DBMS > LeanXcale vs. Oracle Coherence vs. RDF4J vs. SwayDB

System Properties Comparison LeanXcale vs. Oracle Coherence vs. RDF4J vs. SwayDB

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Editorial information provided by DB-Engines

Name

Description

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Oracles in-memory data grid solution

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Key-value store
Relational DBMS

Key-value store

RDF store

Key-value store

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

Score	0.35
Rank	#283	Overall
	#41	Key-value stores
	#128	Relational DBMS

Score	2.02
Rank	#131	Overall
	#22	Key-value stores

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	0.04
Rank	#373	Overall
	#57	Key-value stores

Website

www.leanxcale.com

www.oracle.com/java/coherence

rdf4j.org

swaydb.simer.au

Technical documentation

docs.oracle.com/en/middleware/standalone/coherence

rdf4j.org/documentation

Developer

LeanXcale

Oracle

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

Simer Plaha

Initial release

2015

2007

2004

2018

Current release

14.1, August 2023

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GNU Affero GPL V3.0

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

Java

Scala

Server operating systems

All OS with a Java VM

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

schema-free

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

yes

through Apache Derby

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

JCache
JPA
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
Java
Scala

.Net
C++
Java

Java
PHP
Python

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Live Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with selectable consistency level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID

Isolation support depends on the API used

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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

User concepts

Access control

authentification to access the cache via certificates or http basic authentication

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