Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

MariaDB

Stacks16.6K

Followers12.8K

Votes468

GitHub Stars6.6K

Forks1.9K

Cassandra vs MariaDB: What are the differences?

Key Differences Between Cassandra and MariaDB

Introduction

Cassandra and MariaDB are both popular database management systems that offer different features and capabilities. Understanding the key differences between these two systems can help in making the right choice for specific use cases.

Scalability: Cassandra is designed for distributed environments and offers seamless scalability. It can handle large amounts of data and supports linear scalability by adding more nodes to the cluster. On the other hand, MariaDB is designed for vertical scalability, which means it can handle increased loads by adding more resources to a single server.
Data Model: Cassandra follows a NoSQL, column-oriented data model, while MariaDB follows a relational data model. Cassandra's data model is highly flexible, allowing for dynamic and unpredictable data types, and providing horizontal scalability. In contrast, MariaDB provides a structured organization of data using tables, rows, and columns, making it suitable for complex relationships and traditional SQL queries.
Consistency and Availability: Cassandra is designed for high availability and eventual consistency. It employs a distributed architecture with multiple replicas which allows for high uptime but may introduce some level of data inconsistency. MariaDB focuses on maintaining strict consistency across its nodes, sacrificing availability under certain circumstances.
Replication: Cassandra uses a masterless distributed architecture, where all nodes in the cluster are equal and can accept write requests. It provides seamless data replication across multiple nodes for fault tolerance and high availability. On the other hand, MariaDB follows a master-slave replication scheme, where there is a single master node for write operations and multiple slave nodes for read operations.
High Performance: Cassandra is known for its high throughput and low latency characteristics. It can handle a massive number of read and write operations simultaneously. MariaDB, being a relational database, provides efficient querying capabilities with support for complex joins and transactions, but may not perform as well as Cassandra in scenarios with massive write operations or high data volumes.
Suitability: Due to its distributed nature, Cassandra is commonly used for handling large-scale data systems such as social media platforms, IoT applications, and financial systems. MariaDB, being a traditional relational database, is well-suited for applications that require ACID compliance and complex data relationships, such as e-commerce platforms, content management systems, and business applications.

In Summary, Cassandra is a distributed NoSQL database with high scalability and availability, while MariaDB is a relational database with strong consistency and efficient querying capabilities. Choosing between them depends on the specific requirements and use cases of the application.

Advice on Cassandra, MariaDB

Micha

CEO & Co-Founder at Dechea

May 27, 2022

Decided

Fauna is a serverless database where you store data as JSON. Also, you have build in a HTTP GraphQL interface with a full authentication & authorization layer. That means you can skip your Backend and call it directly from the Frontend. With the power, that you can write data transformation function within Fauna with her own language called FQL, we're getting a blazing fast application.

Also, Fauna takes care about scaling and backups (All data are sharded on three different locations on the globe). That means we can fully focus on writing business logic and don't have to worry anymore about infrastructure.

93k views93k

Comments

Maxim

student at USI

Aug 25, 2020

Needs adviceon

Node.js

Mongoose

PostgreSQL

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

Krishna Chaitanya

Head of Technology at Adonmo

Jun 27, 2021

Review

For such a more realtime-focused, data-centered application like an exchange, it's not the frontend or backend that matter much. In fact for that, they can do away with any of the popular frameworks like React/Vue/Angular for the frontend and Go/Python for the backend. For example uniswap's frontend (although much simpler than binance) is built in React. The main interesting part here would be how they are able to handle updating data so quickly. In my opinion, they might be heavily reliant on realtime processing systems like Kafka+Kafka Streams, Apache Flink or Apache Spark Stream or similar. For more processing heavy but not so real-time processing, they might be relying on OLAP and/or warehousing tools like Cassandra/Redshift. They could have also optimized few high frequent queries using NoSQL stores like mongodb (for persistance) and in-memory cache like Redis (for further perfomance boost to get millisecond latencies).

53.8k views53.8k

Comments

Detailed Comparison

Cassandra	MariaDB
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.	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.
-	Replication;Insert Delayed;Events;Dynamic;Columns;Full-text;Search;GIS;Locale;Settings;subqueries;Timezones;Triggers;XML;Functions;Views;SSL;Show Profile
Statistics
GitHub Stars 9.5K	GitHub Stars 6.6K
GitHub Forks 3.8K	GitHub Forks 1.9K
Stacks 3.6K	Stacks 16.6K
Followers 3.5K	Followers 12.8K
Votes 507	Votes 468
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Updates 1 Size	Pros 149 Drop-in mysql replacement 100 Great performance 74 Open source 55 Free 44 Easy setup

What are some alternatives to Cassandra, MariaDB?

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.

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.

CouchDB

Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.