Relational databases like MySQL and NoSQL databases like MongoDB are two of the most popular database solutions on the market today. But with different architectures, features, use cases, and more, it can be difficult to decide which one is right for your application.

In this comprehensive guide, we'll dig into all the key differences between MySQL and MongoDB to help you determine which database management system (DBMS) is the best fit for your needs.

A Quick Overview

First, let's briefly introduce MySQL and MongoDB at a high level:

MySQL is an open-source relational database management system (RDBMS) based on structured query language (SQL). It uses tables, rows, and columns to store and organize data. MySQL is a popular choice for web-based applications and runs on virtually all platforms.

MongoDB is a popular document-oriented NoSQL database. Instead of using tables and rows, MongoDB stores data in flexible JSON-like documents with dynamic schemas. This makes it highly scalable and capable of handling large volumes of unstructured or semi-structured data.

Now let's explore some of the key differences between these two database giants in more detail:

Data Models: Relational vs. Document-Oriented

One of the biggest differences between MySQL and MongoDB is their underlying data models:

  • MySQL is a relational database, which means data is stored in predefined tables, rows, and columns. Tables are related to each other via foreign keys.
  • MongoDB is a document database, which stores data in flexible JSON-like documents that don't adhere to a strict schema. Related data is nested within documents rather than split across tables.

Here's a simple example of how the same data would be stored differently in MySQL vs. MongoDB:

MySQL (relational):

MongoDB (document-oriented):

As you can see, MySQL splits related data into separate tables, while MongoDB nests related data within a single document.

Both models have their pros and cons that we'll explore more throughout this article.

Schemas: Static vs. Dynamic

Another key difference arises from the data models of MySQL and MongoDB:

  • MySQL requires a predefined schema that specifies the tables, columns, data types, constraints, relationships, and more. Changing the schema once data is in the database is often difficult and disruptive.
  • MongoDB has a dynamic schema that doesn't enforce a strict structure upfront. New fields and data types can easily be added to MongoDB documents on the fly.

The rigid schema of MySQL provides more data consistency and validation. But the flexibility of MongoDB's schema makes it easier to evolve applications and store dynamic data.

Query Languages: SQL vs. NoSQL

MySQL and MongoDB utilize very different query languages:

  • MySQL uses SQL (structured query language) for manipulating and querying data in tables. SQL is the standard language for relational databases that many developers are already familiar with.
  • MongoDB uses its own NoSQL query language that operates on document structures rather than tables/rows. The syntax is JSON-oriented and allows for dynamic queries, indexing, and aggregation.

For example, here is how you would query a users table in MySQL vs a users collection in MongoDB:


MongoDB (NoSQL):

Both languages are powerful in their own right but certainly differ in their approach and syntax. SQL is an ANSI standard that has been battle tested for decades. MongoDB's query language is relatively young but flexible.

Joins vs. Embedding/Linking

When it comes to connecting related data in your database, MySQL and MongoDB offer different methods:

  • MySQL uses joins to connect data across different tables. Joins are performed when querying by matching values like primary keys.
  • MongoDB relates data via embedding and linking. Related data can be nested within a single document or referenced from other documents via manual linking. Joins aren't natively supported.

Here is an example of using joins in MySQL vs. embedding in MongoDB:

MySQL (joins):

MongoDB (embedding):

Joins often provide better consistency and normalization. Embedding can improve read performance since related data is stored together.

Performance: ACID vs. Eventual Consistency

MySQL and MongoDB also differ in their approaches to performance and data consistency:

  • MySQL follows ACID semantics - transactions are fully Atomic, Consistent, Isolated, and Durable. This provides maximum data integrity but can impact performance with large volumes of writes.
  • MongoDB provides eventual consistency - data is eventually consistent across distributed nodes. This provides faster performance for writes/reads but leaves room for inconsistency in the short term.

If your application requires flawless data consistency, MySQL is likely the safer choice. For higher performance and availability at scale, MongoDB may be preferable.

Scalability: Vertical vs. Horizontal

Another significant difference is how MySQL and MongoDB are designed to scale:

  • MySQL can primarily scale vertically by increasing the CPU, RAM, storage, etc on a single server. Vertical scaling has limitations though - large MySQL deployments often require additional caching layers.
  • MongoDB scales horizontally using sharding to distribute data across many servers. Additional nodes can be added without much disruption or impact to performance. Horizontal scaling is more complex but allows for greater scalability.

If you anticipate rapid growth or needing to store massive volumes of data (terabytes+), MongoDB likely has the scalability advantage. MySQL's vertical scaling approach may be sufficient for smaller use cases.

High Availability: Clusters/Replicas

For mission critical systems requiring high availability:

  • MySQL offers multi-node cluster solutions like MySQL Cluster to provide replication, automated failover, and no single point of failure. MySQL Cluster handles node outages seamlessly.
  • MongoDB has built-in replication where copies of data are synced across different servers. If the primary node goes down, an eligible secondary will automatically become primary. Config servers act as arbiters to coordinate replicas.

