Top Differences MongoDB Vs MySQL
Databases are embedded in every transaction of organizations, and even for the people who own computers or cell phones, access them frequently either knowingly or unknowingly. Right from when we pull out money from ATMs, check our savings account, shop online on an e-commerce portal, view social media platforms, or perform any digital interactions, a database is involved.
Databases have made a lot of progress since their inception in the early 1960s, and at first, they were used as a solution to help store and retrieve data and make it more accessible at a relatively faster speed. All the databases traditionally used structured query language (SQL) to retrieve, manipulate, and store data. MongoDB online training helps you to learn more skills and techniques.
MySQL database has become a relatively economical option for organizations around the globe that needed a relational database when compared to many costly DBs like Oracle, Teradata, etc.
Recently, we have witnessed an increase in the variety and volume of data. So, non-relational databases or NoSQL data stores like MongoDB have become the go-to database to handle dynamism in business and meet the new needs of ever-changing architecture and design landscape of the applications. Let us look at some of the differences between these two databases in detail in the upcoming sections.
Internal Structure
MySQL DB: MySQL keeps its data in the form of rows and columns in tables and uses the SQL to access the data from the tables. This is similar to any relational database management systems like Oracle, MS SQL Server, etc. MySQL uses database schemas to outline its internal structure that requires the rows in the table to have the same structure as the values represented by its data type.
MongoDB: MongoDB stores the data in the form of JSON (JavaScript Object Notation) like format, i.e., BSON (binary-encoded format behind the scenes) that can have diverse data structures. BSON extends the JSON model and offers more data types, ordered fields, and more. Its implementation is lightweight and traversable.
To improve the query retrieval speed, MongoDB can gather and store the associated data together, which is then accessed by its very own query language (MongoDB query language). In contrast to MySQL DB, which has schema to represent its internal structure, MongoDB is schema-agnostic, thus allowing you to create formats or documents without having to define the internal structure of the format or document initially. These formats or documents can be easily changed by adding or deleting fields or otherwise called as key-value pairs.
Query Language
MySQL DB: The MySQL DB follows standard SQL like other RDBMS. See examples below,
Selecting from employee table:
Select * from employee;
Inserting to employee table:
INSERT INTO employee (emp_id, dept, status) VALUES ('1234', 'HR', 'A')
Updating employee table:
UPDATE employee SET dept = 'Finance' WHERE Empid = 1234;
MySQL can be vulnerable to SQL injection attacks as the values are directly passed to the DB instead of objects or documents, unlike MongoDB.
MongoDB: MongoDB uses its query language and let us see them below,
Selecting from employee table:
db.find.employee()
Inserting to employee table:
db.employee.insert ({emp_id:’1234’, dept:’HR’, status:’A’})
Updating employee table:
db.employee.update({Empid:{$eq:1234}},{$set{dept:’Finance’}},{multi:true})
As MongoDB uses object querying, where formats or documents are passed during runtime to query DB, it reduces the risk of attack as there is no SQL language used to parse and identify the data being passed across.
Replication / Clustering
MySQL DB: MySQL database supports master and slave replication and master-master replication similar to any other relational database management system. Multi-source replication in MySQL database also allows you to reproduce from several master databases in parallel. Replication lags can occur if master-slave configurations vary or a load of operations fluctuate.
MongoDB: MongoDB, on the contrary, supports inbuilt replication, sharding, and auto-elections. By the use of auto-elections, you can set up the secondary database that can spontaneously take over the functionality if the primary database fails at any point in time.
The concept of sharding (shared-nothing architecture) allows horizontal scaling, which is difficult to achieve in the MySQL database that supports only vertical scaling. MongoDB uses replicas to create multiple copies of the data stored internally. Each member of the replica can have the role of a primary or secondary database at any point in time. The database operations like reads, writes, and deletes are executed by default on the primary replica and then replicated onto the secondary replicas. Best MongoDB course from industrial experts.
Terminology and Concepts
MySQL DB: MySQL concept does not allow efficient replication and sharding, but one can access associated data using joins, which minimizes duplication.
MongoDB: MongoDB has been designed with high availability and scalability in mind and includes inbuilt replication and sharding.
The differences between the two databases have been explained below in DB terminology.
MySQL
MongoDB
ACID Transactions
ACID Transactions
Table
Collection
Row
Document
Column
Field
JOINs
$lookup & $graphLookup
GROUP_BY
Aggregation Pipeline
Security
MySQL DB uses a privilege-based security model. It authenticates any user and facilitates them with privileges on a specific database such as CREATE, SELECT, or INSERT. On the transport layer, MySQL DB uses encoded connections between clients and the server using SSL.
