Incorporating a database management system (DBMS) allows you to efficiently store and manage large volumes of data. With its capabilities, you can easily conduct analytical and transactional operations.
To perform these tasks, you can leverage two types of DBMS—relational and non-relational. Each type has multiple solutions available in the market, the most commonly used being MongoDB and MySQL. MongoDB is one of the most popular non-relational database systems, whereas MySQL is a relational database system.
This guide will help you understand the differences between MongoDB vs MySQL and decide which DBMS best suits your workflow.
MongoDB: An Overview
MongoDB is an open-source NoSQL database management system that allows you to store large amounts of semi-structured and unstructured data. Unlike relational databases, MongoDB uses the Binary JavaScript Object Notation (BSON) format to store data.
In MongoDB, each document—a data structure composed of key-value pairs—in a collection can be verified with the help of an ID or the primary key. The primary key represents unique values in the document. To manage and interact with this data, you can use MongoDB Query Language (MQL), a robust language.
Key Features of MongoDB
- Ad-hoc Queries: These are short-lifespan commands that rely on a variable, such that each execution of a query will result differently depending on the variable. MongoDB enables you to optimize ad-hoc queries to significantly improve performance at scale.
- Indexing: With appropriate indexing, the server can optimally execute user queries. MongoDB allows you to create indices in real-time to accommodate your specific requirements. These indices will help you improve query performance and search speed.
- Sharding: MongoDB lets you distribute data to enhance complex query performance, which might otherwise be time-consuming. Sharding is the principle that helps you to split larger datasets across distributed components (shards). This allows you to scale your applications to handle growing business demands.
- Replication: Distributing data across multiple servers is considered an essential operation as it eliminates potential issues when a server crashes or any hardware failure occurs. For data replication, MongoDB employs replica sets. In case a failure occurs in the primary server, the secondary server gets elected as the new primary node.
- Load Balancing: Managing millions of client requests with thousands of servers leads to enhancement in performance. With horizontal scaling, MongoDB can easily support large-scale load balancing. The platform allows you to handle multiple read and write requests for the same data with robust locking protocols and concurrency control, ensuring data consistency.
MySQL: An Overview
Developed by Oracle, MySQL is an open-source relational database management system (RDBMS). Like other RDBMS, it allows you to store data in tabular format, enforce referential integrity, and access data using structured query language (SQL).
In MySQL, you must define database schemas before performing queries, and the data you store must match this schema. This working principle prioritizes safety over flexibility, as storing a new data format requires you to perform complex and time-consuming schema change operations.
Key Features of MySQL
- Replication: MySQL lets you replicate data from one server to multiple servers, spreading the load among multiple replicas to improve performance. In this case, reads can occur on numerous servers, but writes and updates happen on the source server.
- Clustering: MySQL offers an NDB cluster, a distributed database with linear scalability and high availability. This cluster is designed for mission-critical workloads, providing you with in-memory, real-time access while maintaining transactional consistency across distributed and partitioned datasets.
- Security: With multiple security features, MySQL safeguards your data from unauthorized access. These features include encryption, access control, and authentication. To secure data at transit and rest, it supports an SSL-encrypted connection between the server and the client.
- Backup: MySQL has multiple backup options, such as non-blocking, partial backup, incremental, and streaming, among others, to help you protect your data from damage. In addition, data recovery features like parallel apply-log, partial restore, and direct restore let you retrieve the lost information stored in the backup.
- Monitoring Server Execution: Performance Schema is a feature that enables you to monitor server execution at a low level while having minimum impact on performance.
MongoDB vs MySQL Server
Let’s explore the differences between MongoDB and MySQL through a brief comparison table.
MongoDB vs MySQL Benchmark: A Detailed Comparison
When differentiating MySQL vs MongoDB, several factors contribute to the selection of the perfect tool that best fits your organizational requirements. Let’s explore the most common criteria that you must consider to decide which database suits you the best.
MongoDB vs MySQL: Scalability
MongoDB offers multiple scalability features, including sharding, which allows you to distribute data across various servers. This feature is commonly used for horizontal scaling, which is required to handle increasing data volumes.
MySQL, on the other hand, provides limited scalability. However, it offers additional services like the NDB Cluster, which supports sharding for horizontal scaling. Moreover, one of MySQL’s versions, Enterprise Edition, offers Thread Pool, a highly scalable thread-handling model. With this model, you can serve more than a million users subsequently.
MongoDB vs MySQL: Availability
In MongoDB, high availability is achieved through replica sets that store copies of datasets. This replication process allows you to keep copies of data, providing fault tolerance and redundancy. If the primary server fails, the replica can serve the purpose of making the data available for use.
