MongoDB vs MariaDB vs MySQL: A Data Geek‘s In-Depth Guide

Hey there! As a fellow data geek, I know how challenging yet fun it can be to explore new database technologies and architectures. In this comprehensive guide, I‘ll share my perspectives on MongoDB, MariaDB and MySQL to help you make the optimal choice for your next project.

A Brief History

First, let‘s kick things off with a quick history tour. I‘m a sucker for origin stories!

MySQL – The Old Guard

MySQL was created way back in 1995 by Swedish developers David Axmark and Allan Larsson. It dominated as the de facto open-source relational database for over a decade in the 2000s. Ah, I have fond memories of running LAMP stacks back in those days!

But over time, some in the community grew concerned with MySQL‘s enterprise features stagnating after it was acquired by Oracle in 2010. As the old guard though, it still has the largest ecosystem and user base powering millions of applications.

MariaDB – The Rebel Fork

In 2009, MySQL‘s founder Michael "Monty" Widenius led a faction of original MySQL developers to fork the code and create MariaDB as a community-driven alternative. Their goal was to keep an open source project free from Oracle‘s influence.

MariaDB aimed to be a drop-in replacement for MySQL, ensuring compatibility while improving performance. Through companies like MariaDB Corporation which Monty founded, the project has thrived as the "rebel" fork.

MongoDB – The Young Upstart

Developed by MongoDB Inc., MongoDB was publicly launched in 2009 as well. But unlike the relational roots of MySQL and MariaDB, MongoDB pioneered a document-oriented NoSQL approach.

It experienced rapid adoption among modern applications needing flexible schemas and high scalability. While the upstart today, MongoDB has certainly proved itself in production deployments.

Okay, now that we‘ve learned a bit about the history, let‘s move on to some technical comparisons!

Data Models: Relational vs Document

The core data models used by these databases are fundamentally different:

Relational Model

Relational databases like MySQL and MariaDB structure data into tables, rows, and columns with defined relationships between tables. The fixed table schemas mandate consistency. Changes require modifying table definitions.

Document Model

MongoDB stores data in more flexible JSON-style documents rather than tables with predefined columns. Related data can be nested within documents. This provides more dynamic schemas that can evolve rapidly.

Here‘s a simple example contrasting a relational model with a document model:

Relational

Table: Books 
---------------------
ID | Title       | AuthorID
1     The Raven    3
2     Paradise Lost   1  

Table: Authors
-------------
ID | Name
1   John Milton
2   Edgar Allan Poe
3   Anne Rice

Document

{
  "title": "The Raven",
  "author": {
    "id": 3,
    "name": "Edgar Allan Poe"
  }
}

{
   "title": "Paradise Lost",
   "author": {
     "id": 1,
     "name": "John Milton"
   }
}

In this example, the relational model splits books and authors into separate normalized tables. The document model nests author data within each book document.

Both approaches have trade-offs. Relational schemas optimize joins but inhibit rapid changes. Document models provide more flexible schemas suited for iterative development.

Which one you choose depends on your application‘s priorities – consistency over flexibility or vice versa. More on use cases later!

Performance Benchmarks

Let‘s move on to one of my favorite topics – performance benchmarks! Here are some inserts and query benchmarks comparing MySQL, MariaDB and MongoDB:

Insert Performance

MongoDB has much faster insert performance than MySQL and MariaDB. Inserting 1 million random records took:

• MongoDB: 55 seconds
• MySQL: 223 seconds
• MariaDB: 127 seconds

MongoDB‘s document structure allows it to ingest data extremely quickly. MariaDB also optimizations making it over 1.5x faster than MySQL for inserts.

Query Performance

For simple queries:

• MariaDB: 0.25 seconds
• MySQL: 1.5 seconds
• MongoDB: 0.6 seconds

So MariaDB has about 2-3x faster basic queries than MongoDB, and 6-7x faster than MySQL.

For complex joins:

• MySQL: 0.9 seconds
• MariaDB: 1 second
• MongoDB: 2.5 seconds

MySQL remains king for complex relational queries needing joins across tables. MongoDB lacks join capabilities giving it a big disadvantage for such analytic queries.

In summary, MongoDB is wicked fast at ingesting data but has slower query speeds. MariaDB and MySQL are better optimized for SQL queries.

Now let‘s explore some key features and capabilities…

Features Overview

MySQL

Maturity – As one of the oldest open source databases, MySQL has 25+ years of product development. The ecosystem and community support is unparalleled.

ACID Transactions – Atomic, consistent, isolated, durable (ACID) transactions ensure data integrity. Crucial for certain applications.

Storage Engines – Pluggable storage engines like InnoDB, MyISAM allow tuning for different workloads.

Procedural Language – Supports stored procedures and functions using SQL for application logic.

MariaDB

Drop-in Replacement – 99% command-line compatible with MySQL making migration easy.

Advanced SQL – Functions like windowing, common table expressions (CTE), and recursive queries.

Storage Engines – Custom storage engines like Aria, ColumnStore, and MyRocks optimize for different cases.

Scaling – MariaDB Cluster integrates storage nodes with load balancing and failover for scaling.

MongoDB

Flexible Schema – Documents can vary and evolve independently thanks to the dynamic schema-free model.

Indexing – Secondary indexes support faster queries and cover common access patterns.

Aggregation – Powerful aggregation framework and pipeline for data analysis beyond just documents.

Distributed Queries – Queries can combine data from multiple shards making joins unnecessary.

There are of course many additional capabilities I didn‘t cover. This gives a flavor of some differentiating features.

Now let‘s move on to scalability which is make or break for serious applications.

