Decoding the Rise of MongoDB vs MySQL and MariaDB

As someone who lived through the early days of web development, I have fond memories of building sites on the LAMP stack – Linux, Apache, MySQL and PHP. AWS and cloud servers were still a speck on the horizon. But our humble server setups powered a ridiculous number of dynamic, database-backed websites. And as one of the “M’s” in LAMP, MySQL grew to become the undisputed king of open source relational databases.

Yet today‘s landscape looks much different than those early 2000s days. The tide has slowly shifted towards NoSQL databases like MongoDB. This leaves many wondering whether MongoDB is rendering MySQL obsolete.

In this epic guide, we’ll analyze the history, use cases, performance and architecture of MongoDB and MySQL-based alternatives like MariaDB. You’ll uncover real-world insights on when to consider adopting document database concepts for your next application.

The Rise and Fall of the MySQL Empire

Let’s rewind a bit to understand the origins of MySQL. Finnish developer Michael "Monty" Widenius created MySQL in 1995 as an open source alternative combining performance and simplicity absent in commercial options at the time.

The Graph below from DB-Engines shows MySQL‘s astronomical growth over the next 15 years:

Several factors fueled mass MySQL adoption:

1. Languages and Frameworks

Scripting languages like PHP with MySQL libraries made queries simple. Later Ruby on Rails provided helper methods accessing MySQL under the hood.

2. Scale and Support

MySQL handled incredible data volumes while powering pioneers like Facebook, YouTube and Twitter. Community knowledge flourished.

3. Cost

A free open source database appealed to lean startup budgets. MySQL became the default choice for web applications.

This meteoric rise did not go unnoticed by large enterprises who wanted a piece of the database pie (and profits). After Oracle acquired Sun Microsystems (and with it MySQL) in 2010, developers worried about MySQL‘s future open source commitment.

While Oracle helped further harden and optimize MySQL in some ways, cracks began to show in this empire. Something transformative was stirring below the surface that would challenge MySQL‘s dominance…

NoSQL and MongoDB Emerge from Web Scale Demands

By the mid to late 2000s, internet giants like Google reached a scale that exposed cracks in traditional relational databases like MySQL:

• Massive Data Volumes: Storing petabytes of data across globally distributed data centers pushed the limits of single database scale.

• Performance Bottlenecks: Reading and writing data for user requests measured in millions per second revealed choke points in SQL query execution.

• Always-On Availability: Downtime from shard rebalancing or failovers became unacceptable at 24/7 web scale.

Relational models also struggled representing semi-structured data from new sources like documents, emails, graphs and mobile apps.

Out of these scaling and flexibility pain points, the NoSQL movement began. Startups like MongoDB pioneered a document model with an intuitive JSON-like format. Instead of predefined schemas, dynamic structures made iterating on data models easy. And auto-sharding distributed huge datasets across cheap commodity servers.

While detractors viewed these NoSQL tradeoffs vs SQL initially with skepticism, MongoDB adoption quickly soared:

Today over 27% of developers now use MongoDB in applications across all industries. From traditional enterprises to digital disruptors, shifts towards document databases seem inevitable rather than just hype-driven.

Can MariaDB Restore MySQL Prominence?

Among those unhappy with MySQL under Oracle‘s reign, Monty Widenius remained outspoken given his baby straying far from intended roots. When Oracle acquired Sun Microsystems in 2010, Monty left to launch an enhanced MySQL fork named MariaDB.

The MariaDB Foundation governs everything transparently through open meetings and publicly accessible mailing lists discussions. Widenius has since stepped down but technical leadership including other MySQL alumni continues advancing MariaDB as a full replacement drop-in substitute.

While adoption trails MySQL still, MariaDB usage has risen sharply to 17% of developers. Part of this comes from major customers like Google migrating away from Oracle. Specific technical improvements also entice some users:

Let‘s analyze MariaDB and MongoDB capabilities across crucial database selection criteria…

MongoDB vs MariaDB – Schemas and Flexibility

If you‘ve ever spent days modifying a large legacy MySQL schema, you will appreciate MongoDB‘s dynamic document model. On the other hand, MariaDB provides battle-tested structure for relational purists.

Consider this example schema representing an e-commerce site‘s customers, orders and products:

SQL Database Schema

Table: Customers
- id
- first_name 
- last_name
- shipping_address

Table: Orders 
- id
- customer_id (FK)  
- total_price

Table: Order_Items
- id
- order_id (FK)
- product_id (FK) 
- quantity

Table: Products
- id 
- name
- price

Now in MongoDB as Nested Documents:

{
  "_id": "cust123",
  "first_name": "John", 
  "last_name": "Doe",
  "shipping_address": {
    "street": "123 Sesame St",
    "city": "Anytown",
    "state": "CA",
    "zip": 12345  
  },
  "orders": [
     {"_id": "ord123",
      "total_price": 98.00,
      "items": [
        {"product": "Widget",
         "quantity": 2},
        {"product": "Gadget",
         "quantity": 1}
       ]
     }
  ]  
}

Even this simple example shows MongoDB’s flexibility in representing order data inside the customer document without any table JOINs. Adding new attributes or nested data becomes trivial.

While freeform documents provide great iteration speed, some may prefer the rigor of relational modeling. Choose what aligns best with internal application development skillsets.