MongoDB Unlike MySQL DB, MongoDB uses a role-based authentication or access control with a flexible set of privileges. Its security bundle includes authentication, auditing, and authorization. It is also possible to use Transport Layer Security and Secure Sockets Layer for encoding and decoding purposes. This provision or facility ensures that the database is only accessible to the intended user or client.
Data Representation
MySQL DB: The difference between the way data is stored and maintained in both databases is relatively different. MySQL stores data in the form of rows of the table, just like any other relational database management system.
A record in MySQL DB would look like below,
Emp ID Emp Name Dept Status
1234 ABCD HR Active
MongoDB: MongoDB stores data in the form of JSON-like documents. An example below,
{
Emp_id: 1234,
Emp_name:”ABCD”,
Dept:”HR”,
Status:”A”
}
Scalability
MySQL DB: In many situations, most of the SQL databases like MySQL DB, Oracle, SQL Servers are vertically scalable, which means that you can increase the load on a single server by increasing CPU specifications or RAM.
MongoDB: But NoSQL databases, on the other hand, are horizontally scalable. This implies that you handle more traffic by sharding or adding more servers for your NoSQL database and create a MongoDB cluster with multiple nodes/servers. It is similar to adding more floorings to the same house as against adding more homes to the neighborhood. The MongoDB or NoSQL DB can eventually evolve and become more dominant, making it the preferred choice for large or ever-changing fluid data sets.
Benefits and Strengths
MySQL DB: MySQL is an extremely well-known database as there is a vast support community, and extensively being tested and for high stability. MySQL is available for all major operating system platforms like Linux, Windows, Mac, BSD, and Solaris. It also has adapters to multiple programming languages like Node.js, Ruby, C#, C++, Java, Perl, Python, PHP, etc. The database is open-source and can be replicated across multiple nodes as well.
MongoDB: MongoDB gives you the flexibility to change your schema without modifying any of your existing data. You can also add new columns or fields in a MongoDB collection without affecting application performance. It is user-friendly to a great extent, which both developers and administrators could use.
High Availability and Cloud
MySQL DB: For stable environments, MySQL DB provides better handling techniques. Taking a backup of MySQL is not required, as it already has a replication provision integrated within. However, setting up a master-slave is relatively slower compared to MongoDB.
MongoDB: For unstable environments, MongoDB offers an improved handling technique compared to MySQL DB. This is because it takes a smaller amount of time for the active secondary nodes to elect a new primary node in case of failures or unavailability of primary nodes. Besides, due to in-built native replication, creating a backup for a MongoDB database is relatively fast and easy when compared to MySQL as the latter has integrated replication support.
Also, cloud-based storage solutions require data to be smoothly and evenly spread across multiple servers for scaling up. MongoDB can load a high volume of data as compared to MySQL in a short time and with a built-in sharding feature, and it is straightforward to partition and spread out data across multiple servers.
Data Embedding
MySQL DB: MySQL DB does not provide the option of nesting or an embedding feature in the SQL query or format. You can do a JOIN on tables in MySQL, in which case, you may end up having larger tables with possibly unnecessary fields. JOIN operations are costly, time-consuming, and performance-intensive.
MongoDB: MongoDB provides a better embedding technique for related data. In MongoDB, you can decide to embed the data into a field or reference the data from another collection of data if you think that the document could potentially grow in size in the future. For example, if you want to collect the addresses of the employees and other information, you can easily have a simple structure like below in MongoDB,
{
id:1,
name:'ABCD',
gender: 'Male',
age:23,
address:{
City: 'New Jersey,
Street: 'London',
Zip_code: 11243
}
}
Companies that use MySQL & MongoDB
MySQL: Some of the internet companies like Pinterest, Twitter, YouTube, Netflix, Spotify, US Navy, NASA, Walmart, and PayPal use MySQL.
MongoDB: Citrix, T-Mobile, Zendesk, Sony, Hootsuite, SurveyMonkey, MuleSoft, Foursquare, and InVision use MongoDB.
Conclusion
Relational databases are being hard-pressed beyond their boundaries because of the way that the applications are being built today, coupled with an unprecedented rate of growth in data sources and users accessing the databases. MySQL and MongoDB both have their fortes and flaws. If your data requires multi-row manipulations or transactions or you have to upkeep a legacy system, a relational database like MySQL DB could be the right choice for your organization. MongoDB online course helps you to learn more skills and techniques.