EdgeDB vs MariaDB

Overview

MariaDB

Stacks16.5K

Followers12.8K

Votes468

GitHub Stars6.6K

Forks1.9K

EdgeDB

Stacks17

Followers52

Votes0

EdgeDB vs MariaDB: What are the differences?

Introduction

EdgeDB and MariaDB are two popular database management systems that cater to different needs and functionalities. Understanding the key differences between these two platforms can help in making an informed decision when choosing the right database solution for a specific use case.

Data Modeling Approach: EdgeDB follows a strongly-typed data modeling approach where data types are defined and enforced at the schema level, providing robust data integrity. On the other hand, MariaDB allows a more flexible data modeling approach, offering more freedom for developers to define and manipulate data types as needed.
Query Language: EdgeDB introduces its own query language, EdgeQL, which is designed for expressiveness and efficiency in querying complex data structures. MariaDB uses SQL (Structured Query Language) as its primary query language, which is a widely used standard in relational databases. EdgeQL aims to simplify and enhance querying capabilities, especially for complex data relationships.
Performance and Scalability: EdgeDB is known for its high performance and scalability, particularly when dealing with complex data models and queries. It is optimized for handling intricate data structures efficiently. On the other hand, while MariaDB also offers good performance, it may face challenges in scaling for extremely large datasets or complex queries compared to EdgeDB.
ACID Compliance: Both EdgeDB and MariaDB are ACID-compliant databases, ensuring data consistency, durability, and transactional integrity. However, EdgeDB's data modeling approach and query optimizations further enhance its compliance with ACID properties, making it a robust choice for mission-critical applications.
Data Relationships and Integrity: EdgeDB emphasizes strong data relationships and integrity by design, making it easier to maintain referential integrity and enforce constraints in the database schema. MariaDB provides features for defining relationships and constraints but may require additional effort from developers to ensure data integrity compared to EdgeDB.
Ease of Use and Learning Curve: EdgeDB's strongly-typed data modeling and EdgeQL may have a steeper learning curve for developers who are more familiar with traditional relational databases like MariaDB. While MariaDB offers a more familiar SQL-based interface, the learning curve for EdgeDB can be offset by its powerful data modeling capabilities once mastered.

In Summary, understanding the key differences between EdgeDB and MariaDB in areas such as data modeling approach, query language, performance, compliance, data relationships, and ease of use can help in choosing the right database solution for specific use cases.

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Advice on MariaDB, EdgeDB

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	EdgeDB
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.	An object-relational database that stores and describes the data as strongly typed objects and relationships between them.
Replication;Insert Delayed;Events;Dynamic;Columns;Full-text;Search;GIS;Locale;Settings;subqueries;Timezones;Triggers;XML;Functions;Views;SSL;Show Profile	Strict, strongly typed schema; Powerful and clean query language; Ability to easily work with complex hierarchical data; Built-in support for schema migrations
Statistics
GitHub Stars 6.6K	GitHub Stars -
GitHub Forks 1.9K	GitHub Forks -
Stacks 16.5K	Stacks 17
Followers 12.8K	Followers 52
Votes 468	Votes 0
Pros & Cons
Pros 149 Drop-in mysql replacement 100 Great performance 74 Open source 55 Free 44 Easy setup	No community feedback yet
Integrations
No integrations available	GraphQL Python

What are some alternatives to MariaDB, EdgeDB?

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.

Related Comparisons

EdgeDB vs MariaDB: What are the differences?

Introduction

Data Modeling Approach: EdgeDB follows a strongly-typed data modeling approach where data types are defined and enforced at the schema level, providing robust data integrity. On the other hand, MariaDB allows a more flexible data modeling approach, offering more freedom for developers to define and manipulate data types as needed.
Query Language: EdgeDB introduces its own query language, EdgeQL, which is designed for expressiveness and efficiency in querying complex data structures. MariaDB uses SQL (Structured Query Language) as its primary query language, which is a widely used standard in relational databases. EdgeQL aims to simplify and enhance querying capabilities, especially for complex data relationships.
Performance and Scalability: EdgeDB is known for its high performance and scalability, particularly when dealing with complex data models and queries. It is optimized for handling intricate data structures efficiently. On the other hand, while MariaDB also offers good performance, it may face challenges in scaling for extremely large datasets or complex queries compared to EdgeDB.
ACID Compliance: Both EdgeDB and MariaDB are ACID-compliant databases, ensuring data consistency, durability, and transactional integrity. However, EdgeDB's data modeling approach and query optimizations further enhance its compliance with ACID properties, making it a robust choice for mission-critical applications.
Data Relationships and Integrity: EdgeDB emphasizes strong data relationships and integrity by design, making it easier to maintain referential integrity and enforce constraints in the database schema. MariaDB provides features for defining relationships and constraints but may require additional effort from developers to ensure data integrity compared to EdgeDB.
Ease of Use and Learning Curve: EdgeDB's strongly-typed data modeling and EdgeQL may have a steeper learning curve for developers who are more familiar with traditional relational databases like MariaDB. While MariaDB offers a more familiar SQL-based interface, the learning curve for EdgeDB can be offset by its powerful data modeling capabilities once mastered.

EdgeDB vs MariaDB

Overview

EdgeDB vs MariaDB: What are the differences?