Both MySQL clusters and MongoDB replicas aim to maximize uptime and eliminate downtime during server incidents. Each database offers some unique capabilities here like MongoDB's more flexible consistency controls.

When to Use MySQL vs. MongoDB

Now that we've compared MySQL and MongoDB across various criteria, let's discuss ideal use cases for each database:

When to use MySQL

  • Your data is structured and unchanging - MySQL performs best with predefined schemas and consistent data formats.
  • You need ACID compliance - MySQL guarantees maximum data integrity and reliability.
  • You want to use SQL - MySQL supports ANSI SQL, the standard relational query language.
  • Your app requires joins - MySQL can efficiently connect related data across tables via joins.
  • Transactions are important - MySQL has robust transaction support to group multiple operations into all-or-nothing units.

When to use MongoDB

  • You have unstructured, variable or rapidly evolving data - MongoDB's dynamic schemas and JSON documents can easily adapt.
  • You need greater scalability and availability - MongoDB makes it easier to scale out across many distributed nodes.
  • Your data is heavily nested or embedded - MongoDB can store related data together in single documents.
  • High write throughput is required - MongoDB provides excellent performance for write-heavy applications.
  • Geographic distribution is needed - MongoDB optional global clusters can span multiple data centers.

As you can see, there are many factors to weigh when deciding between MySQL vs. MongoDB. Consider your data structures, scalability needs, data consistency requirements, and application architecture when choosing a primary database.

In some cases, a hybrid approach is optimal. For instance, you could use MySQL as the primary transactional database and MongoDB for analytics, logging, or caching. Many organizations employ multiple databases where each excels at different tasks.

Example Scenarios for Using MySQL and MongoDB

To make the choice more concrete, here are a few common scenarios and which database typically provides the best fit:

Online Banking App

An online banking application that handles sensitive financial transactions and account data. It requires full ACID compliance, joins for looking up related account info, strong integrity controls, and crash proof durability.

Ideal database: MySQL

Product Catalog

An ecommerce site's product catalog containing thousands of items, each with various details like price, inventory count, vendor info, and related/recommended products. The catalog needs to be highly available and scalable.

Ideal database: MongoDB

User Profiles

A social media site storing user profiles with structured fields like name, address, email, etc as well as variable content like posts, photos, videos and comments. User data is retrieved frequently.

Ideal database: Hybrid with MySQL for structured user profile data and MongoDB to store variable user content.

Log Files

Log data like clickstreams, impressions, actions, errors, and telemetry from a high traffic web application. The logs are unstructured, high volume, and analyzed to find trends/patterns.

Ideal database: MongoDB

As you can see, there are good use cases for both MySQL and MongoDB when you factor in data models, queries, and scalability needs. Blending both in a polyglot persistence architecture allows you to get the best of both worlds.

Summary and Key Takeaways

MySQL and MongoDB represent two of the most popular open source database options. In summary:

  • MySQL is a relational database that uses SQL, predefined schemas, ACID compliance, and works well for structured data. It scales vertically well.
  • MongoDB is a document database with dynamic schemas, faster writes, and greater scalability via horizontal scaling. It works better for variable/nested data.
  • MySQL has joins to connect data across tables while MongoDB embeds or links related data within documents.
  • MySQL provides full ACID guarantees, while MongoDB offers eventual consistency for higher performance and availability.
  • Use MySQL when you need rigid schemas, joins, ACID compliance, and maximum data integrity.
  • Use MongoDB for more flexibility, nested data, easy scaling, higher write performance, and variable/evolving data.
  • Often a hybrid approach using both MySQL and MongoDB together is an optimal solution.

As you assess your database needs, consider how your data structures, queries, transactions, and scalability requirements align with the strengths and weaknesses of MySQL vs. MongoDB. This will help you select the best database management system(s) for your application architecture.

Both MySQL and MongoDB are extremely capable databases with fantastic communities and resources behind them. The good news is that you can't go wrong picking either one - as long as you factor in the key criteria covered in this guide.

Hopefully this overview dispelled some common myths and gave you clarity on MySQL vs. MongoDB. Armed with this knowledge, you can now confidently evaluate and choose the ideal database for your next project.

1. What are the key differences between MySQL and MongoDB architectures?

The most fundamental difference is that MySQL is a relational database whereas MongoDB is a document-oriented NoSQL database.

MySQL uses predefined schemas with tables, rows, and columns to store structured data. Related data is stored in separate tables and joined together when querying. MySQL adheres to ACID principles and transactions for data integrity.

MongoDB uses a document data model to store data in JSON-like documents rather than rows and columns. Related data can be nested within documents rather than split across tables. MongoDB provides eventual consistency across distributed nodes to support high availability and scalability.