Benchmarking MariaDB vs MongoDB Performance

Debates rage whether SQL or NoSQL databases perform intrinsically faster. In truth, benchmark outcomes depend heavily on database design, query patterns, and hardware resources.

Let‘s examine sample query benchmarks on reasonably optimized databases:

• MongoDB shows faster single record access speeds
• MariaDB aggregates and more complex queries leverage SQL query optimization strength
• Indexing appropriately is crucial for both databases

Delving further into optimizations each database offers:

MariaDB Performance Tricks

• Key cache warming on restart
• MyISAM storage for read heavy uses
• Keeping transactions small
• Careful index balancing

MongoDB Performance Techniques

• Embedding related data to avoid joins
• Pre-joining reporting collections
• Sharding to distribute load
• Indexing appropriately
• Using covered queries

While mundane, neglecting database care and feeding will degrade either system over time. Learn optimization techniques relevant to your usage.

And when truly pushing limits, leverage MongoDB’s distributed scales or explore MariaDB columnar storage for analytical use cases.

Scaling Patterns – Sharded vs Massively Parallel

Although Oracle markets MySQL editions supporting roster-like sharding, MongoDB pioneered automatic horizontal scaling. MariaDB in contrast focuses on squeezing vertical scale limits.

MySQL Scales Up Bigger Boxes

Traditional MySQL architects application tier separation with beefier database servers containing more disks, memory and processors. This monolithic scaling hits limits, where throwing more expensive hardware no longer helps.

MongoDB Designed for Distributed Clusters

MongoDB partitions and distributes data automatically across commodity servers called shards. Expanding capacity simply involves adding more lower-cost shards.

If starting today without legacy constraints, MongoDB likely handles future scaling needs easier. But existing MySQL infrastructures can also balance loads reasonably well when carefully managed.

Reliability and Data Integrity Tradeoffs

When exploring alternatives to reigning database incumbents like MySQL, no CTO wants to jeopardize data accuracy or site reliability.

However the desire for always-on global site availability and infinite horizontal scaling has driven NoSQL systems like MongoDB to forfeit traditional ACID transaction compliance.

MariaDB Guarantees ACID Compliance

Like MySQL, MariaDB adheres firmly to atomic, consistent, isolated and durable transactions. Member operations either wholly succeed or completely fail. Enforcing these constraints maintains data integrity, avoiding corruption issues plaguing non-ACID systems.

MongoDB Trades ACID for Scale

Unfortunately distributed databases cannot prevent parallel conflicting writes across partitions. MongoDB focuses instead on providing high availability and eventuality consistent replica sets.

This means developers must handle anomalies across shards using error retry handling rather than expecting database enforced integrity by default.

While perhaps uncomfortable at first, many find mimic‘ing eventual consistency logic aligns better with real world behavior. Preferring availability over absolute correctness has driven internet scale NoSQL adoption.

But mission critical applications like banking still require foolproof ACID transactions, where MariaDB shines over MongoDB.

Native Query Languages

Both databases provide rich native languages for reading and analyzing data. While SQL skills remain more prevalent, MongoDB’s JSON-derived dialect continues gaining adoption.

MariaDB Uses Standard SQL Dialect

MariaDB accepts ANSI SQL including complex JOIN operations across tables, aggregations, window functions and other advanced reporting features. SQL skills translate easily.

MongoDB Employs JSON-like Syntax

MongoDB Query Language features dot notation traversing into documents, equality matches, $or conditionals along with $sort/$limit cursors for paging. Aggregation pipelines $group and $project fields similarly to SQL Select clauses. Indexing helps accelerate common queries for large collections.

MariaDB’s complex SQL optimizer represents an advantage for convoluted analytic queries. But MongoDB querying provides low overhead and fast execution for simpler document access patterns commonly seen in modern application development.

Summarizing Key Database Differences

	MariaDB	MongoDB
Data Model	Tabular Relational	Document/JSON
Schemas	Predefined Structure	Dynamic
Scaling	Vertical	Auto Sharding
Transactions	Full ACID Support	Eventual Consistency
Durability	Reliable	Tunable Peristence
Query Language	SQL	JSON-based
Adoption	Growing	Very Popular

Both traditional RDBMS systems like MariaDB and newer NoSQL document stores like MongoDB handle an incredibly diverse array of workloads. The optimal database aligns closely with application data access patterns and query complexity along with developer experience.

While trends move towards distributed systems for more flexibility, many applications perform perfectly fine on mature MySQL or MariaDB. Evaluate options objectively based on your technical and business environment.

Key Takeaways and Recommendations

Hopefully contrasting MongoDB and MariaDB/MySQL proved educational, whether currently using either database or considering migrating between them.

Here are my closing thoughts as an unbiased industry practitioner:

• No panacea database solution exists across all uses.
• Consider graduation towards MongoDB for glass ceiling limitations around scale or iterations blocking application velocity on current SQL systems.
• MongoDB compliments rather than replaces SQL long-term. Its document model mirrors code friendlier than tabular SQL.
• For simpler schema needs prioritizing reliability over all-out speed, MariaDB and MySQL remain great relational database choices.
• When evaluating MongoDB, factor in required ramp up on non-relational modeling techniques to ensure success.

As always, reach out with any database questions! I’m glad to help point you towards the ideal backend data platform given your application’s unique needs.