MongoDB vs MySQL -- which is the best database?

Databases are the memory block of every application or website, where data are stored and retrieved when needed. When creating a new project, developers usually debate which database to use. This debate is mainly over MongoDB and MySQL, two of the most in-demand and competitive database services for applications and websites. In this article, we are going to learn about MongoDB and MySQL. The core difference between these two database systems is quite significant, and choosing which one to use is a question of approach rather than purely a technical decision. We will also look at the differences between MongoDB and MySQL, their features, which one to choose, and when to use them. In short, at the end of this article, you will have a good understanding of the fundamentals of MongoDB and MySQL.

What are databases?

Before we dive into these databases, we have first to know at least what a database is. A database is an organized collection of structured information, or data, typically stored electronically in a computer system in such a way that it can be easily accessed, retrieved, managed, and updated. There are two main database types: Relational database and Non-relational database.

A relational database is a type of database that stores and provides access to data points related to one another. A relational database organizes data into tables containing information about each entity and representing pre-defined categories through rows and columns. A relational database uses Structured Query Language (SQL) to store, manipulate and retrieve data. Examples of relational databases are MySQL, PostgreSQL, Oracle Database, MariaDB, and Microsoft SQL server.

On the other hand, a non-relational database, also known as NoSQL (Not Only SQL), is a type of database that is used to store and retrieve data in a non-tabular form. Examples of Non-relational databases are MongoDB, Apache Cassandra, Redis, Couchebase, and Apache base.

What is MongoDB?

MongoDB is a powerful free-to-use non-relational database system that uses NoSQL queries while providing flexibility and scalability. It is a popular NoSQL database that stores data in a JSON-like document called BSON (Binary JavaScript Object Notation).

MongoDB is typically for designing specialized data sets through document compression and can adapt to data variation. Instead of storing data in rows and columns like a traditional database, it takes a document-oriented design that represents data in various JSON-like documents and collections. These documents contain a series of key/value pairs of different types, such as nested documents and arrays. The key/value pairs can be structured differently from one document/collection to another. MongoDB uses dynamic schemas that eliminate the need to pre-define the structure; this means you can create records without first defining the structure.

MongoDB is a scalable, flexible NoSQL document database platform designed to overcome the relational databases approach and the limitations of other NoSQL solutions. MongoDB is well known for its horizontal scaling and load-balancing capabilities, which have given application developers unprecedented flexibility and scalability. Companies that use MongoDB are;

• Twitter
• Sony
• Survey Monkey
• Klout
• T-mobile
• Invision
• Zendisk

Let’s now discuss MongoDB’s features.

Ad-hoc queries

When designing the schema of a database, it is impossible to know in advance all the queries that end users will perform. An ad-hoc query is a short-lived command whose value depends on a variable. Each time an ad-hoc query is executed, the result may differ depending on the variables in question.

Optimizing how ad-hoc queries are handled can make a significant difference at scale, when thousands to millions of variables may need to be considered. This is why MongoDB, a document-oriented, flexible schema database, stands apart as the cloud database platform of choice for enterprise applications that require real-time analytics. The performance improvement can be game-changing with ad-hoc query support, allowing developers to update ad-hoc queries in real-time.

MongoDB supports field queries, geo queries, and regular expression searches. Queries can return specific fields and also account for user-defined functions. This is made possible with MongoDB indexes, BSON documents, and the MongoDB Query Language (MQL). MongoDB also supports aggregations via the Aggregation Framework. This is an amazing feature that makes MongoDB stand out. Ad hoc queries function in a way to better the performance and are real-time.

Replication

Data replication is the process of storing the same data in multiple locations to improve data availability and accessibility and to improve system reliability and durability. One common importance of data replication is for disaster recovery, to ensure that an accurate backup exists at all times in case of a catastrophe or a hardware failure.

