DBMS > HEAVY.AI vs. Hypertable vs. Kinetica vs. MariaDB vs. VelocityDB

System Properties Comparison HEAVY.AI vs. Hypertable vs. Kinetica vs. MariaDB vs. VelocityDB

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

An open source BigTable implementation based on distributed file systems such as Hadoop

Fully vectorized database across both GPUs and CPUs

MySQL application compatible open source RDBMS, enhanced with high availability, security, interoperability and performance capabilities. MariaDB ColumnStore provides a column-oriented storage engine and MariaDB Xpand supports distributed SQL.

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Graph DBMS
Object oriented DBMS

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS

with OQGraph storage engine
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	93.81
Rank	#13	Overall
	#9	Relational DBMS

Score	0.11
Rank	#347	Overall
	#35	Graph DBMS
	#15	Object oriented DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

www.kinetica.com

mariadb.com

Site of MariaDB Corporation
mariadb.org

Site of MariaDB Foundation

velocitydb.com

Technical documentation

docs.heavy.ai

docs.kinetica.com

mariadb.com/kb/en/library

velocitydb.com/UserGuide

Developer

HEAVY.AI, Inc.

Hypertable Inc.

Kinetica

MariaDB Corporation Ab (MariaDB Enterprise),
MariaDB Foundation (community MariaDB Server)

The lead developer Monty Widenius is the original author of MySQL

VelocityDB Inc

Initial release

2016

2009

2012

2009

Fork of MySQL, which was first released in 1995

2011

Current release

5.10, January 2022

0.9.8.11, March 2016

7.1, August 2021

11.3.2, February 2024

7.x

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

GNU version 3. Commercial license available

commercial

Open Source

GPL version 2, commercial enterprise subscription available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++ and CUDA

C++

C, C++

C and C++

Server operating systems

Linux

Linux
OS X
Windows

an inofficial Windows port is available

Linux

FreeBSD
Linux
Solaris
Windows

ColumnStore storage engine not available on Windows

Any that supports .NET

Data scheme

yes

schema-free

yes

Dynamic columns are supported

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC
Thrift
Vega

C++ API
Thrift

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

.Net

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
Perl
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

Ada
C
C#
C++
D
Eiffel
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net

Server-side scripts

Stored procedures

user defined functions

yes

PL/SQL compatibility added with version 10.3

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

several options for horizontal partitioning and Sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor on file system level

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

not for in-memory storage engine

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

GPU vRAM or System RAM

yes

with MEMORY storage engine

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

Based on Windows Authentication

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Web, SaaS and Cloud operational applications that require high availability, scalability...
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Key customers

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Market metrics

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Licensing and pricing models

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