The Battle “Cassandra vs MongoDB” - Differences You Should Know!

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18th Jul, 2022
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04th May, 2022
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The Battle “Cassandra vs MongoDB” - 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.

  • Consistency: All nodes receive the latest write or show an error.
  • Availability: Read queries contain the same data on all nodes, but it might not be the latest.
  • Partition tolerance:  The system continues to be responsive despite a network failure (for example: slow or unavailable network connections between nodes and dropped partitions.)

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.

Principles of CAP theorem

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: 

  1. Atomicity indicates that the operations will be entirely successful or fail altogether as a single unit.
  2. Consistency shows that the databases will not violate certain constraints defined for the data as a whole
  3. Isolation means hiding each operation from view until the whole transaction is complete, i.e., a read is possible only after a write is completed.
  4. Durability ensures all changes to the data are safely preserved.

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.

What are NoSQL Databases, and Why Do We Need One? 

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-

  • Document databases  store information in the form of documents like JSON, XML and more. Eg: MongoDB, Couchbase, Amazon DynamoDB, Apache CouchDB, MarkLogic
  • Wide-column databases store data in a columnar format similar to relational databases and, at the same time, support a wide range of data naming and formatting in each row within the same table. These databases, like key-value stores, contain some fundamental structure while also retaining a great deal of flexibility. E.g., Apache Cassandra, Apache HBase, Google BigTable, Microsoft Azure Cosmos DB
  • Key-value databases arrange related data into collections with entries defined by unique keys for simple retrieval. Although they show a structure similar to a relational database, they still retain the advantages of NoSQL. E.g., Redis, Riak, Amazon DynamoDB, Couchbase Server (previously known as Membase)
  • Graph databases define the relationships between stored data points using graph structures. Patterns in unstructured and semi-structured data can be identified using these databases. Eg: Neo4j, ArangoDB, HypergraphDB, Nebula Graph

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.

What is Cassandra?

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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What is MongoDB? 

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. 

Many developers across the globe use MongoDB Atlas to deploy fully managed cloud databases across AWS, Azure, and Google Cloud. It provides best-in-class data security and privacy standards procedures allowing developers to have faster access to the availability, scalability, and compliance essential for developing enterprise-level applications. Knowledgehut is one of the best MongoDB DBA online training providers, with hands-on learning on how to use MongoDB.

Databases and CAP Theorem

Cassandra vs MongoDB: Similarities 

After a short introduction to these two NoSQL databases, let us look at some similarities between them.

  • Both MongoDB and Cassandra are NoSQL distributed databases. 
  • Both are open-source.
  • Both are horizontally scalable but in different ways.
  • Both of these databases support sharding (horizontal partitioning) and replication.
  • Both these databases cannot serve as a replacement for the traditional RDBMS databases.
  • Both these databases do not comply with ACID (Atomicity, Consistency, Isolation, Durability), a property that indicates that database transactions that guarantee database transactions are processed reliably.
  • Consistency and Normalization are two concepts that these two database types do not satisfy (as these lean more towards the RDBMS database types)

Cassandra vs MongoDB: Differences 

Both these technologies are significant in their respective industries. We will highlight some common aspects of these tools and their main differences.

  • MongoDB is a document store database that works with collections containing multiple documents, whereas Cassandra is a column-oriented database.
  • MongoDB has a master-slave architecture, while Cassandra has a peer-to-peer architecture where all are master nodes in communication with each other.
  • Cassandra has no single-point failure due to its peer-to-peer architecture, while MongoDB can have a single-point failure with its master, but this could be repaired quickly by switching the master.
  • MongoDB supports secondary indexes, whereas Cassandra works well with a primary index stored globally. Although Cassandra can support secondary indexes, it tends to be less efficient as secondary indexes are stored locally on the nodes.
  • MongoDB fulfils consistency and partitioning tolerance, whereas Cassandra is highly available with partition tolerance as per CAP theorem. MongoDB sacrifices availability, whereas Cassandra gives up consistency.
  • MongoDB uses binary JSON or BSON format to store datastores, an extremely expressive data model, while Cassandra uses a columnar style and tables.
  • MongoDB has a rich and expressive data model known as the 'object-oriented model,' which supports different data structures and nested properties for multiple levels. In contrast, Cassandra has a traditional data model with table structure, rows, and specific data type columns.