In MongoDB, a primary server or node accepts all write operations and applies those same operations across secondary servers, replicating the data. If the primary server should ever experience a critical failure, any secondary servers can be elected to become the new primary node. And if the former primary node returns online, it does so as a secondary server for the new primary node.

Schema-less Database

The schema of a database describes the structure of the data to be stored. In a relational database, the schema defines its tables, the fields in each table, and the relationships between each field and each table. The data stored needs to comply with the structure defined (tables, columns, data types, and relations). So every register in a table has the same number of columns and format.

In document-oriented databases (like MongoDB), data is stored in collections of key-value pairs. The key is a unique identifier for the register and the value it’s a document (JSON, XML, YAML, etc.). This means that a MongoDB database can contain multiple documents in a single collection, and these documents can consist of varying numbers of fields, content, and sizes. One document doesn’t have to look like another like in a relational database. There are no constraints for the data stored, so every document in the collection can have different attributes and formats.

Example:

In the above example, you can see that the two documents have slightly different keys. We have city and company keys in the second record. This is possible because of the schema-less feature, which gives MongoDB great flexibility for your database.

Indexing

Without the right indexes, a database is forced to scan documents one by one to identify the ones that match the query statement. But if an appropriate index exists for each query, the server can optimize user requests. MongoDB offers a broad range of indexes and features with language-specific sort orders that support complex access patterns to datasets.

MongoDB indexes can be created on demand to accommodate real-time, ever-changing query patterns and application requirements. They can also be declared on any field within your documents, including those nested within arrays.

Load-balancing

Database load balancing distributes concurrent client requests to multiple database servers to reduce the load on any single server. This can improve the performance of your database drastically. Fortunately, MongoDB can handle multiple clients’ requests to read and write the same data simultaneously by default. It uses some concurrency control mechanisms and locking protocols to ensure data consistency.

In this way, MongoDB also ensures that all the clients get a consistent view of data at any time. Because of this built-in feature of handling requests from multiple clients, you don’t have to worry about adding an external load balancer on top of your MongoDB servers.

What is MySQL?

MySQL is a free, full-featured open-source relational database management system ( RDBMS ) created in 1995 by MySQL AB, a Swedish company whose founder was Swedes David Axmark, Finland Swede Michael Widenius, and Allan Larsson. However, Sun Microsystems bought MySQL AB, and in 2010, Oracle acquired Sun Microsystems.

| Customer ID | CustomerName       | Address                     | City       | Country |
| ----------- | ------------------ | --------------------------- | ---------- | ------- |
| 1           | John Wayne         | 8236 Columbia Rd.           | New york   | USA     |
| 2           | Aaron Cook         | 17 Canal street             | Manchester | UK      |
| 3           | Antonio Moreno     | 190B Faulks Rd              | Mexico     | Mexico  |
| 4           | Hugo Francisco     | 1 Antonio Larrazabal street | Madrid     | Spain   |
| 5           | Alfred Futterkiste | 16 Rosenthaler Str.         | Berlin     | Germany |

MySQL follows a specified schema of tables, unlike MongoDB, which is document-oriented and schema-less. MySQL uses Structured Query Language to access and retrieve data stored in rows and columns. MySQL operates through the master-slave approach, where data replication and backup are possible, making it very reliable. Companies that use MySQL are;

• Pinterest
• Twitter
• Youtube
• Netflix
• Spotify

Let’s now see MySQL’s features.

Open-source and compatible

This simply means that anyone can install and use the basic software while enabling third parties to modify and customize the source code. More advanced versions, which offer additional capacity, tools, and services, come with tiered pricing plans. MySQL is also built to be highly compatible with a wide range of systems, programming languages, and database models. This includes alternative DBMS solutions, SQL and NoSQL databases, and cloud databases. MySQL also has extensive database design and data modeling capabilities (e.g., conceptual or logical data models). This makes it a simple and practical option for many organizations while reducing fears of being ‘locked in’ to the system.