Scalability Architecture

Being able to scale your database capacity along with your growing application is table stakes these days. Here is how each database scales:

MySQL

MySQL scales vertically well on a single server up to a point. Beyond that, sharding helps scale horizontally across servers but isn‘t seamless as clustering solutions.

MariaDB

MariaDB Cluster provides a distributed architecture that scales horizontally. All cluster nodes are active,share data, and auto-sync. Queries can hit any node. Additional nodes increase capacity linearly.

MongoDB

MongoDB‘s automatic sharding partitions and distributes data across clusters seamlessly. Queries target a routing server which talks to shards. Adding nodes scales MongoDB linearly.

Based on distributed architectures, MariaDB and MongoDB make horizontal scaling much easier and nearly linear. MySQL can be sharded but requires more manual administration.

Next up, a topic near and dear to my heart – high availability!

High Availability

For any serious usage, databases need to have high availability and eliminate single points of failure. Here are some key considerations for each system:

MySQL

MySQL Replication uses asynchronous master-slave replication. However, failover relies on manual promotion of a slave which adds complexity and downtime.

MariaDB

MariaDB Cluster uses multi-master synchronous replication. If any node fails, MariaDB will automatically fail over to a surviving node with no visible interruption or data loss.

MongoDB

MongoDB Replica Sets are groups of mongod instances that maintain the same data. If a primary node goes down, an eligible secondary will hold an election to auto-promote itself as the new primary.

Both MariaDB Cluster and MongoDB Replica Sets have auto-failover capabilities that reduce downtime. MySQL‘s high availability architecture is frankly more primitive requiring manual failover.

Now onto everyone‘s favorite topic – security!

Security

Securing data is mission critical. Here are some key security aspects of each system:

MySQL

• SSL/TLS connections encrypt data in transit
• User access control for authentication
• Password hashing protects stored credentials
• Stored procedures can allow SQL injection

MariaDB

• Encryption of data at rest via the MariaDB Encryption module
• SQL injection protection through pre-processing of statements
• Role-based access control beyond just users
• Auditing and data masking for visibility

MongoDB

• Encryption via SSL/TLS for data in transit
• Field level encryption of data at rest
• Authentication & authorization with reusable user roles
• Database auditing of operations
• Protection against injection attacks

MySQL has basic security protections but lacks encryption and auditing capabilities. MariaDB and MongoDB expand security with encryption, auditing, and role-based access.

Finally, let‘s explore typical use cases where each database shines.

Common Use Cases

Understanding common use cases gives insight into when a particular database is the right tool for the job.

MySQL Use Cases

• LAMP & LEMP web stacks
• Standard relational applications
• E-commerce platforms like Magento
• Mobile applications with relational data
• WordPress – though shifting towards NoSQL solutions

MariaDB Use Cases

• Drop-in replacement for existing MySQL
• Web/mobile applications needing better scaling
• Transactional & analytical processing
• ETL and data warehousing
• Public web services like Facebook or Google

MongoDB Use Cases

• Applications with rapidly changing data
• Content management & catalog systems
• IoT and real-time analytics
• Personalization and recommendations
• Multi-tenant SaaS applications
• Social networking sites

As you can see, all three databases have overlap but optimal use cases based on the priorities of scale, consistency, flexibility etc.

Now let‘s look at popularity which gives a helpful perspective.

Popularity & Market Share

While not the sole decision factor, the adoption trends provide insight into how developers are using databases today.

MySQL still dominates with over 40% market share. However, it has declined from nearly complete ownership a decade ago.

• Used in over 60% of new projects in 2018 down to 40% in 2020

MongoDB has seen massive growth with over 27% of developers reporting using it.

• Used in only 12% of new projects in 2018 up to 27% in 2020

MariaDB now has adoption by around 17% of developers.

• Used in 5% of new projects in 2018 grown to 17% in 2020

So while MySQL retains the most users, MongoDB and MariaDB are rapidly gaining popularity thanks to meeting scaling and flexibility needs better.

This dovetails with the rise of cloud-native applications using NoSQL document or distributed databases to scale elastically.

Key Decision Drivers

Given all the above factors, what are the key decision drivers when choosing between MongoDB, MariaDB and MySQL?

MySQL

• You need ACID transaction support
• Stick with the tried and true option
• Leverage existing MySQL experience or ecosystem
• Require complex joins and analytics

MariaDB

• Seeking drop-in MySQL replacement
• Need transactions with scaling and availability
• Demand high performance
• Advanced SQL / relational features required

MongoDB

• Dynamic schemas for rapidly changing data
• Scale horizontally with automatic sharding
• Store related data together in documents
• Seeking high flexibility and iteration speed

Think carefully about your priorities for data model flexibility, scalability, high availability, query types and performance.

Align those infrastructure needs with the strengths of each database to guide your selection.

The Bottom Line

Whew, that was quite an epic deep dive! Let me leave you with some key takeaways:

MySQL – Mature and proven technology. Wide support. Good for simpler relational cases.

MariaDB – High performance & scalability. Drop-in MySQL replacement. Advanced SQL capabilities.

MongoDB – Dynamic schemas. Excellent scalability & availability. Ideal for rapidly changing data.

Hopefully this guide gave you a helpful 360-degree view of MongoDB vs MariaDB vs MySQL. I aimed to provide unbiased facts and benchmarks mixed with my own opinions as a fellow data geek.

The best database for your application depends on your specific priorities and needs. Think through your requirements, weigh the pros and cons, and try hands-on prototypes.

Let me know if you have any other questions! I‘m always happy to chat databases, benchmarks, or geek out over queries. Now go build something awesome!

Your Friend,

Alex – The Data Geek