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  1. Stackups
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  4. Databases
  5. Greenplum Database vs MariaDB

Greenplum Database vs MariaDB

OverviewDecisionsComparisonAlternatives

Overview

MariaDB
MariaDB
Stacks16.5K
Followers12.8K
Votes468
GitHub Stars6.6K
Forks1.9K
Greenplum Database
Greenplum Database
Stacks45
Followers111
Votes0
GitHub Stars6.2K
Forks1.7K

Greenplum Database vs MariaDB: What are the differences?

Key Differences between Greenplum Database and MariaDB

  1. Storage Architecture: Greenplum Database uses a shared-nothing architecture, where each node in the cluster has its own CPU, memory, disk storage, and network interface. This allows for parallel processing of data across multiple nodes, resulting in high scalability and performance. On the other hand, MariaDB follows a shared-disk architecture, where the database server is connected to a shared disk storage system. This architecture enables high availability and fault tolerance but may limit some aspects of scalability and performance.

  2. Data Distribution Model: Greenplum Database utilizes a distributed data model and employs automatic data distribution across the cluster. This means that the database automatically spreads the data evenly across the nodes, optimizing parallel processing and query execution. In contrast, MariaDB follows a traditional sharding approach, where data is manually partitioned and distributed across different servers. This requires explicit management of data distribution and may result in less efficient query performance.

  3. Data Processing Paradigm: Greenplum Database is specifically designed for parallel processing and analytics workloads. It includes advanced features like columnar storage, massively parallel processing (MPP), and advanced query optimization techniques for efficient handling of large datasets. On the other hand, MariaDB is a general-purpose relational database that is optimized for online transaction processing (OLTP) workloads, focusing more on transactional integrity and data consistency rather than parallel analytics.

  4. SQL Compatibility: Both Greenplum Database and MariaDB support the SQL language, but there are some differences in their SQL dialects and extensions. Greenplum Database adheres to the SQL-2003 standard and implements several SQL extensions for analytics and data manipulation. MariaDB, being a fork of MySQL, follows the MySQL dialect of SQL with some additional features and extensions.

  5. Community and Licensing: Greenplum Database is primarily developed and supported by Pivotal Software, with contributions from an active user community. It is released under the Apache License 2.0. MariaDB, on the other hand, has a larger community of contributors and is developed under the GNU General Public License (GPL). The different licensing models may influence the availability of certain features, commercial support options, and integration with other software ecosystems.

  6. Ecosystem Integration: Greenplum Database is built on top of the PostgreSQL database management system, benefiting from its rich ecosystem of tools, extensions, and community support. It offers seamless integration with various data processing frameworks like Apache Hadoop and Apache Kafka. MariaDB, originally derived from MySQL, has its own ecosystem with a wide range of tools and connectors, as well as compatibility with the MySQL ecosystem.

In summary, Greenplum Database and MariaDB differ in their storage architectures, data distribution models, data processing paradigms, SQL compatibility, community and licensing models, and ecosystem integrations. These differences impact their performance characteristics, scalability options, and suitability for different types of workloads.

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Advice on MariaDB, Greenplum Database

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)

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Comments

Detailed Comparison

MariaDB
MariaDB
Greenplum Database
Greenplum Database

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 a massively parallel processing (MPP) database server with an architecture specially designed to manage large-scale analytic data warehouses and business intelligence workloads. It is based on PostgreSQL open-source technology.

Replication;Insert Delayed;Events;Dynamic;Columns;Full-text;Search;GIS;Locale;Settings;subqueries;Timezones;Triggers;XML;Functions;Views;SSL;Show Profile
Core SQL Conformance; MPP Architecture; Innovative Query Optimization; Polymorphic Data Storage; Integrated In-Database Analytics
Statistics
GitHub Stars
6.6K
GitHub Stars
6.2K
GitHub Forks
1.9K
GitHub Forks
1.7K
Stacks
16.5K
Stacks
45
Followers
12.8K
Followers
111
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
PostgreSQL
PostgreSQL
Kong
Kong
Slick
Slick
Heroku
Heroku
Apache Hive
Apache Hive
Clever Cloud
Clever Cloud
Couchbase
Couchbase
Sequelize
Sequelize
Sails.js
Sails.js
Metabase
Metabase

What are some alternatives to MariaDB, Greenplum Database?

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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