DBMS > EsgynDB vs. KairosDB vs. RDF4J vs. Stardog vs. TimesTen

System Properties Comparison EsgynDB vs. KairosDB vs. RDF4J vs. Stardog vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Distributed Time Series DBMS based on Cassandra or H2

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

In-Memory RDBMS compatible to Oracle

Primary database model

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

www.esgyn.cn

github.com/kairosdb/kairosdb

rdf4j.org

www.stardog.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

kairosdb.github.io

rdf4j.org/documentation

docs.stardog.com

docs.oracle.com/database/timesten-18.1

Developer

Esgyn

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Stardog-Union

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2015

2013

2004

2010

1998

Current release

1.2.2, November 2018

7.3.0, May 2020

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

commercial

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

Java

Server operating systems

Linux

Linux
OS X
Windows

Linux
OS X
Unix
Windows

Linux
macOS
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

schema-free and OWL/RDFS-schema support

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

Graphite protocol
HTTP REST
Telnet API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Java
JavaScript

Node.js
PHP
Python

Java
PHP
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

Java Stored Procedures

yes

user defined functions and aggregates, HTTP Server extensions in Java

PL/SQL

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

selectable replication factor

based on Cassandra

none

Multi-source replication in HA-Cluster

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency in HA-Cluster

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

relationships in graphs

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

simple password-based access control

Access rights for users and roles

fine grained access rights according to SQL-standard

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