DBMS > Hazelcast vs. HEAVY.AI vs. Kinetica vs. Microsoft SQL Server vs. SAP SQL Anywhere

System Properties Comparison Hazelcast vs. HEAVY.AI vs. Kinetica vs. Microsoft SQL Server vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

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

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Key-value store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

hazelcast.com

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

www.kinetica.com

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

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

hazelcast.org/imdg/docs

docs.heavy.ai

docs.kinetica.com

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

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Hazelcast

HEAVY.AI, Inc.

Kinetica

Microsoft

SAP

formerly Sybase

Initial release

2008

2016

2012

1989

1992

Current release

5.3.6, November 2023

5.10, January 2022

7.1, August 2021

SQL Server 2022, November 2022

17, July 2015

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2; enterprise edition available

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++ and CUDA

C, C++

C++

Server operating systems

All OS with a Java VM

Linux

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

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

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

user defined functions

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

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Events

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Multi-source replication

Source-replica replication

yes, but depending on the SQL-Server Edition

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes

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

Role-based 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

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