Overview

MySQL

Stacks129.6K

Followers108.6K

Votes3.8K

GitHub Stars11.8K

Forks4.1K

Fauna

Stacks112

Followers153

Votes27

FaunaDB vs MySQL: What are the differences?

Introduction: FaunaDB and MySQL are two popular databases used for managing and storing data in web applications. While both serve similar purposes, they have key differences that make them suitable for different types of applications.

1. Scalability: One major difference between FaunaDB and MySQL is scalability. FaunaDB is designed to scale horizontally effortlessly, making it suitable for applications with unpredictable or rapidly changing workloads. On the other hand, MySQL traditionally requires more manual intervention and careful planning to scale horizontally.

2. Architecture: The architecture of FaunaDB and MySQL also differs significantly. FaunaDB is a globally distributed database that operates on a distributed system model, allowing for high availability and fault tolerance. In contrast, MySQL follows a more traditional client-server architecture, making it easier to set up but less resilient in the face of failures.

3. Consistency: FaunaDB is known for its strong consistency guarantees, providing a reliable and predictable system behavior. In contrast, MySQL offers eventual consistency by default, which can lead to data conflicts and inconsistencies in certain scenarios where multiple nodes are involved.

4. Query Language: Another key difference between FaunaDB and MySQL is the query language they support. FaunaDB uses the FQL (Fauna Query Language), a powerful and expressive language optimized for working with complex, nested data structures. On the other hand, MySQL uses SQL (Structured Query Language), a standard language with widespread adoption but sometimes limited in its capabilities for advanced data manipulation.

5. Multi-Model Support: FaunaDB supports multiple data models, including document, relational, and graph models, allowing developers to choose the most appropriate model for their specific use case. In comparison, MySQL primarily focuses on the relational data model, limiting the flexibility for applications that require different types of data structures.

6. Serverless Capabilities: FaunaDB offers serverless capabilities, where developers can focus on building applications without managing infrastructure or provisioning servers. This can lead to reduced operational overhead and faster development cycles compared to MySQL, which typically requires more manual configuration and maintenance.

In Summary, FaunaDB and MySQL differ in terms of scalability, architecture, consistency, query language, support for multiple data models, and serverless capabilities, making them suitable for different types of applications depending on the specific requirements.

🔥 Trending in Databases on StackShare

PostgreSQL Databases

A powerful, open source object-relational database system

Try it View Docs Alternatives

Try

431

83.9k

Advice on MySQL, Fauna

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	Fauna
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.	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.
-	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 11.8K	GitHub Stars -
GitHub Forks 4.1K	GitHub Forks -
Stacks 129.6K	Stacks 112
Followers 108.6K	Followers 153
Votes 3.8K	Votes 27
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 5 100% ACID 4 Generous free tier 4 Removes server provisioning or maintenance 3 No more n+1 problems (+ GraphQL) 3 Low latency global CDN's Cons 1 Log stack traces to avoid improper exception handling 1 Must keep app secrets encrypted 1 Susceptible to DDoS (& others) use timeouts throttling

What are some alternatives to MySQL, Fauna?

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.