DBMS > DataFS vs. EventStoreDB vs. GBase vs. Splice Machine

System Properties Comparison DataFS vs. EventStoreDB vs. GBase vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Object oriented DBMS

Event Store

Relational DBMS

Secondary database models

Graph DBMS

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

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

Technical documentation

dev.mobiland.com/Overview.xsp

developers.eventstore.com

splicemachine.com/how-it-works

Developer

Mobiland AG

Event Store Limited

General Data Technology Co., Ltd.

Splice Machine

Initial release

2018

2012

2004

2014

Current release

1.1.263, October 2022

21.2, February 2021

GBase 8a, GBase 8s, GBase 8c

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

commercial

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, Java, Python

Java

Server operating systems

Windows

Linux
Windows

Linux

Linux
OS X
Solaris
Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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

Secondary indexes

yes

SQL

Support of SQL

Standard with numerous extensions

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

ADO.NET
C API
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

yes

Java

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

horizontal partitioning (by range, list and hash) and vertical partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

Windows-Profile

yes

Access rights for users, groups and roles according to SQL-standard

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More resources

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