Overview

MariaDB

Stacks16.5K

Followers12.8K

Votes468

GitHub Stars6.6K

Forks1.9K

Fauna

Stacks112

Followers153

Votes27

FaunaDB vs MariaDB: What are the differences?

Introduction: When comparing FaunaDB and MariaDB, it is essential to understand the key differences between these two popular database management systems.

Data Model: FaunaDB is a serverless, globally distributed, and multi-model database that supports document, relational, graph, and temporal data models. On the other hand, MariaDB is a traditional relational database management system that primarily supports the relational data model. This difference allows FaunaDB to be more flexible in handling different types of data structures compared to MariaDB.
Scalability: FaunaDB is designed to be highly scalable out of the box, with built-in support for global distribution and automatic sharding of data. In contrast, MariaDB requires manual configuration and additional tools to achieve scalability, making it less straightforward than FaunaDB in scaling applications.
Consistency Model: FaunaDB employs a strict serializable isolation level by default to ensure strong consistency guarantees across transactions, making it suitable for applications that require strict data integrity. MariaDB, on the other hand, offers various isolation levels, including Read Committed and Repeatable Read, giving developers more flexibility but potentially sacrificing some consistency guarantees.
Serverless Architecture: FaunaDB follows a serverless architecture, meaning developers do not need to manage infrastructure provisioning, configuration, or scaling, as it is managed by the FaunaDB service. In contrast, MariaDB requires the deployment and maintenance of servers, potentially adding complexity and operational overhead for development teams.
Multi-Region Support: FaunaDB natively supports multi-region deployments, allowing data to be automatically replicated across different geographic locations for improved performance and data redundancy. MariaDB, although supporting some form of replication, may require additional setup and configuration to achieve a similar level of geographic redundancy and performance optimization.
Pricing Model: FaunaDB offers a consumption-based pricing model where customers pay for actual usage, including storage, data transfer, and compute resources. In comparison, MariaDB typically follows a more traditional licensing model based on server instances or cores, which may result in a different cost structure for organizations depending on their usage patterns and requirements.

In summary, FaunaDB and MariaDB differ in their data models, scalability, consistency models, architecture, multi-region support, and pricing models, offering developers a choice between flexibility, scalability, and ease of management in database solutions.

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Advice on MariaDB, Fauna

Omran

CTO & Co-founder at Bonton Connect

Jun 19, 2020

Needs advice

We actually use both Mongo and SQL databases in production. Mongo excels in both speed and developer friendliness when it comes to geospatial data and queries on the geospatial data, but we also like ACID compliance hence most of our other data (except on-site logs) are stored in a SQL Database (MariaDB for now)

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Detailed Comparison

MariaDB	Fauna
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.	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.
Replication;Insert Delayed;Events;Dynamic;Columns;Full-text;Search;GIS;Locale;Settings;subqueries;Timezones;Triggers;XML;Functions;Views;SSL;Show Profile	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 6.6K	GitHub Stars -
GitHub Forks 1.9K	GitHub Forks -
Stacks 16.5K	Stacks 112
Followers 12.8K	Followers 153
Votes 468	Votes 27
Pros & Cons
Pros 149 Drop-in mysql replacement 100 Great performance 74 Open source 55 Free 44 Easy setup	Pros 5 100% ACID 4 Generous free tier 4 Removes server provisioning or maintenance 3 Works well with GraphQL 3 Also supports SQL, CQL Cons 1 Must keep app secrets encrypted 1 Log stack traces to avoid improper exception handling 1 Susceptible to DDoS (& others) use timeouts throttling

What are some alternatives to MariaDB, 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.

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.

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.