Overview

MySQL

Stacks128.5K

Followers108.6K

Votes3.8K

GitHub Stars11.8K

Forks4.1K

Mongoose

Stacks2.1K

Followers1.4K

Votes56

Mongoose vs MySQL: What are the differences?

Key Differences between Mongoose and MySQL

Mongoose and MySQL are both widely used in database management, but they differ in several key aspects. Here are the key differences between Mongoose and MySQL:

Schema-based vs. Schema-less: Mongoose is a schema-based database, where the structure of the data is defined using schemas and models. On the other hand, MySQL is schema-less, meaning it does not enforce a specific structure for the data. This makes Mongoose more suitable for applications that require strict data validation and consistency.
Data Query Language: Mongoose uses MongoDB's query language, which is a JSON-based query language that allows for more flexible and expressive queries. On the other hand, MySQL uses Structured Query Language (SQL), which is a standardized language for managing relational databases. SQL provides a more structured and powerful querying mechanism, especially for complex relational operations.
Database Scalability: Another significant difference between Mongoose and MySQL is their scalability capabilities. Mongoose is designed primarily for horizontal scalability, where data is distributed across multiple servers or instances. MySQL, on the other hand, is more suitable for vertical scalability, where the database server is upgraded to increase its capacity. This makes Mongoose a better choice for handling large-scale applications with high data volumes and traffic.
Data Consistency: In terms of data consistency, Mongoose provides strong consistency out of the box. When writing data to the database, Mongoose ensures that the data is immediately consistent across all replicas or instances. MySQL, on the other hand, offers configurable consistency levels, such as eventual consistency or strong consistency, depending on the application's requirements.
Data Modeling: Mongoose provides a higher-level abstraction for data modeling, allowing developers to define relationships and associations using schemas and models. This makes it easier to work with complex data structures and relationships. MySQL, being a traditional relational database, enforces referential integrity and supports various relationship types such as one-to-one, one-to-many, and many-to-many.
Data Storage: Mongoose uses a document-based storage model, where data is stored in the form of documents in a JSON-like format. This makes it easier to work with semi-structured or unstructured data. MySQL, being a relational database, stores data in tables with predefined schemas, making it suitable for structured and normalized data.

In Summary, Mongoose and MySQL differ in their approach to data modeling, query language, scalability, data consistency, and storage model. Mongoose is well-suited for applications that require strict schema-based modeling, flexibility in querying, and horizontal scalability, while MySQL is a reliable choice for applications that need strong consistency, powerful SQL querying, and structured data management.

Advice on MySQL, Mongoose

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

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Navraj

CEO at SuPragma

Apr 16, 2020

Needs adviceon

MySQL

PostgreSQL

I asked my last question incorrectly. Rephrasing it here.

I am looking for the most secure open source database for my project I'm starting: https://github.com/SuPragma/SuPragma/wiki

Which database is more secure? MySQL or PostgreSQL? Are there others I should be considering? Is it possible to change the encryption keys dynamically?

Thanks,

Raj

401k views401k

Comments

Detailed Comparison

MySQL	Mongoose
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.	Let's face it, writing MongoDB validation, casting and business logic boilerplate is a drag. That's why we wrote Mongoose. Mongoose provides a straight-forward, schema-based solution to modeling your application data and includes built-in type casting, validation, query building, business logic hooks and more, out of the box.
Statistics
GitHub Stars 11.8K	GitHub Stars -
GitHub Forks 4.1K	GitHub Forks -
Stacks 128.5K	Stacks 2.1K
Followers 108.6K	Followers 1.4K
Votes 3.8K	Votes 56
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 17 Several bad ideas mixed together 17 Well documented 10 JSON 8 Actually terrible documentation 2 Recommended and used by Valve. See steamworks docs Cons 3 Model middleware/hooks are not user friendly
Integrations
No integrations available	Node.js MongoDB

What are some alternatives to MySQL, Mongoose?

MongoDB

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.

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.