DBMS > HEAVY.AI vs. Hypertable vs. JaguarDB vs. Kinetica vs. MySQL

System Properties Comparison HEAVY.AI vs. Hypertable vs. JaguarDB vs. Kinetica vs. MySQL

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

Performant, highly scalable DBMS for AI and IoT applications

Fully vectorized database across both GPUs and CPUs

Widely used open source RDBMS

Primary database model

Relational DBMS

Wide column store

Key-value store
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	0.09
Rank	#356	Overall
	#53	Key-value stores
	#13	Vector DBMS

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Website

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

www.jaguardb.com

www.kinetica.com

www.mysql.com

Technical documentation

docs.heavy.ai

www.jaguardb.com/support.html

docs.kinetica.com

dev.mysql.com/doc

Developer

HEAVY.AI, Inc.

Hypertable Inc.

DataJaguar, Inc.

Kinetica

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2016

2009

2015

2012

1995

Current release

5.10, January 2022

0.9.8.11, March 2016

3.3 July 2023

7.1, August 2021

8.3.0, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

GNU version 3. Commercial license available

Open Source

GPL V3.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

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.

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Implementation language

C++ and CUDA

C++

the server part. Clients available in other languages

C, C++

C and C++

Server operating systems

Linux

Linux
OS X
Windows

an inofficial Windows port is available

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC
Thrift
Vega

C++ API
Thrift

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
Perl
PHP
Python
Ruby

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C++
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

user defined functions

yes

proprietary syntax

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor on file system level

Multi-source replication

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

Eventual 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

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

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

User concepts

Access control

fine grained access rights according to SQL-standard

rights management via user accounts

Access rights for users and roles on table level

Users with fine-grained authorization concept

no user groups or roles

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