DBMS > HEAVY.AI vs. Kinetica vs. Microsoft SQL Server vs. Oracle Rdb vs. Quasardb

System Properties Comparison HEAVY.AI vs. Kinetica vs. Microsoft SQL Server vs. Oracle Rdb vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

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

Fully vectorized database across both GPUs and CPUs

Microsofts flagship relational DBMS

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	1.04
Rank	#184	Overall
	#84	Relational DBMS

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Website

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

www.kinetica.com

www.microsoft.com/en-us/sql-server

www.oracle.com/database/technologies/related/rdb.html

quasar.ai

Technical documentation

docs.heavy.ai

docs.kinetica.com

learn.microsoft.com/en-US/sql/sql-server

www.oracle.com/database/technologies/related/rdb-doc.html

doc.quasar.ai/master

Developer

HEAVY.AI, Inc.

Kinetica

Microsoft

Oracle, originally developed by Digital Equipment Corporation (DEC)

quasardb

Initial release

2016

2012

1989

1984

2009

Current release

5.10, January 2022

7.1, August 2021

SQL Server 2022, November 2022

7.4.1.1, 2021

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

restricted free version is available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++ and CUDA

C, C++

C++

Server operating systems

Linux

Linux
Windows

HP Open VMS

BSD
Linux
OS X
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

yes

with tags

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
JavaScript (Node.js)
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

yes, but depending on the SQL-Server Edition

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

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

Transaction concepts

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

ACID

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

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

Transient mode

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

Cryptographically strong user authentication and audit trail

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