MySQL also supports replication, enabling you to replicate all the databases, selected databases, or tables within a database. It offers three types of replications: row-based, statement-based, and mixed-based.
MongoDB vs MySQL: Query Language
MongoDB supports MQL (MongoDB Query Language), a proprietary language built for data retrieval operations. With its functionality, you can handle semi-structured and unstructured data. If you are familiar with programming languages like Java or node.js, MongoDB can be a great DBMS choice. Contrarily, MySQL is a better choice if you have experience working with SQL. It lets you design database schema and store structured data in tabular format.
MongoDB vs MySQL: Performance
MongoDB follows a hierarchical data model in which join operations are less commonly used. This model allows storing documents containing nested data structures, keeping the related information together rather than spreading it across tables. Storing the data this way makes MongoDB a better platform for performing write operations.
On the contrary, MySQL is optimized for high-performance joins across tables with appropriate indexing. Adding data to the database requires you to insert records row-by-row, which degrades MySQL’s performance while inserting and updating the rows. However, MySQL is faster at selecting a large number of records at once.
MongoDB vs MySQL: Flexibility
One advantage of using MongoDB is its schema-less approach. This approach offers flexibility for storing different types of data without needing a fixed schema. This flexibility must be the key reason why 5804 developers working with MySQL in 2024 want to switch to MongoDB. In contrast to relational databases, MongoDB doesn’t require you to design schema as it primarily deals with data in key-value pairs. This feature makes it easy for you to build applications with higher flexibility.
In contrast, MySQL requires you to define a schema first, leaving you with less flexibility to deal with different data formats. To store data that doesn’t match the database schema, you must modify the database’s structural framework, which is often an expensive and time-consuming process.
MongoDB vs MySQL: Security
MongoDB provides role-based access control (RBAC), where users are assigned roles to access and perform functions on the database. In addition, all the communication in MongoDB is encrypted with transport layer security (TLS). With Queryable Encryption, you can encrypt sensitive data, store it in the database, and run queries without decryption, securing the data throughout its lifecycle.
MySQL, in hindsight, offers robust security features, including auditing, encryption, and access control. It also provides privileges with access roles, giving you permission to conduct database operations on particular datasets.
Effortlessly Migrate Data to Any Database with Airbyte
After selecting the best available database for your use case, consolidating information can help you enhance data accessibility and management. You can leverage a no-code tool like Airbyte to streamline your data integration requirements.
Airbyte is a data replication platform that allows you to migrate data from multiple sources to your preferred destination. It offers 400+ pre-built data connectors, providing you the flexibility to choose between numerous platforms. However, if the platform you are looking for is unavailable, Airbyte enables you to create custom connectors using the Connector Development Kit (CDK).
Here are the key features that Airbyte provides:
- Developer-Friendly Pipelines: PyAirbyte is an open-source Python library offered by Airbyte. It allows you to use pre-built Airbyte connectors to extract data from multiple sources within your Python environment.
- Schema Management: For efficient data synchronization, Airbyte allows you to manage schema changes in the source for each connection. This feature also enables you to refresh the schema anytime to handle changes in source data.
- Deployment Flexibility: It has three deployment options: Self-Managed, which can be deployed locally; Cloud-hosted, where Airbyte manages infrastructure; and Hybrid, a combination of both.
- Change Data Capture (CDC): Airbyte’s CDC feature lets you identify the incremental changes made to the source dataset and replicate them into the destination, ensuring data consistency.
- RAG Transformations: With Airbyte, you can utilize LLM frameworks like LlamaIndex or LangChain to perform complex RAG transformations, such as chunking and indexing. This capability helps you to simplify the development of LLM applications.
These features help you reduce manual intervention and migrate data from your in-house or cloud storage into a database like MongoDB or MySQL. If you are already working with one of these tools but wish to switch, you can use Airbyte to sync MongoDB to MySQL or vice versa. Furthermore, the pre-built MongoDB connector in Airbyte provides the CDC synchronization speed of 10MB/s to replicate updates.
Conclusion
Enhancing workflow requires you to choose the DBMS that resonates with your daily tasks. Both tools that you have studied are efficient in creating, managing, and updating databases using create, read, update, and delete (CRUD) operations.
To select any of the tools between MongoDB vs MySQL, you must thoroughly understand the data format and the flexibility you need to manage it. MongoDB is an effective and flexible platform for managing structured, semi-structured, and unstructured data. Alternatively, MySQL provides a rigid schema to store your data but enables you to easily select large records.