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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. MongoDB vs Mongoose

MongoDB vs Mongoose

OverviewDecisionsComparisonAlternatives

Overview

MongoDB
MongoDB
Stacks96.6K
Followers82.0K
Votes4.1K
GitHub Stars27.7K
Forks5.7K
Mongoose
Mongoose
Stacks2.4K
Followers1.4K
Votes56

MongoDB vs Mongoose: What are the differences?

MongoDB vs Mongoose: Key Differences

MongoDB and Mongoose are two popular technologies used for working with databases in web applications. While they both serve similar purposes, there are several key differences between them.

  1. Database Structure: MongoDB is a NoSQL database that stores data in a flexible, schema-less format, allowing for dynamic changes to the data structure. On the other hand, Mongoose is an Object Data Modeling (ODM) library for MongoDB, which provides a more structured approach by defining schemas and models for the data.

  2. Implementation: MongoDB can be used directly with the MongoDB Node.js driver, providing direct access to MongoDB functionality. Mongoose, on the other hand, is an additional layer built on top of the MongoDB driver, providing additional features and abstractions to simplify database operations.

  3. Schema Validation: While MongoDB allows for flexible data structures, it does not provide built-in schema validation. Mongoose, on the other hand, allows developers to define schemas with validation rules, ensuring that data conforms to specific requirements and preventing invalid data from being stored in the database.

  4. Middleware: Mongoose provides middleware functions that can be executed before or after specific database operations, such as saving or querying data. This allows developers to add custom logic or perform actions such as data validation or encryption before or after certain operations. MongoDB does not have built-in middleware functionality.

  5. Population and References: Mongoose provides a feature called population, which allows for linking documents from different collections based on references. This can simplify querying and accessing related data. MongoDB, being a schema-less database, does not have built-in support for population or references.

  6. Transactions: MongoDB provides support for multi-document transactions, allowing multiple database operations to be grouped together and either all succeed or all fail. This ensures data consistency in complex operations. Mongoose, being a library built on top of MongoDB driver, can also utilize MongoDB's transaction support.

In summary, MongoDB is a flexible NoSQL database that allows for dynamic data structures, while Mongoose is an ODM library that provides a more structured approach with features such as schema validation and middleware functionality. They differ in database structure, implementation, schema validation, middleware, population/references, and transaction support.

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Advice on MongoDB, Mongoose

George
George

Student

Mar 18, 2020

Needs adviceonPostgreSQLPostgreSQLPythonPythonDjangoDjango

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
Ido
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
Mike
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

MongoDB
MongoDB
Mongoose
Mongoose

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.

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.

Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
-
Statistics
GitHub Stars
27.7K
GitHub Stars
-
GitHub Forks
5.7K
GitHub Forks
-
Stacks
96.6K
Stacks
2.4K
Followers
82.0K
Followers
1.4K
Votes
4.1K
Votes
56
Pros & Cons
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
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
Node.js

What are some alternatives to MongoDB, Mongoose?

MySQL

MySQL

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.

PostgreSQL

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

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

SQLite

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

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

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

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

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

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

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.

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