MongoDB vs RDBMS: Which one is Better?

When it comes to selecting a database management system, it generally becomes MongoDB vs RDBMS because these are the two major contenders in the market. Each one of these has its strengths and limitations, making the decision of which one to use a crucial consideration for developers and businesses.  

In this blog, you will learn about the key differences between MongoDB vs RDBMS and their performance characteristics. This blog also provides insights for those looking to transition from RDBMS to MongoDB. 

Table of Contents 

1) The difference between MongoDB and RDBMS 

      a) Data model 

      b) Schema flexibility 

      c) Relationships and joins 

      d) Scalability 

      e) ACID vs BASE 

      f) Use cases 

2) MongoDB vs RDBMS performance comparison 

      a)  Read/write operations 

      b) Scalability and high availability 

3) Conclusion  

The difference between MongoDB and RDBMS 

MongoDB and RDBMS are two distinct database management systems, each with its strengths and best-fit use cases. The choice between the two depends on the specific needs of your application, the nature of your data, and the scalability requirements.  

Developers and businesses should carefully evaluate their data requirements, performance expectations, and development agility before making the decision to opt for MongoDB or RDBMS. Let us understand the key differences between the two on various parameters.  

Data model  

MongoDB adopts a NoSQL, document-oriented data model, making it particularly adept at handling unstructured or semi-structured data. Instead of using tables with rigidly defined schemas, MongoDB stores data in flexible, self-descriptive BSON (Binary JSON) documents.  

These documents can contain nested arrays and other documents, allowing developers to store and retrieve complex data structures without the need for complex joins or foreign keys. 

While RDBMS follows a relational data model, data is organised into tables, where rows represent a record, and columns represent a data attribute. This structured approach enforces strict data integrity through primary keys, foreign keys, and constraints, ensuring that data follows predefined rules and relationships.
 

Schema flexibility  

MongoDB's schema-less nature allows for rapid development and iteration, especially in scenarios where data requirements are evolving or undefined. New fields can be added to documents without affecting existing ones, providing the flexibility to adapt to changing business needs.  

RDBMS relies on a fixed schema, which can make it cumbersome to modify the database structure once it is deployed. Altering the schema may involve significant downtime and data migration efforts, which can hinder agile development and innovation. 

Relationships and joins 

In MongoDB, relationships between data can be modelled using embedded documents or references (or a combination of both). Embedded documents facilitate related data storage together within the same document, minimising the need for joins and enabling fast, single-document read operations.   

RDBMS uses the concept of joins to combine related data from multiple tables based on common keys. This allows for normalisation, where data is stored in separate tables, reducing data redundancy and maintaining data integrity. Joins enable complex data retrieval, but they may introduce performance overhead, especially when dealing with large datasets.  

Scalability  

MongoDB is designed for horizontal scalability, which means it can effortlessly distribute data across multiple nodes or servers. As data volumes grow, additional servers can be added to the cluster, allowing for seamless scaling to accommodate high traffic and big data workloads.  

RDBMS typically relies on vertical scaling, where more powerful hardware is added to a single server to handle increased demand. While vertical scaling can improve performance, it may have limitations in handling extremely large datasets or high concurrency scenarios.  

ACID vs BASE  

MongoDB follows the “Basically Available, Soft state, Eventually consistent” (BASE) model, which prioritises availability and partition tolerance over strict consistency. While MongoDB offers strong consistency guarantees within a single document, consistency across multiple documents or collections might be eventually achieved.  

RDBMS adheres to the “Atomicity, Consistency, Isolation, and Durability” (ACID) properties. These properties guarantee that transactions are reliably processed and the database maintains a consistent state even in the presence of failures.  

Use cases  

MongoDB is well-suited for use cases where flexible data models, high write throughput, and horizontal scalability are essential. It is often preferred for real-time analytics, content management systems, Internet of Things (IoT) applications, and applications dealing with semi-structured or unstructured data.  

RDBMS excels in use cases requiring strict data integrity, complex transactions, and well-defined relationships between data entities. It is commonly used for traditional business applications, accounting systems, inventory management, and applications where data consistency is critical. 

MongoDB vs RDBMS performance comparison 

The performance comparison between MongoDB and RDBMS is context-dependent, and the choice between the two depends on the specific requirements of your application. MongoDB shines in read-heavy, large-scale, and unstructured data scenarios, offering excellent horizontal scalability and high availability.  

RDBMS, with its ACID properties and structured data model, is well-suited for applications demanding strict data integrity and complex transactions. When deciding between MongoDB and RDBMS, developers must carefully evaluate the nature of their data, the anticipated read/write workload, and their scalability and high availability requirements.  

Additionally, query patterns and indexing strategies should be taken into account to optimise performance for either database management system. Here is a detailed performance comparison between MongoDB and RDBMS. 

Read/write operations  

MongoDB: MongoDB's performance in read-heavy workloads is often superior to that of RDBMS. The document-oriented data model allows MongoDB to retrieve entire documents in a single operation, reducing the need for complex joins and improving read efficiency. Additionally, MongoDB's flexible schema means that documents can evolve over time without impacting read operations. 

Write operations in MongoDB are also efficient, especially when inserting new documents or updating existing ones. MongoDB uses a technique called "write-ahead logging" to ensure durability and crash recovery without impacting write performance. In cases where write concerns can be relaxed, MongoDB can provide impressive write throughput.  

RDBMS: RDBMS can excel in write-intensive scenarios, especially when dealing with complex transactions. The ACID properties ensure data consistency and durability, making RDBMS a preferred choice for applications that require strict data integrity and concurrency control.  

However, in read-heavy use cases involving complex joins and aggregation queries, RDBMS may encounter performance challenges. Joining large tables and performing complex queries can result in increased query execution times, impacting read performance. Additionally, as data volume grows, RDBMS may face limitations in handling large datasets, which could lead to performance bottlenecks. 

Scalability and high availability  

MongoDB: Scalability is one of MongoDB's greatest strengths. By employing horizontal scaling, MongoDB can distribute data across multiple servers, allowing applications to handle growing data volumes and high traffic without sacrificing performance. As data increases, additional servers can be added to the cluster seamlessly, ensuring smooth scalability.  

MongoDB offers built-in high availability through replica sets. A replica set is a group of MongoDB instances that maintain the same data, providing automatic failover in the event of primary node failure. This enhances fault tolerance and ensures continuous availability of data. 

RDBMS: RDBMS traditionally relies on vertical scaling to improve performance, which involves upgrading hardware resources of a single server. While vertical scaling can improve performance to a certain extent, it may have limitations in handling extremely large datasets and achieving true horizontal scalability. 

In terms of high availability, RDBMS often requires third-party clustering solutions or manual setup of failover configurations, making it more complex to implement compared to MongoDB's built-in replication features.  

Indexing  

MongoDB: MongoDB supports various types of indexes, including single-field, compound, multi-key, and geospatial indexes. Properly indexing collections can significantly boost query performance, especially in read-heavy workloads. MongoDB's indexing capabilities are crucial for executing complex aggregation queries efficiently.  

RDBMS: RDBMS also supports indexing to speed up query execution, allowing developers to create indexes on specific columns. Indexes in RDBMS can enhance the performance of SELECT queries, especially when filtering and joining data from multiple tables. 

Conclusion 

When deciding between MongoDB vs RDBMS, developers and businesses must carefully evaluate the nature of their data, the anticipated read/write workload, and their scalability and high availability requirements. Additionally, query patterns and indexing strategies should be taken into account to optimise performance for either database management system. 

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