Replication

MySQL replication is a process that enables data from one MySQL database server (the master) to be copied automatically to one or more MySQL database servers (the slaves). It is usually used to spread read access on multiple servers for scalability, although it can also be used for other purposes, such as for failover or analyzing data on the slave in order not to overload the master. As the master-slave replication is a one-way replication (from master to slave), only the master database is used for the write operations. In contrast, read operations may be spread on multiple slave databases. This means that if master-slave replication is used as the scale-out solution, you need to have at least two data sources defined, one for write operations and the second for read operations.

Security

This is always an important consideration for businesses as they need to protect sensitive data and defend against cyberattacks. MySQL supports encrypted connections between clients and servers using the TLS (Transport Layer Security) protocol. TLS uses encryption algorithms to ensure that data received over a public network can be trusted, and it has a mechanism to detect data change, loss, or replay. MySQL secures your data by encrypting data in transit with Transport Layer Security which is enforced by default.

Client/Server Architecture

Client-server architecture is a distributed application framework dividing tasks between servers and clients, which either reside in the same system or communicate through a computer network or the Internet. The client relies on sending a request to another program to access a service made available by a server. The server runs one or more programs that share resources with and distribute work among clients. In this case, our web server is a “Client” that connects and reads/writes to/from a database server (MySQL).

MySQL client/server architecture system involves the primary database MySQL Server and multiple clients – application programs. MySQL Server creates the database and defines the relationship of each table. The clients communicate with the server to request and manipulate the data with specific SQL commands. The tasks may include querying the data, adding and saving changes, etc. The server application provides the requested information on the clients’ side.

The server and clients can be on the same computer or on different machines. In the latter case, they communicate via the network (local or the Internet).

Indexing

Indexing is a powerful feature of MySQL that can be leveraged to get a faster response time from common queries. In MySQL, an index is a data structure that finds rows quickly. MySQL queries achieve efficiency by generating a smaller table, called an index, from a specified column or set of columns. This feature helps MySQL maximize its efficiency when carrying out query operations.

Differences between MongoDB and MySQL

In other to know which is better than the other and why we have to make a deep comparison between both databases to have an answer to this question.

Data Querying

MongoDB query is a method of retrieving data from a MongoDB database. MongoDB queries can easily retrieve data from a database, similar to SQL queries in the SQL database language. When performing query operations, you can also use criteria or conditions that can be used to retrieve specific data from the database.

MongoDB provides the function name as db.collection_name.find() to perform query operations on the database. MongoDB supports CRUD operations(Create, Read, Update, Delete), data Aggregation, text search, and Geospatial queries. Some of these queries are;

• insertOne() : This query method is used to insert a single document.

Syntax:
    db.collection_name.insertOne(Object)

Example;
        db.posts.insertOne({
            "title": "Post Title one",
            "body": "Body of post",
            "category": "Tech News"
         });

• insertMany() : This method is used to insert many documents at once

Syntax:
    db.collection_name.insertMany(object)

Example;
    db.posts.insertMany([
        {
            "title": "Post Title one",
            "body": "Body of post",
            "category": "Tech News"
        },
        {
            "title": "Post Title two",
            "body": "Body of post",
            "category": "News"
        },
        {
            "title": "Post Title three",
            "body": "Body of post",
            "category": "Event"
        },
    ]) 

• find() : This command is used to select data from a collection. This method accepts a query object if left empty, that is it will return all documents in the collection.

Syntax:
    db.collection_name.find()

 Example:
    db.posts.find()

To query or filter data, you simply specify the document.

Syntax:
    db.collection_name.find(object)  

Example:
    db.posts.find({ "category": "Tech News" })

• updateOne() : This method is used to update the first document that is found to match the provided query.

