DuckDB vs MariaDB

Overview

MariaDB

Stacks16.5K

Followers12.8K

Votes468

GitHub Stars6.6K

Forks1.9K

DuckDB

Stacks49

Followers60

Votes0

DuckDB vs MariaDB: What are the differences?

Introduction

DuckDB and MariaDB are both popular database management systems, but they have key differences that set them apart. This article will highlight and discuss six specific differences between DuckDB and MariaDB.

Data Storage Strategy: DuckDB is an in-memory analytical database designed for efficient query performance on large datasets. It stores data in memory to achieve fast processing speeds. On the other hand, MariaDB is a disk-based relational database management system that persists data on disk, which allows for larger data storage but can result in slower query performance compared to DuckDB.
Query Execution: DuckDB implements vectorized query execution, which means it processes data in batches to achieve higher query execution speeds. This approach is optimized for analytical workloads. In contrast, MariaDB performs row-based query execution, which is better suited for relational database operations and transactional workloads.
Supported SQL Features: DuckDB offers a subset of SQL features, focusing on analytic queries and data processing. It provides a limited set of SQL functions and lacks some advanced features like stored procedures and triggers. Conversely, MariaDB provides a comprehensive set of SQL features, including support for stored procedures, triggers, full-text search, and more.
Concurrency Control: DuckDB employs a multiversion concurrency control (MVCC) approach, which allows concurrent reads and writes to the database without blocking access. This concurrency control mechanism ensures high performance in read-heavy workloads. In contrast, MariaDB uses various concurrency control mechanisms, including locking and transaction isolation levels, to handle concurrent access. However, these mechanisms can introduce more overhead and potentially impact performance.
Scalability: DuckDB is designed to handle large datasets efficiently in memory, which makes it suitable for analytical workloads but limits its scalability compared to disk-based systems like MariaDB. MariaDB can handle larger datasets by utilizing disk storage, allowing for more significant storage capacity and horizontal scalability.
Community and Ecosystem: MariaDB has a larger and more established community compared to DuckDB. It benefits from a vast ecosystem, extensive documentation, and community support. DuckDB, while gaining popularity, has a smaller community and a relatively smaller ecosystem. This difference can impact the availability of additional tools, libraries, and plugins for specific use cases.

In summary, DuckDB and MariaDB differ in their data storage strategy, query execution approach, supported SQL features, concurrency control mechanisms, scalability, and community/ecosystem support. DuckDB is optimized for in-memory analytics, while MariaDB offers a more comprehensive set of features and better scalability with disk-based storage.

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Advice on MariaDB, DuckDB

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	DuckDB
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.	It is an embedded database designed to execute analytical SQL queries fast while embedded in another process. It is designed to be easy to install and easy to use. DuckDB has no external dependencies. It has bindings for C/C++, Python and R.
Replication;Insert Delayed;Events;Dynamic;Columns;Full-text;Search;GIS;Locale;Settings;subqueries;Timezones;Triggers;XML;Functions;Views;SSL;Show Profile	Embedded database; Designed to execute analytical SQL queries fast; No external dependencies
Statistics
GitHub Stars 6.6K	GitHub Stars -
GitHub Forks 1.9K	GitHub Forks -
Stacks 16.5K	Stacks 49
Followers 12.8K	Followers 60
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	Python C++ R Language

What are some alternatives to MariaDB, DuckDB?

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

DuckDB vs MariaDB: What are the differences?

Introduction

Data Storage Strategy: DuckDB is an in-memory analytical database designed for efficient query performance on large datasets. It stores data in memory to achieve fast processing speeds. On the other hand, MariaDB is a disk-based relational database management system that persists data on disk, which allows for larger data storage but can result in slower query performance compared to DuckDB.
Query Execution: DuckDB implements vectorized query execution, which means it processes data in batches to achieve higher query execution speeds. This approach is optimized for analytical workloads. In contrast, MariaDB performs row-based query execution, which is better suited for relational database operations and transactional workloads.
Supported SQL Features: DuckDB offers a subset of SQL features, focusing on analytic queries and data processing. It provides a limited set of SQL functions and lacks some advanced features like stored procedures and triggers. Conversely, MariaDB provides a comprehensive set of SQL features, including support for stored procedures, triggers, full-text search, and more.
Concurrency Control: DuckDB employs a multiversion concurrency control (MVCC) approach, which allows concurrent reads and writes to the database without blocking access. This concurrency control mechanism ensures high performance in read-heavy workloads. In contrast, MariaDB uses various concurrency control mechanisms, including locking and transaction isolation levels, to handle concurrent access. However, these mechanisms can introduce more overhead and potentially impact performance.
Scalability: DuckDB is designed to handle large datasets efficiently in memory, which makes it suitable for analytical workloads but limits its scalability compared to disk-based systems like MariaDB. MariaDB can handle larger datasets by utilizing disk storage, allowing for more significant storage capacity and horizontal scalability.
Community and Ecosystem: MariaDB has a larger and more established community compared to DuckDB. It benefits from a vast ecosystem, extensive documentation, and community support. DuckDB, while gaining popularity, has a smaller community and a relatively smaller ecosystem. This difference can impact the availability of additional tools, libraries, and plugins for specific use cases.

DuckDB vs MariaDB

Overview

DuckDB vs MariaDB: What are the differences?