What are MySQL and MongoDB
MySQL and MongoDB are two database management systems that can be used to store and manage data. MySQL is a relational database system that stores data in a structured tabular format. In contrast, MongoDB stores data as JSON documents in a more flexible format. Both provide performance and scalability, but they provide better performance for different application scenarios.
MySQL is a relational database management system that uses Structured Query Language (SQL) to manipulate data. SQL is a general-purpose, standardized, and declarative language that can define data structures, constraints, operations, queries, and more. MySQL uses a table to store data. A table consists of rows and columns. Each row represents a record, and each column represents an attribute. Tables can be associated through primary keys and foreign keys to achieve data integrity and consistency.
MongoDB is a non-relational database management system that uses documents to store data. Documents are a JSON-like format consisting of key-value pairs, each of which represents an attribute. There is no fixed structure between documents, properties can be added or removed flexibly as needed. Documents are stored in collections, which are similar to tables but do not have a predefined schema. Collections can be associated by reference or embedding to achieve data flexibility and efficiency.
similarities
Both MySQL and MongoDB are database management systems. They store data and have built-in user interfaces and query languages so we can all add, edit, modify, and analyze data.
open source license
Both MySQL and earlier versions of MongoDB have open source licenses, and open source versions are available for free download. We can modify it according to what we need to do with the code. MySQL uses the GPL agreement, so that anyone can use MySQL for free and modify its code. On October 16, 2018, MonogoDB changed the license to the SSPL agreement, which has no impact on the open source community, but for cloud vendors, MongoDB will explicitly require cloud vendors hosting MongoDB instances to either obtain a commercial license from MongoDB, or Open source its service code to the community.
index support
MySQL and MongoDB use indexes to improve query speed and performance. An index is a database structure that speeds up data queries and helps to find and retrieve data very quickly. Both the MySQL and MongoDB database platforms use hash indexes, B-trees (MySql actually uses B+ trees) indexes, and several others.
User Interface
Both MongoDB and MySQL are easy to use. They provide a natural language-based query language to update and read data, and also provide a graphical user interface (GUI) for more intuitive management and analysis of data.
Programming language
MySQL and MongoDB are compatible with a variety of the same programming languages. Both MongoDB and MySQL can be used with Java, Python, Node.js, PHP, Ruby, and C#.
safety
Both MySQL and MongoDB use authentication, access control, and encryption to secure their databases. They use TLS/SSL encryption to protect data in transit and at rest, and also allow defining different user access levels.
Documentation and Community Support
Both MySQL and MongoDB have detailed official documentation on their respective websites. The tutorials, manuals, and guides for both contain complete instructions for installing, configuring, and running operational tasks. Both MongoDB and MySQL have an active developer community that can answer questions and help with troubleshooting. They also offer enterprise editions with dedicated support for specific requirements.
main difference
MySQL is a relational database management system and MongoDB is a NoSQL database system. MySQL uses SQL, and most developers have experience with it. Instead MongoDB uses the MongoDB Query Language (MQL). Despite the similarities between MQL and SQL, MQL usually requires extra effort to learn. Next, the blogger will introduce some of the main differences.
data model
MySQL is a relational database system that stores data in columns, rows, and tables. We store data in rows, with each column representing a different type of data. We can then define relationships between data using foreign keys and primary keys. Each table has a primary key that identifies it, and foreign keys are used to create relationships.
MongoDB is a document-oriented database that stores all its data as binary JSON (BSON) documents. BSON allows serialization of many forms of data. Unstructured, semi-structured, and structured data can be stored using BSON documents. Instead of using a database schema, MongoDB takes a flexible approach of storing documents in collections.
scalability
In the MySQL database system, the available scaling options are limited. The following options are available:
- Enables vertical scalability by adding more resources to the current database server
- Read replication by creating a read-only replica of the database on another server
There is a limit to creating institutional copies, a maximum of five copies. Replicas can also lag behind the master, causing consistency issues. Vertical scalability is also limited by the performance bottleneck of a single machine.
In contrast, MongoDB has a significant advantage in scalability. It has two key functions for scaling:
- replica set — a backup of a MongoDB server that contains the same data
- Sharding — distributing data across different servers
MongoDB allows the creation of sharded clusters, so parts of our data will be replicated across multiple servers. For example, if we have a large number of customer records, we can distribute them so that AJ's name and KZ's name are kept in their own replica sets. So MongoDB can scale out to optimize read and write performance at scale.
performance
MySQL is designed for high-performance joins between appropriately indexed tables. But it needs to insert data row by row, so write performance is slower.
