MongoDB vs WatermelonDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

WatermelonDB

Stacks12

Followers123

Votes1

GitHub Stars11.3K

Forks626

MongoDB vs WatermelonDB: What are the differences?

MongoDB is a popular NoSQL database that offers flexibility and scalability, while WatermelonDB is designed specifically for mobile and offline-first applications, providing efficient data handling for such scenarios. Here are some key differences between MongoDB and WatermelonDB:

Database Type: MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like BSON documents. It offers dynamic schema capabilities, making it suitable for projects with evolving data requirements. WatermelonDB, on the other hand, is a client-side database designed for mobile applications, particularly for scenarios where offline access and synchronization are critical.
Target Use Case: MongoDB is widely used for various applications, ranging from web applications to enterprise systems, due to its ability to handle large volumes of unstructured or semi-structured data. WatermelonDB is specifically tailored for mobile applications, especially those that require offline access and real-time synchronization.
Offline-First Architecture: WatermelonDB is optimized for offline-first scenarios, where mobile apps need to work seamlessly even without an internet connection. It supports data synchronization and conflict resolution when the app reconnects to the internet. MongoDB doesn't inherently provide offline capabilities and is better suited for online scenarios.
Performance and Size: MongoDB is designed to handle large-scale data storage and processing, making it suitable for applications with extensive data requirements. WatermelonDB is optimized for mobile devices, focusing on efficient storage and retrieval within the limitations of mobile environments, which often have restricted storage and processing capabilities.
Query Language and API: MongoDB uses the MongoDB Query Language (MQL) to perform queries and operations on the data. It offers a powerful and flexible querying system. WatermelonDB uses a custom query language that focuses on efficient querying and data retrieval on the client-side for offline scenarios. The querying capabilities are tailored to the requirements of mobile applications.
Data Synchronization: WatermelonDB provides built-in synchronization capabilities for real-time data synchronization when the app comes online. MongoDB doesn't offer native synchronization features and typically requires additional tools or services to achieve real-time data synchronization.

In summary, MongoDB is a versatile NoSQL database suitable for a wide range of applications, while WatermelonDB is optimized for mobile apps that require efficient offline data storage and synchronization.

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Advice on MongoDB, WatermelonDB

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

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

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	WatermelonDB
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.	WatermelonDB is a new way of dealing with user data in React Native and React web apps. It's optimized for building complex applications in React Native, and the number one goal is real-world performance. In simple words, your app must launch fast.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	-
Statistics
GitHub Stars 27.7K	GitHub Stars 11.3K
GitHub Forks 5.7K	GitHub Forks 626
Stacks 96.6K	Stacks 12
Followers 82.0K	Followers 123
Votes 4.1K	Votes 1
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 1 Undefined is not an object (evaluating 'columnSchema.ty
Integrations
No integrations available	RxJS React SQLite React Native

What are some alternatives to MongoDB, WatermelonDB?

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 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.

Related Comparisons

Database Type: MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like BSON documents. It offers dynamic schema capabilities, making it suitable for projects with evolving data requirements. WatermelonDB, on the other hand, is a client-side database designed for mobile applications, particularly for scenarios where offline access and synchronization are critical.
Target Use Case: MongoDB is widely used for various applications, ranging from web applications to enterprise systems, due to its ability to handle large volumes of unstructured or semi-structured data. WatermelonDB is specifically tailored for mobile applications, especially those that require offline access and real-time synchronization.
Offline-First Architecture: WatermelonDB is optimized for offline-first scenarios, where mobile apps need to work seamlessly even without an internet connection. It supports data synchronization and conflict resolution when the app reconnects to the internet. MongoDB doesn't inherently provide offline capabilities and is better suited for online scenarios.
Performance and Size: MongoDB is designed to handle large-scale data storage and processing, making it suitable for applications with extensive data requirements. WatermelonDB is optimized for mobile devices, focusing on efficient storage and retrieval within the limitations of mobile environments, which often have restricted storage and processing capabilities.
Query Language and API: MongoDB uses the MongoDB Query Language (MQL) to perform queries and operations on the data. It offers a powerful and flexible querying system. WatermelonDB uses a custom query language that focuses on efficient querying and data retrieval on the client-side for offline scenarios. The querying capabilities are tailored to the requirements of mobile applications.
Data Synchronization: WatermelonDB provides built-in synchronization capabilities for real-time data synchronization when the app comes online. MongoDB doesn't offer native synchronization features and typically requires additional tools or services to achieve real-time data synchronization.

MongoDB vs WatermelonDB