Introduction

RDBMS, short for Relational Database Management System, employs tables to store data in a structured format. It ensures various levels of data security. Each table typically includes a primary key for unique record identification and may optionally feature foreign keys to establish relationships between tables. SQL is the standard query language for RDBMS databases, with popular examples being MySQL, Oracle, and SQL Server.

In contrast, MongoDB represents a type of NoSQL database. It operates on a document-oriented model and relies on binary JSON or BSON for data storage. MongoDB does not employ SQL for querying; instead, it stores data as documents, offering a versatile data model and support for distributed servers. Additionally, MongoDB is open-source in nature.

Comparing the performance of MongoDB with RDBMS

The choice between MongoDB and RDBMS depends on the particular requirements of your application, and the performance comparison between the two is context-dependent. Large-scale, unstructured data scenarios with a high read volume are ideal for MongoDB, which excels at horizontal scalability and high availability.

RDBMSs are ideal for applications requiring precise data integrity and intricate transactions because of their structured data model and ACID characteristics. Developers must carefully consider the type of data they have, the expected read/write workload, and their scalability and high availability needs when choosing between MongoDB and RDBMS.

Query patterns and indexing techniques should also be considered to maximize the performance of either database management system. A thorough performance comparison between MongoDB and RDBMS may be found here.

High availability and scalability

MongoDB: MongoDB offers exceptional scalability, allowing data to be distributed across multiple servers through horizontal scaling. This capability ensures that applications can effortlessly handle growing data volumes and high traffic while maintaining optimal performance. Scaling up by adding more servers to the cluster is seamless, ensuring smooth expansion as data needs increase.

Furthermore, MongoDB's built-in high availability is provided through replica sets. These sets consist of multiple MongoDB instances that maintain identical data copies. In the event of a primary node failure, replica sets automatically switch over, ensuring uninterrupted data availability and enhancing fault tolerance.

RDBMS: RDBMS has historically relied on vertical scaling to enhance a single server's hardware resources to increase performance. Although vertical scaling can help with performance to some degree, it might not be able to handle very big datasets or achieve real horizontal scalability.

Regarding high availability, RDBMS is easier to adopt than MongoDB because it doesn't always involve the implementation of failover settings or third-party clustering solutions.

Indexing

MongoDB: MongoDB supports single-field, compound, multi-key, and geographic indexes, among other index types. Properly indexing collections can greatly improve query performance, particularly in workloads with high reading content. For sophisticated aggregate queries to be efficiently executed, MongoDB's indexing capabilities are essential.

RDBMS: RDBMSs enable developers to build indexes on particular columns, which speeds up query processing. RDBMS indexes can improve select query performance, particularly when filtering and merging data from several tables.