Overview

MongoDB

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Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Fauna

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Followers153

Votes27

FaunaDB vs MongoDB: What are the differences?

Introduction

FaunaDB and MongoDB are both popular NoSQL databases used for storing and retrieving data. However, there are several key differences between the two that make them suitable for different use cases. In this article, we will explore these differences.

Data Model: One of the main differences between FaunaDB and MongoDB lies in their data models. FaunaDB is a transactional database that supports a relational data model, making it suitable for complex use cases where data consistency is crucial. On the other hand, MongoDB is a document-oriented database that follows a flexible schema-less data model, allowing for easy and fast data retrieval.
Scalability: When it comes to scalability, FaunaDB provides built-in multi-region replication and global consistency, making it highly scalable for global applications. MongoDB, however, requires additional configurations and setup to achieve the same level of scalability, making it more suitable for smaller-scale applications.
Query Language: Another key difference lies in the query languages supported by the two databases. FaunaDB uses a powerful and expressive query language called FQL (Fauna Query Language) which enables complex queries, joins, and aggregations, similar to SQL. MongoDB, on the other hand, uses a JSON-based query language that is more flexible but lacks some advanced querying capabilities.
Integration with Serverless Functions: FaunaDB has native integrations with serverless functions (such as AWS Lambda), allowing for seamless integration and enabling developers to build powerful serverless applications. MongoDB also offers integration with serverless functions, but it requires additional setup and configuration.
ACID Transactions: ACID (Atomicity, Consistency, Isolation, Durability) transactions are a crucial feature for ensuring data consistency and integrity. FaunaDB provides strong ACID transactions out-of-the-box, allowing for confident and reliable data manipulation. MongoDB, on the other hand, offers some transactional support starting from version 4.0, but it doesn't provide the same level of consistency and atomicity as FaunaDB.
Managed Service: FaunaDB offers a fully managed database-as-a-service, taking care of infrastructure management, backups, scaling, and monitoring. This makes it easier for developers to focus on their application logic without worrying about the underlying infrastructure. MongoDB, on the other hand, offers both self-hosted and managed options, giving users more control but also requiring more management efforts.

In summary, FaunaDB is a highly scalable and consistent database with a relational data model, powerful query language, and seamless integration with serverless functions. MongoDB, on the other hand, is a flexible document-oriented database with a simpler schema-less data model, a JSON-based query language, and support for ACID transactions starting from version 4.0.

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Advice on MongoDB, Fauna

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.

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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.

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Comments

Detailed Comparison

MongoDB	Fauna
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.	Escape the boundaries imposed by legacy databases with a data API that is simple to adopt, highly productive to use, and offers the capabilities that your business needs, without the operational pain typically associated with databases.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Native support for GraphQL and others. Easily access any data with any API. No middleware necessary.; Access all data via a data model that best suits your needs - relational, document, graph or composite.; A unique approach to indexing makes it simpler to write efficient queries that scale with your application.; Build SaaS apps more easily with native multi-tenancy and query-level QoS controls to prevent workload collisions.; Eliminate data anomalies with multi-region ACID transactions that don't limit number of keys or documents.; Data-driven RBAC that combines with SSL to offers reliable protection, and yet is simple to understand and codify.; Travel back in time with temporal querying. Run queries at a point-in-time or as change feeds. Track how your data evolved.; Dynamically replicates your data to global locations, so that your queries run fast no matter where your users are.; Easily deploy a FaunaDB cluster on your workstation accompanied by a powerful shell and tools to simplify your workflow.;
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 112
Followers 82.0K	Followers 153
Votes 4.1K	Votes 27
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 5 100% ACID 4 Generous free tier 4 Removes server provisioning or maintenance 3 Works well with GraphQL 3 No more n+1 problems (+ GraphQL) Cons 1 Log stack traces to avoid improper exception handling 1 Susceptible to DDoS (& others) use timeouts throttling 1 Must keep app secrets encrypted

What are some alternatives to MongoDB, Fauna?

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.