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HomeBlogData ScienceCassandra vs MongoDB : Key Differences You Should Know!
To go into the essential aspects of the battle between MongoDB and Cassandra, we must revisit the basics of Big Data and data science. We know that big data is a relative term and can have a size of Megabytes, Gigabytes, or even Terabytes, but it is of no use if not interpreted for a meaningful purpose.
In the early 70s, the need to store data and do necessary calculations was fulfilled by row table tabulation format. It came to be established as Relational Database Management System (RDBMS). It was an excellent tool until the data was generated from limited sources and manageable volume. Around the year 2000, there was a sudden upsurge in data generation due to social platforms, sensor records, and web development. The need to store and handle big data of all types like structured, unstructured (share about 80%), and semi-structured with varying types became unmanageable for RDBMS.
SQL, the structured query language used until then and RDBMS started facing storage, speed, and scalability problems. Researchers were continuously trying to find a solution to this drawback of RDBMS. This was the entry point of NoSQL (not only SQL) databases, which progressively started giving answers to handle any type of data at high speed, scalability, and CAP features.
CAP theorem is an important terminology in the design of distributed databases. According to this theorem, also known as Brewer's theorem, CAP stands for Consistency, Availability, and Partition Tolerance. Let us briefly understand what each of these three terms means.
As the CAP theorem states, these NoSQL databases fulfil only two of the three features, viz. consistency, availability, and partition tolerance. However, these features are significant while handling queries to ensure reliability, failure-free operations, and huge volumes of read and writes at low latency. This theorem is considered valid for distributed database design; however, it is currently under debate as some people claim that a distributed database can provide all three CAP features with a few modified actions during setup.
Another important term to consider in the context of relational databases is ACID. It is an acronym for Atomicity, Consistency, Isolation, and Durability. ACID transactions are an essential feature of traditional RDBMS as they allow combining a series of different database operations into a single transaction that ensures the following four guarantees:
Until recently, it was assumed that NoSQL databases do not provide full ACID compliance similar to RDBMS. However, this is changing as more NoSQL databases like MongoDB, Cassandra, CouchDB, etc., now seem to provide a higher degree of ACID compliance through the BASE (Basically Available, Soft State, and Eventually Consistent) principles. Interested in learning MongoDB? Check out the best MongoDB course here.
NoSQL databases are non-tabular databases that store data differently instead of several relational tables with rows and columns as in traditional RDBMS. NoSQL stands for "not only SQL" rather than "no SQL at all." NoSQL databases are classified into several categories based on their data model. These databases are intended to be versatile, scalable, and capable of quickly responding to the data management demands of modern businesses.
NoSQL databases are of 4 main types-
As the requirements of businesses are changing with the increasing use of interactive applications, real-time data management has become crucial. This calls for faster and more versatile systems capable of scaling and handling data variability. These requirements are difficult to meet using a traditional RDBMS solution, and hence, companies are keen on implementing the NoSQL database technology.
The following sections will explore two popular NoSQL databases, Cassandra and MongoDB.
Cassandra, also known as Apache Cassandra, is a distributed NoSQL database that was developed at Facebook and published as an open-source project in July 2008. Cassandra provides modern applications with continuous availability without downtime. It further ensures high performance and linear scalability required by such applications. Additionally, Cassandra provides simple operations and a seamless replication facility across data centers and zones. It can handle data in petabytes and multiple concurrent operations in seconds. This capability allows organizations to handle huge volumes of data across hybrid and multi-cloud systems. Cassandra ensures low latency for clients by providing strong support for data clusters with asynchronous masterless replication. Moreover, it complements Amazon Dynamo's distribution strategy with Google Bigtable's data model.
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MongoDB was first released in 2009 by 10gen as an open-source project. MongoDB is a versatile and scalable NoSQL document database platform developed to overcome the constraints of previous NoSQL solutions and the approach of relational databases.
MongoDB is popular for its horizontal scaling and load balancing features, which provide application developers with better levels of flexibility and scalability due to its master-slave architecture.
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After a short introduction to these two NoSQL databases, let us look at some similarities between them.
Both these technologies are significant in their respective industries. We will highlight some common aspects of these tools and their main differences.