Syntax:
    db.collection_name.updateOne(object) 

Example:
    db.posts.updateOne({ "title":" Post title One"}, {$set: {"body":"Body of post updated"}})

MySQL uses structured Query Language(SQL) like any other Relational Database. MySQL uses dynamic commands to query, filter, sort, join tables, group, and modify data. These commands include;

| PostID | Title          | Body         | Category    | Likes |
| ------ | -------------- | ------------ | ----------- | ----- |
| 1      | Post title one | Body of post | World News  | 9     |
| 2      | Post title two | Body of post | Crypto News | 5     |

• SELECT: The SELECT statement is used to select data from a database. The data result is stored in a result table called the result set.

Syntax
    SELECT column1, column2 ... FROM table_name; 

Example
    SELECT Title, Body, Likes FROM Post

Result:

| Title          | Body         | Likes |
| -------------- | ------------ | ----- |
| Post title one | Body of post | 9     |
| Post title two | Body of post | 5     |

• INSERT TO: The INSERT TO method is used to insert new records in a table.

Syntax:
    INSERT TO table_name values (value1, value2, value3, ...)

Example:
    INSERT TO Posts ( PostID, Title, Body, Category, Likes ) VALUES ( 3, "Post title three", "Body of post", "Tech News", 2)

Result:

| PostID | Title            | Body         | Category    | Likes |
| ------ | ---------------- | ------------ | ----------- | ----- |
| 1      | Post title one   | Body of post | World News  | 9     |
| 2      | Post title two   | Body of post | Crypto News | 5     |
| 3      | Post title three | Body of post | Tech News   | 2     |

• WHERE: The WHERE option is used to filter records

Syntax:
    SELECT column1, column2 ... FROM table_name WHERE condition

Example:
    SELECT PostID, Title, Body, Category From Post WHERE Likes > 4

Result:

| PostID | Title            | Body         | Category    |
| ------ | ---------------- | ------------ | ----------- |
| 1      | Post title one   | Body of post | World News  |
| 2      | Post title two   | Body of post | Crypto News |
| 3      | Post title three | Body of post | Tech News   |

• UPDATE: The UPDATE command is used to modify an existing record in a table.

Syntax:
    UPDATE table_name SET column = value1, column2 = value2, ... WHERE condition

Example:
    UPDATE Posts 
       SET Body='Body of post updated',Likes = 5
       WHERE PostID = 3;

Result:

| PostID | Title            | Body                 | Category    | Likes |
| ------ | ---------------- | -------------------- | ----------- | ----- |
| 1      | Post title one   | Body of post         | World News  | 9     |
| 2      | Post title two   | Body of post         | Crypto News | 5     |
| 3      | Post title three | Body of post updated | Tech News   | 5     |

• ORDER BY: The ORDER BY option is used to sort the result set in ascending or descending order. The ORDER BY keyword sorts the records in ascending order by default. To sort the records in descending order, use the DESC keyword

Syntax:
    SELECT column1, column2 ... FROM table_name ORDER BY column1, column2, ... ASC|DESC

Example:
    SELECT * FROM Posts ORDER BY Category

Result:

| PostID | Title            | Body         | Category    | Likes |
| ------ | ---------------- | ------------ | ----------- | ----- |
| 2      | Post title two   | Body of post | Crypto News | 5     |
| 3      | Post title three | Body of post | Tech News   | 5     |
| 1      | Post title one   | Body of post | World News  | 9     |

Data Storage

MongoDB is a document-oriented database management system. Documents in MongoDB are stored in BSON format, which allows for the storage of binary data, which is useful for storing images, videos, and other binary data. Each collection comprises a number of documents, each of which contains the fundamental units of data in the form of key-value pairs.

Example of how data is stored in MongoDB

{
    "First Name": "Wisdom",
    "Last Name": "Woke",
    "Stack": "Front-end"
}

MySQL is a relational database management system (RDBMS). It uses a structured query language (SQL), representing data in pre-defined tables and rows. MySQL uses the JOIN operator to retrieve data from related tables.

