DBMS > Oracle Coherence vs. RDF4J vs. Spark SQL vs. SwayDB vs. Transwarp Hippo

System Properties Comparison Oracle Coherence vs. RDF4J vs. Spark SQL vs. SwayDB vs. Transwarp Hippo

Editorial information provided by DB-Engines

Name

Description

Oracles in-memory data grid solution

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

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

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

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

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	0.05
Rank	#386	Overall
	#16	Vector DBMS

Website

www.oracle.com/java/coherence

rdf4j.org

spark.apache.org/sql

swaydb.simer.au

www.transwarp.cn/en/subproduct/hippo

Technical documentation

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

rdf4j.org/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Oracle

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

Apache Software Foundation

Simer Plaha

Initial release

2007

2004

2014

2018

2023

Current release

14.1, August 2023

3.5.0 ( 2.13), September 2023

1.0, May 2023

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

Open Source

GNU Affero GPL V3.0

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

Java

Scala

C++

Server operating systems

All OS with a Java VM

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Linux
macOS

Data scheme

schema-free

yes

RDF Schemas

yes

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

APIs and other access methods

JCache
JPA
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

RESTful HTTP API

Supported programming languages

.Net
C++
Java

Java
PHP
Python

Java
Python
R
Scala

Java
Kotlin
Scala

C++
Java
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Live Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

none

Sharding

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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

Role based access control and fine grained access rights

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More resources

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