MongoDB documents follow a hierarchical data model, keeping most of the data in a single document, reducing the need for joins across multiple documents. Joins are supported through the $lookup operation, but are not optimized for performance. However, MongoDB provides the insertMany() API for fast data insertion, giving priority to write performance.
flexibility
As a relational database management system, MySQL has a stricter structure than MongoDB. MySQL uses a fixed schema, organizing data into rows and tables. Data must be structured and put into a tabular system to use MySQL.
By storing data as JSON documents, MongoDB allows building complex applications with many different data types. For example, new fields can be created by updating nested array fields. Aggregation pipelines (which are a MongoDB feature) are also available, allowing data to be transformed by combining multiple operations into a single workflow.
Access control
In MongoDB, access permissions can be controlled at the operation, collection, or database level. It uses Kerberos, X.509, and LDAP certificates to authenticate users. In contrast, MySQL allows restricting user access at the user, database, and table levels. MySQL uses its own authentication system. It creates another security hole in SQL injection attacks that MongoDB's schemaless approach avoids.
Difference table
MongoDB MySql Data Model MongoDB stores data in JSON documents, which are then organized into collections. MySQL stores data in columns and rows. Data storage is tabular and relational. Scalability MongoDB uses replication and sharding to scale horizontally. MySQL uses scale-up and read replicas to improve performance at scale. Query Language MongoDB uses the MongoDB query language. MySQL uses SQL. Performance MongoDB is good at inserting or updating large numbers of records. MySQL is faster when querying large numbers of records. Flexibility MongoDB has no schema, so it has more flexibility and can handle unstructured, semi-structured and structured data. MySQL has a strict schema that handles structured data well. Security MongoDB uses Kerberos, X.509, and LDAP certificates to authenticate users. MySQL uses built-in authentication methods.
Advantages and disadvantages
MySQL and MongoDB have their own advantages and disadvantages, as shown below:
- Advantages of MySQL: Mature and stable: MySQL is a long-established, widely used, and tested database system with rich documentation and community support. Easy-to-use and powerful: MySQL provides an easy-to-use and powerful SQL language that can implement complex queries and operations. Data security: MySQL supports ACID transactions, which can ensure data integrity and consistency, and avoid data loss or error.
- Disadvantages of MySQL: Low flexibility: MySQL needs to pre-define the structure and type of data, and is not suitable for storing dynamically changing or unstructured data. Poor scalability: MySQL only supports vertical expansion and is not suitable for processing massive or distributed data. Performance bottlenecks: When MySQL handles a large number of operations such as connections, concurrency, indexes, and associations, performance bottlenecks or failures may occur.
- Advantages of MongoDB: High flexibility: MongoDB uses a document model that can dynamically adjust the structure and type of data, suitable for storing dynamically changing or unstructured data. Good scalability: MongoDB supports horizontal expansion, can handle massive or distributed data, and improve system availability and fault tolerance. Efficient performance: MongoDB can achieve efficient performance and throughput when dealing with a large number of read and write, indexing, embedding and other operations.
- Disadvantages of MongoDB: Difficult to use and complex: MongoDB provides an MQL language that is difficult to use, complex and has limited functions, and cannot implement complex queries and operations. Data insecurity: MongoDB does not support ACID transactions, and cannot guarantee data integrity and consistency, which may result in data loss or errors.
Application Scenario
MySQL and MongoDB are suitable for different application scenarios, the following are some main examples:
MySQL is suitable for the following scenarios:
- The data storage format in MySQL makes it suitable for data warehousing and online analytical processing. It complies with the ACID standard, which makes MySQL suitable for complex transactions, such as in e-commerce and transaction application scenarios.
- Scenarios that need to ensure data security, consistency, and reliability, such as government and financial fields.
MongoDB is suitable for the following scenarios:
- MongoDB is a better database for handling unstructured data in application scenarios such as social networking, media, or the Internet of Things (IoT).
- Scenarios that require a large number of operations such as reading and writing, indexing, and embedding, and require scalable and changeable data, such as content management, personalized recommendations, and other fields.