DBMS > Ehcache vs. Kinetica vs. RDF4J vs. Trafodion

System Properties Comparison Ehcache vs. Kinetica vs. RDF4J vs. Trafodion

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

A widely adopted Java cache with tiered storage options

Fully vectorized database across both GPUs and CPUs

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

Technical documentation

www.ehcache.org/documentation

docs.kinetica.com

rdf4j.org/documentation

trafodion.apache.org/documentation.html

Developer

Terracotta Inc, owned by Software AG

Kinetica

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

Apache Software Foundation, originally developed by HP

Initial release

2009

2012

2004

2014

Current release

3.10.0, March 2022

7.1, August 2021

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++

Java

C++, Java

Server operating systems

All OS with a Java VM

Linux

Linux
OS X
Unix
Windows

Linux

Data scheme

schema-free

yes

RDF Schemas

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JCache

JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ADO.NET
JDBC
ODBC

Supported programming languages

Java

C++
Java
JavaScript (Node.js)
Python

Java
PHP
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions

yes

Java Stored Procedures

Triggers

yes

Cache Event Listeners

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

Source-replica replication

none

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

in-memory storage is supported as well

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

User concepts

Access control

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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