Let us summarize these above similarities and differences in a table-

FeaturesCassandraMongoDB
Developed byApache FoundationMongoDB Inc.
Type of DatabaseNoSQLNoSQL
LicenseOpen-SourceOpen-Source
Horizontal ScalabilityYesYes
Sharding supportYes, Auto-shardingYes, built-in
Developed inJavaC++
Support for secondary indexesLimitedYes. Full
ArchitecturePeer-to-PeerMaster-Slave
Single point failureNoYes
CAP theorem featuresAP (Highly Available and Partition Tolerant)CP (Consistency and Partition Tolerant)
Database formatTabular or Wide- ColumnDocument store & uses Binary JSON (BSON)
Database modelTraditional model with rows and columnsRich and expressive (object oriented)
Query LanguageCQL (Cassandra Query Language) similar to SQLMongoDB Query Language (MQL).
Queries use Javascript
ReplicationYes, Selectable replication factorYes, Master slave replication 
AggregationNoYes, Built-in
TransactionsYes, as transactions require availability and Cassandra is highly availableNo, MongoDB is consistent but not highly available
Writing & Reading SpeedsWrites blazingly fast as multiple masters can accept requests in parallel but complex reads are slowerReads extremely fast but write speed is limited due to master-slave architecture
Few of the Languages supportedC++, C#, Java, Python, Nodejs, Ruby, Go, Scala,C/C++, C#, Java, Python, Nodejs, Ruby, Go, Scala, Matlab, R

Code Syntax for Cassandra vs MongoDB

A sample query to insert a record into an Apache Cassandra table as follow:

Apache Cassandra Code Syntax

The same query in MongoDB will be written as follows:

MongoDB Code Syntax

Pros and Cons of Cassandra 

Advantages of Cassandra

  • It's open-source technology with a peer-to-peer architecture which eliminates a single point of failure
  • Cassandra is highly scalable
  • It supports data replication, and hence, it is fault-tolerant and has high availability
  • It can easily handle massive amounts of data, and writes are extremely fast  

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.

  • It doesn't support ACID and relational data properties
  • Cassandra doesn't support aggregates
  • Cassandra has been optimized for fast writes, and hence, reads are slow 
  • There is no official documentation from Apache

Pros and Cons of MongoDB 

Advantages of MongoDB

  • MongoDB is an open-source, scalable NoSQL database
  • It is a schema-less database that supports sharding and aggregation
  • Both community and enterprise versions are available
  • Consistency is inbuilt due to its master-slave architecture, and availability is also possible due to replica sets.

Disadvantages of MongoDB

  • Complex joins are not possible.
  • High memory usage
  • Limited nesting and document data size 

Top use cases

After understanding the benefits and limitations of Cassandra and MongoDB, let us look at their top use cases.

Cassandra 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 Use Cases 

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.

Key Factors That Drive the Apache Cassandra Versus MongoDB Decision 

The decision of whether to choose one of these two technologies is relative and depends on multiple factors outlined below-

  • Scalability and Speed: Cassandra can be preferred if high scalability with faster writing speed is the main requirement.
  • Data availability: MongoDB is a good choice if consistency is a priority and availability can be compromised.
  • Data Model: For a richer data model, MongoDB can be preferred as its schema-less, and document type architecture imparts higher flexibility and an option to arrange objects within the given hierarchy.
  • Schema: Although Cassandra and MongoDB are schema-less, MongoDB is better as it has higher flexibility.
  • Query language:  The choice of programming language depends on the individual experience, project requirements (i.e., handled data size and expected types of queries), and available frameworks. Both MongoDB and Cassandra support a wide range of programming languages. Cassandra meets the query language requirements better with its native CQL language. 
  • Aggregation: A built-in aggregation framework is available in MongoDB but not Cassandra. So, if this is required, MongoDB is a better choice.
  • Secondary indexes: This depends on the way of querying. Cassandra can be chosen for queries mainly by the primary index, but MongoDB would be a better solution if secondary indexes are required.

So, the decision of whether to use MongoDB or Cassandra will ultimately depend on the company's requirements, infrastructure, and available technical expertise.

Compare & Contrast 

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.

Frequently Asked Questions (FAQs) 

Q. When should we prefer Cassandra over MongoDB?

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.

Q. When should we use MongoDB instead of Cassandra? 

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.

Q. Are MongoDB and Cassandra made for similar use cases?

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. 

Q. Where do you use the Cassandra database?

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.

Profile

Devashree Madhugiri

Author

Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms.
She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.