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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. MariaDB vs MonetDB

MariaDB vs MonetDB

OverviewDecisionsComparisonAlternatives

Overview

MariaDB
MariaDB
Stacks16.5K
Followers12.8K
Votes468
GitHub Stars6.6K
Forks1.9K
MonetDB
MonetDB
Stacks13
Followers35
Votes2

MariaDB vs MonetDB: What are the differences?

### Introduction
MariaDB and MonetDB are both popular open-source relational databases, but they have key differences that set them apart from each other.

1. **Storage Engine**: MariaDB uses the InnoDB storage engine by default, which is known for its reliability and performance optimization for transaction processing. On the other hand, MonetDB has its own proprietary storage engine called MonetDB/X100, which is tailored for analytical processing with speed and efficiency in mind. This difference in storage engines impacts the overall performance and capabilities of each database in specific use cases.

2. **Data Processing Approach**: MariaDB follows the traditional row-based data storage and processing approach, where data is stored and retrieved in rows. In contrast, MonetDB utilizes a column-based storage and processing method, where data is stored and processed by columns rather than by rows. This columnar approach in MonetDB provides faster query performance and better compression rates for analytical workloads.

3. **ACID Compliance**: MariaDB is fully ACID (Atomicity, Consistency, Isolation, Durability) compliant, ensuring data integrity and reliability in transactional operations. MonetDB, on the other hand, prioritizes performance over strict ACID compliance, offering eventual consistency with a focus on high-speed data retrieval and processing. This difference in ACID compliance levels makes each database suitable for different types of applications and use cases.

4. **Language Support**: MariaDB is compatible with standard SQL and supports various programming languages like PHP, Java, and Python for application development. In contrast, MonetDB leverages its own SQL-compliant query language called MQL (MonetDB Query Language), which is optimized for analytics and complex data processing tasks. This language specificity in MonetDB may require users to adapt to its unique syntax for querying and interacting with the database.

5. **Community and Support**: MariaDB has a large and active community of developers, contributors, and users that provide extensive support, documentation, and resources for users at all levels. MonetDB also has a community backing, but it is comparatively smaller and may offer more niche-specific help and resources due to its focus on analytics and research applications. The level of community support and available resources may influence the ease of use and adoption of each database in different scenarios.

6. **Use Case Scenarios**: MariaDB is well-suited for general-purpose relational database applications, ranging from small to enterprise-level systems that require transactional consistency and robust features. On the other hand, MonetDB excels in analytical workloads, data warehousing, and research environments where speed, scalability, and efficient data processing are crucial. Understanding the specific use case requirements can help in deciding the most suitable database solution for a given project or application.

In Summary, MariaDB and MonetDB differ in their storage engines, data processing approaches, ACID compliance, language support, community and support, and use case scenarios, making each database cater to distinct needs and preferences in terms of performance, functionality, and application requirements.

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Advice on MariaDB, MonetDB

Maxim
Maxim

student at USI

Aug 25, 2020

Needs adviceonNode.jsNode.jsMongooseMongoosePostgreSQLPostgreSQL

Hi all. I am an informatics student, and I need to realise a simple website for my friend. I am planning to realise the website using Node.js and Mongoose, since I have already done a project using these technologies. I also know SQL, and I have used PostgreSQL and MySQL previously.

The website will show a possible travel destination and local transportation. The database is used to store information about traveling, so only admin will manage the content (especially photos). While clients will see the content uploaded by the admin. I am planning to use Mongoose because it is very simple and efficient for this project. Please give me your opinion about this choice.

321k views321k
Comments
Omran
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)

582k views582k
Comments

Detailed Comparison

MariaDB
MariaDB
MonetDB
MonetDB

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.

MonetDB innovates at all layers of a DBMS, e.g. a storage model based on vertical fragmentation, a modern CPU-tuned query execution architecture, automatic and self-tuning indexes, run-time query optimization, and a modular software architecture.

Replication;Insert Delayed;Events;Dynamic;Columns;Full-text;Search;GIS;Locale;Settings;subqueries;Timezones;Triggers;XML;Functions;Views;SSL;Show Profile
-
Statistics
GitHub Stars
6.6K
GitHub Stars
-
GitHub Forks
1.9K
GitHub Forks
-
Stacks
16.5K
Stacks
13
Followers
12.8K
Followers
35
Votes
468
Votes
2
Pros & Cons
Pros
  • 149
    Drop-in mysql replacement
  • 100
    Great performance
  • 74
    Open source
  • 55
    Free
  • 44
    Easy setup
Pros
  • 2
    High Performance

What are some alternatives to MariaDB, MonetDB?

MongoDB

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

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

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

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

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

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

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

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

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

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.

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