2. When is MySQL the right choice over MongoDB (and vice versa)?

MySQL tends to be a better choice when you need:

  • Rigid schemas and strict data validation
  • Transactions and ACID compliance
  • Joins across normalized tables
  • Strong data consistency guarantees
  • Vertical scalability

MongoDB tends to be better when you need:

  • Flexible schemas for evolving data
  • High availability and scalability
  • Embedding of related data within documents
  • Tunable data consistency
  • Horizontal scalability

3. Can MySQL handle unstructured data as well as MongoDB?

MySQL is optimized for structured data but can also be used to store semi-structured data like JSON documents using column types like JSON or TEXT. However, this can result in more complex application logic to handle manipulating the documents. Joins would still be required to connect related data.

MongoDB is designed from the ground up to natively handle unstructured data in nested BSON document formats. The flexible schemas and powerful aggregation framework provide a lot more capabilities for efficiently querying and analyzing unstructured data at scale.

4. Does MongoDB support transactions and ACID compliance?

MongoDB has added support for multi-document ACID transactions since version 4.0. However, some limitations exist compared to the robust, battle-tested transactions in MySQL:

  • MongoDB transactions are limited to operations within a single collection
  • sharding complicates transactions across nodes
  • no support for cross-database transactions

The MongoDB transactions provide atomicity and isolation but still allow for some inconsistency across nodes. MySQL remains the gold standard for true ACID compliance and rock solid data integrity.

5. Can MongoDB handle complex joins or should denormalization be used instead?

MongoDB does not directly support joins like you would see in MySQL. Joins would need to be handled in application code which can impact performance.

Most MongoDB schemas are designed to avoid joins where possible through denormalization and embedding related data within documents. Lookup tables can also be used to reference documents in other collections.

For many-to-many relations, linking documents together is typically preferred over trying to join across collections.

6. What are the scalability limits of MySQL compared to MongoDB?

MySQL can scale vertically well to a point by increasing server resources. But high-end MySQL deployments often require add-ons like memcached to handle caching. Workloads with high writes can also limit MySQL's scalability.

MongoDB is designed to scale horizontally using sharding to distribute data across many servers. With sharding, MongoDB can handle very large datasets and high throughput workloads that surpass MySQL's limits. However, operational complexity also increases with MongoDB scale.

7. Does MongoDB support ad-hoc queries as well as MySQL?

MongoDB supports quite robust ad-hoc querying capabilities comparable to MySQL SQL:

  • Find queries with flexible filters equivalent to WHERE
  • Projections like SELECT with inclusion/exclusion
  • Limit, skip, sort, updates, deletes
  • Array operators like $unwind
  • Geospatial queries
  • Aggregation framework for analytics

The MongoDB query language is JSON-centric but allows expressing complex queries on documents without joins. Performance tuning may require adding indexes.

8. How reusable are MySQL database skills when transitioning to MongoDB?

Developers proficient in MySQL SQL will certainly find MongoDB's query language quite different syntactically. There are some conceptual similarities but the JSON document model requires thinking about queries differently.

That said, many core competencies like understanding indexes, data modeling, schema design, and database optimization translate well. MongoDB also offers BI connectors to allow using SQL queries. With some ramp up time, MySQL DBAs and developers can become highly proficient in MongoDB.

9. Is MongoDB suitable for OLTP workloads in addition to analytics and scale-out uses?

MongoDB can actually handle OLTP workloads quite well. Features like multi-document transactions (from v4.0) provide ACID guarantees needed for things like e-commerce orders and financial transactions.

MongoDB's performance advantages for writes and scalability make it suitable for high volume OLTP systems. The flexibility of documents can support evolving data requirements.

That said, MySQL still edges out MongoDB for the most rigorous, business critical OLTP needs where flawless data consistency and established frameworks like Spring are must-haves. But MongoDB closes the gap significantly here.

10. What are some tips for deciding between MySQL and MongoDB?

Some high-level tips for deciding between the two:

  • Consider your data structures and how they will evolve - is relational or document model a better fit?
  • Analyze typical access patterns and queries - lots of joins favors MySQL, while aggregations on dynamic data suits MongoDB.
  • Evaluate scaling needs - anticipated data growth and workload favors MongoDB's horizontal scaling.
  • Assess transaction needs - if you absolutely require full ACID and rock solid consistency, lean MySQL.
  • Prototype with both and compare developer experience.
  • Consider skills of team - existing MySQL or MongoDB experience?

For many use cases, both databases can work well. Focus on your workloads, data, queries, scalability needs and team skills to determine if MySQL or MongoDB is preferable. A hybrid deployment using both together is also an option.

Rasheed Rabata

Is a solution and ROI-driven CTO, consultant, and system integrator with experience in deploying data integrations, Data Hubs, Master Data Management, Data Quality, and Data Warehousing solutions. He has a passion for solving complex data problems. His career experience showcases his drive to deliver software and timely solutions for business needs.

Related posts

No items found.