Example of how data is stored in MySQL

| First Name | Last Name | Stack      |
| ---------- | --------- | ---------- |
| Wisdom     | Woke      | Front-end  |
| John       | Cena      | Back-end   |
| Louis      | Armstrong | Full-stack |

MySQL also provides different storage engines to store various data types. Refer to MySQL Storage Engines

Security

Data security is an essential feature customers and clients would like to look out for in every application. MongoDB makes use of role-base access control (RBAC) with a versatile range of privileges. Its security features include authentication, auditing, and authorization. It is also possible to use Transport Layer Security (TLS) and Secure Sockets Layer (SSL) to encrypt data.

MySQL uses a security model that is a privilege-based security model (PBSM). It authenticates users and facilitates privileges of access to databases such as CREATE, SELECT, INSERT, UPDATE, etc. MySQL uses encrypted connections between clients and the server using SSL.

Performance

MongoDB performs excellently regarding unstructured data, and it is relatively fast compared with MySQL because of its document-based data storage.

MySQL performs great in transactional operations. However, you can observe slow performance when the data volume gets huge. The reason is that the tables are stored in a normalized format, and to fetch or change data, it needs to go through lots of tables which increases the load on the server and affects the performance. Read replication involves adding read-only copies of the database to other servers. However, this is usually limited to five replicas which can only be used for read operations. This can cause problems with applications that must write a lot or frequently write and read to the database, as replicas often lag behind the write master. MySQL also supports multi-master replication.

Scalability

In MongoDB configuring a sharded cluster allows a portion of the database, called a shard, to also be configured as a replica set. In a sharded cluster, data is distributed across many servers. This highly flexible approach allows MongoDB to horizontally scale both read and write performance to cater to applications of any scale. A replica set is the replication of a group of MongoDB servers that hold the same data, ensuring high availability and disaster recovery.

MySQL allows data replication and vertical scaling through clustering to help improve application scalability and performance. Vertical scaling is the process of upgrading the load capacity of server hardware by increasing IOPS (input/output operations), amplifying CPU/RAM capacity, as well as disk capacity.

One-on-one Comparison

Why use MongoDB?

• It is flexible and adaptable to real-world situations and requirements.
• It supports the replication of the database.
• It is best used when high data availability and automatic, fast, and instant data recovery are needed.
• It is also the best option when it comes to cloud-based applications.
• It is also used when dealing with big data across multiple geographical locations.
• It can store any type of file, any size, without affecting the stack.

Why use MySQL?

1. It is portable and can connect to a secondary database easily.

2. It can store relational data.

3. It is easily configured and managed.

4. It has excellent reliability.

5. It has an exceptional data security level.

6. It has a huge community; the popularity and effectiveness of MySQL can be judged by looking at its huge community support.

When to use MongoDB and MySQL?

MongoDB and MySQL have different features that make them suitable for different use cases.

When to use MongoDB?

• It is suitable for web applications requiring a large amount of data integration.
• It will be the preferred option if you have an unstable schema
• It is best used in applications with semi-structured or unstructured datasets.
• It is best suitable for cloud-based computing.

When to use MySQL?

• It would be the best option when it comes to data security.
• It is well suited for web applications with fixed schema and structure.
• It is the best option when looking for a database that can be customized (this is because it is an open-source database).
• It is ideal if your application requires a high transaction rate.
• It would be the best choice if you want high performance on a limited budget

Conclusion

In conclusion, it is a mistake to assume that one system leaps beyond the other in performance and responsiveness. Both MongoDB and MySQL perform fast, and both are powerfully designed DBMSs. Just like when a new project is to be created there is a lot of programming language which can be used to work on the project, but instead, some are chosen over others because they best suit the use case. Hence when it comes to data security and consistency, which is suitable for applications involving transactions, financial-based application MySQL will be the ideal choice, while when flexibility and scalability are considered for applications with changing requirements and rapid growth MongoDB would be the best choice.

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