DBMS > GigaSpaces vs. IBM Db2 Event Store vs. MySQL vs. RDFox

System Properties Comparison GigaSpaces vs. IBM Db2 Event Store vs. MySQL vs. RDFox

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Editorial information provided by DB-Engines

Name

Description

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Distributed Event Store optimized for Internet of Things use cases

Widely used open source RDBMS

High performance knowledge graph and semantic reasoning engine

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Event Store
Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Graph DBMS
RDF store

Secondary database models

Graph DBMS
Search engine

Document store
Spatial DBMS

Relational DBMS

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

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.29
Rank	#300	Overall
	#24	Graph DBMS
	#13	RDF stores

Website

www.gigaspaces.com

www.ibm.com/products/db2-event-store

www.mysql.com

www.oxfordsemantic.tech

Technical documentation

docs.gigaspaces.com/latest/landing.html

www.ibm.com/docs/en/db2-event-store

dev.mysql.com/doc

docs.oxfordsemantic.tech

Developer

Gigaspaces Technologies

IBM

Oracle

since 2010, originally MySQL AB, then Sun

Oxford Semantic Technologies

Initial release

2000

2017

1995

2017

Current release

15.5, September 2020

2.0

8.4.0, April 2024

6.0, Septermber 2022

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

free developer edition available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

Java, C++, .Net

C and C++

C++

Server operating systems

Linux
macOS
Solaris
Windows

Linux

Linux, macOS, Windows for the developer addition

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Data scheme

schema-free

yes

RDF schemas

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

through the embedded Spark runtime

yes

with proprietary extensions

APIs and other access methods

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP API
SPARQL 1.1

Supported programming languages

.Net
C++
Java
Python
Scala

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

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

C
Java

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Active-active shard replication

Multi-source replication
Source-replica replication

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Eventual Consistency

Immediate Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

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

No - written data is immutable

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

Roles, resources, and access types

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