Overview

MySQL

Stacks129.6K

Followers108.6K

Votes3.8K

GitHub Stars11.8K

Forks4.1K

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

MongoDB vs MySQL: What are the differences?

Introduction: In the world of databases, MongoDB and MySQL are popular choices for storing and managing data. Understanding the key differences between MongoDB and MySQL is crucial for individuals and organizations when deciding which database to use for their projects.

Data Model: MongoDB is a document-oriented database, which means it stores data in flexible, JSON-like documents. MySQL, on the other hand, is a relational database management system that stores data in tables with predefined schemas. This fundamental difference in data modeling impacts the way data is structured and queried in each database system.
Scalability: MongoDB is known for its ability to scale horizontally, making it easier to handle large volumes of data by adding more servers to distribute the load. In contrast, MySQL traditionally scales vertically by upgrading hardware resources on a single server. This scalability difference can affect the performance and cost of scaling as the data grows.
Query Language: MongoDB uses a query language that is rich in features and allows for complex queries on nested data structures. MySQL uses SQL (Structured Query Language), which is a standardized language for managing relational databases. The choice of query language can influence the ease of querying and manipulating data based on the application's requirements.
ACID Compliance: MySQL is widely recognized for its support of ACID (Atomicity, Consistency, Isolation, Durability) properties, ensuring data integrity and reliability. MongoDB, on the other hand, sacrifices ACID transactions for higher performance and scalability through its flexibility in data storage. Understanding the trade-off between ACID compliance and performance is essential when selecting the appropriate database system.
Schema Flexibility: In MongoDB, there is no need to define a schema upfront, allowing for dynamic and evolving data structures within the same collection. In contrast, MySQL requires a predefined schema before data can be inserted, enforcing structure and data integrity. The level of schema flexibility can impact the agility and adaptability of the database to changing data requirements over time.
Community and Support: MySQL has been around for a longer period of time and has a larger user base, resulting in extensive community support, documentation, and resources. MongoDB, while rapidly growing in popularity, may have a smaller community compared to MySQL. The availability of community support and resources can play a crucial role in troubleshooting issues, learning, and adopting new features for database systems.

In Summary, understanding the key differences between MongoDB and MySQL, such as data model, scalability, query language, ACID compliance, schema flexibility, and community support, is essential for making an informed decision on selecting the most suitable database system for specific project requirements.

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Advice on MySQL, MongoDB

Kyle

Web Application Developer at Redacted DevWorks

Dec 3, 2019

Decidedon

PostGIS

While there's been some very clever techniques that has allowed non-natively supported geo querying to be performed, it is incredibly slow in the long game and error prone at best.

MySQL finally introduced it's own GEO functions and special indexing operations for GIS type data. I prototyped with this, as MySQL is the most familiar database to me. But no matter what I did with it, how much tuning i'd give it, how much I played with it, the results would come back inconsistent.

It was very disappointing.

I figured, at this point, that SQL Server, being an enterprise solution authored by one of the biggest worldwide software developers in the world, Microsoft, might contain some decent GIS in it.

I was very disappointed.

Postgres is a Database solution i'm still getting familiar with, but I noticed it had no built in support for GIS. So I hilariously didn't pay it too much attention. That was until I stumbled upon PostGIS and my world changed forever.

449k views449k

Comments

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k

Comments

Detailed Comparison

MySQL	MongoDB
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.	MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
-	Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
Statistics
GitHub Stars 11.8K	GitHub Stars 27.7K
GitHub Forks 4.1K	GitHub Forks 5.7K
Stacks 129.6K	Stacks 96.6K
Followers 108.6K	Followers 82.0K
Votes 3.8K	Votes 4.1K
Pros & Cons
Pros 800 Sql 679 Free 562 Easy 528 Widely used 490 Open source Cons 16 Owned by a company with their own agenda 3 Can't roll back schema changes	Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language

What are some alternatives to MySQL, MongoDB?

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

CouchDB

Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.