DBMS > BaseX vs. GigaSpaces vs. IBM Db2 Event Store

System Properties Comparison BaseX vs. GigaSpaces vs. IBM Db2 Event Store

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Editorial information provided by DB-Engines

Name

Description

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

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

Primary database model

Native XML DBMS

Document store
Object oriented DBMS

Values are user defined objects

Event Store
Time Series DBMS

Secondary database models

Graph DBMS
Search engine

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Trend Chart

Score	1.56
Rank	#141	Overall
	#4	Native XML DBMS

Score	0.91
Rank	#197	Overall
	#33	Document stores
	#7	Object oriented DBMS

Score	0.18
Rank	#315	Overall
	#2	Event Stores
	#26	Time Series DBMS

Website

basex.org

www.gigaspaces.com

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

Technical documentation

docs.basex.org

docs.gigaspaces.com/latest/landing.html

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

Developer

BaseX GmbH

Gigaspaces Technologies

IBM

Initial release

2007

2000

2017

Current release

11.2, August 2024

15.5, September 2020

2.0

License

Commercial or Open Source

Open Source

BSD license

Open Source

Apache Version 2; Commercial licenses available

commercial

free developer edition available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, C++, .Net

C and C++

Server operating systems

Linux
OS X
Windows

Linux
macOS
Solaris
Windows

Linux

Linux, macOS, Windows for the developer addition

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

XQuery supports types

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

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

through the embedded Spark runtime

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

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

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

.Net
C++
Java
Python
Scala

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

Server-side scripts

Stored procedures

yes

Triggers

yes

via events

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Active-active shard replication

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

Foreign keys

Referential integrity

Transaction concepts

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

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

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

In-memory capabilities

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

yes

User concepts

Access control

Users with fine-grained authorization concept on 4 levels

Role-based access control

fine grained access rights according to SQL-standard

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