are part of the NoSQL family, i.e., they overcome the limitations of traditional relational databases, particularly when it comes to handling large volumes of data and horizontal scalability.
- Schema Design: Both allow for a flexible schema. i.e., the structure of data can be altered over time and does not need to be strictly defined upfront as in traditional relational databases.
- Scalability: Both are designed with scalability in mind. i.e., it can handle a high volume of data and can be easily scaled out by adding more servers to the system to accommodate increasing data loads.
- Distributed Systems: Both are designed to run on distributed systems. They can run across multiple servers or data centres, offering high availability and data redundancy.
- CAP Theorem: Both follow the CAP theorem’s principles (Consistency, Availability, Partition tolerance). They make different trade-offs between consistency and availability, but both ensure partition tolerance.
In this article, we have briefly discussed what MongoDB and Cassandra are and their differences. The article also covers the features, advantages and disadvantages of MongoDB and Cassandra.
Apache Cassandra is a widely adopted wide-column store designed for specific use cases where a single primary key does the writing and is the vast majority of workloads. Scaling in Cassandra is only applicable to fairly niche workloads.
MongoDB is a general-purpose database that can support multiple use cases with its flexible document model, rich aggregation language, and robust features such as sharding and ACID-compliant transactions. Therefore, it can cover most of Cassandra’s most popular use cases.
Hope you will like the article.
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