Let us summarize these above similarities and differences in a table-
| Features | Cassandra | MongoDB |
| Developed by | Apache Foundation | MongoDB Inc. |
| Type of Database | NoSQL | NoSQL |
| License | Open-Source | Open-Source |
| Horizontal Scalability | Yes | Yes |
| Sharding support | Yes, Auto-sharding | Yes, built-in |
| Developed in | Java | C++ |
| Support for secondary indexes | Limited | Yes. Full |
| Architecture | Peer-to-Peer | Master-Slave |
| Single point failure | No | Yes |
| CAP theorem features | AP (Highly Available and Partition Tolerant) | CP (Consistency and Partition Tolerant) |
| Database format | Tabular or Wide- Column | Document store & uses Binary JSON (BSON) |
| Database model | Traditional model with rows and columns | Rich and expressive (object oriented) |
| Query Language | CQL (Cassandra Query Language) similar to SQL | MongoDB Query Language (MQL). Queries use Javascript |
| Replication | Yes, Selectable replication factor | Yes, Master slave replication |
| Aggregation | No | Yes, Built-in |
| Transactions | Yes, as transactions require availability and Cassandra is highly available | No, MongoDB is consistent but not highly available |
| Writing & Reading Speeds | Writes blazingly fast as multiple masters can accept requests in parallel but complex reads are slower | Reads extremely fast but write speed is limited due to master-slave architecture |
| Few of the Languages supported | C++, C#, Java, Python, Nodejs, Ruby, Go, Scala, | C/C++, C#, Java, Python, Nodejs, Ruby, Go, Scala, Matlab, R |
A sample query to insert a record into an Apache Cassandra table as follow:
The same query in MongoDB will be written as follows:
Advantages of Cassandra
For more information about MongoDB Advantages, visit MongoDB advantages & use cases.
Drawbacks of Cassandra
Every database management tool has some limitations, and so does Cassandra.
Advantages of MongoDB
Disadvantages of MongoDB
After understanding the benefits and limitations of Cassandra and MongoDB, let us look at their top use cases.
Cassandra is preferred for handling write-heavy tasks where data is likely to be frequently added but rarely updated. These could be transactions logging in Banking and Finance, events logging in web analytics or messaging systems, time-series data, tracking of inventory, IoT (Internet of Things) data, weather tracking, etc. Cassandra is also an excellent option for geographically distributed data (e.g., data added in the EU is also available in the USA) and highly scalable applications in the cloud. That is why it is being used for handling vast amounts of data with speed and sustained availability. Top Companies that use Cassandra include IBM, Netflix, Spotify, Reddit, Facebook, Uber, etc. To stay abreast of MongoDB real world use cases, check out the top real world use cases and applications of MongoDB.
MongoDB scores better in the context of big data workloads requiring content management, analytics, and time-series data. The built-in aggregation feature makes it possible to pull data into a central database providing a single view of the data. MongoDB finds its application in areas of IoT applications, CMS (Content Management Systems), Mobile Applications, and more. Top companies using MongoDB include eBay, Google, SAP, Forbes, Facebook, and Adobe.
The decision of whether to choose one of these two technologies is relative and depends on multiple factors outlined below-
So, the decision of whether to use MongoDB or Cassandra will ultimately depend on the company's requirements, infrastructure, and available technical expertise.
In this article, we have explored the various similarities and notable differences between Cassandra and MongoDB. Additionally, we looked at the advantages and disadvantages of both the NoSQL databases, followed by their specific use cases. To delve deeper into aspects of Cassandra, a Cassandra Certification Training is a good choice.
Ultimately, deciding which of these to use depends on the business needs (structured flexibility or continuous availability), the amount of data to be handled, and the data model required by the particular application. Like most businesses today tend to have data stored across multiple databases, sometimes, a hybrid approach could also be implemented to get the best of both these databases.
Hence, the result of the so-called battle between Cassandra and MongoDB is decided by the customer's choice. With a focus on one's own data type, its size, the need for speed, volume of reads and writes, and further scaling requirements, the customer will take a call about whether he should go for Cassandra or MongoDB. This is quite obvious from the detailed analysis of these two popular NoSQL databases that we have seen. Want to know about web development? Check out the KnowledgeHut's web development course.
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Both databases have their advantages, but in situations where easy query language support is a must, Apache Cassandra becomes a preferred choice as it has a query language called the Cassandra Query Language (CQL). Similarly, when fast writing speeds are non-negotiable, Cassandra can be preferred over MongoDB as it can handle a vast number of time-series data at rapid speeds. Finally, if a high level of availability is expected from the database, Cassandra scores over MongoDB since it allows for continuous, real-time analysis.
Both MongoDB and Cassandra are NoSQL databases and are good choices when a schema-free design is required. However, if there is a need for a document-centric database, MongoDB is a popular choice over Apache Cassandra, a columnar database. Moreover, the requirement for consistency, secondary indexes, aggregation, and a rich data model favour the implementation of MongoDB instead of Cassandra.
Although NoSQL distributed databases MongoDB and Cassandra may have similar features, they are unsuitable for the same use cases. Their architectures are significantly different. MongoDB, a document type database with a master-slave architecture, is suitable for building apps, especially mobile apps with infinite scaling possibilities. This type of architecture benefits applications requiring quick access and sharing options for locally created information across networks.
On the other hand, Cassandra, a columnar database with peer-to-peer architecture, is suitable for handling large amounts of data for applications in the cloud that require high scalability and sustained availability.
NoSQL database- Apache Cassandra is a very efficient and extensively used database that provides a highly available service with no single point of failure. This advantage is crucial for businesses that cannot afford to have their system fail or lose data and require continuous real-time monitoring.
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