DBMS > GeoSpock vs. Hazelcast vs. HEAVY.AI vs. Microsoft SQL Server vs. Tibero

System Properties Comparison GeoSpock vs. Hazelcast vs. HEAVY.AI vs. Microsoft SQL Server vs. Tibero

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Spatial and temporal data processing engine for extreme data scale

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

Microsofts flagship relational DBMS

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Time Series DBMS

Document store

JSON support with IMDG 3.12

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	6.87
Rank	#55	Overall
	#6	Key-value stores

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	1.81
Rank	#143	Overall
	#65	Relational DBMS

Website

geospock.com

hazelcast.com

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

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

us.tmaxsoft.com/products/tibero

Technical documentation

hazelcast.org/imdg/docs

docs.heavy.ai

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

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

GeoSpock

Hazelcast

HEAVY.AI, Inc.

Microsoft

TmaxSoft

Initial release

2008

2016

1989

2003

Current release

2.0, September 2019

5.3.6, November 2023

5.10, January 2022

SQL Server 2022, November 2022

6, April 2015

License

Commercial or Open Source

commercial

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

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java, Javascript

Java

C++ and CUDA

C++

C and Assembler

Server operating systems

hosted

All OS with a Java VM

Linux

Linux
Windows

AIX
HP-UX
Linux
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

the object must implement a serialization strategy

yes

Secondary indexes

temporal, categorical

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

SQL-like query language

yes

APIs and other access methods

JDBC

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

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

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

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

Persistent Stored Procedure (PSM)

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

Round robin

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

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Multi-source replication

yes, but depending on the SQL-Server Edition

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 selectable by user

Raft Consensus Algorithm

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

planned for next version

User concepts

Access control

Access rights for users can be defined per table

Role-based access control

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.