DBMS > EsgynDB vs. RDF4J vs. searchxml vs. Spark SQL

System Properties Comparison EsgynDB vs. RDF4J vs. searchxml vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

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

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

DBMS for structured and unstructured content wrapped with an application server

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

Primary database model

Relational DBMS

RDF store

Native XML DBMS
Search engine

Relational DBMS

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.esgyn.cn

rdf4j.org

www.searchxml.net/category/products

spark.apache.org/sql

Technical documentation

rdf4j.org/documentation

www.searchxml.net/support/handouts

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

Developer

Esgyn

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

informationpartners gmbh

Apache Software Foundation

Initial release

2015

2004

2015

2014

Current release

1.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Open Source

Apache 2.0

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

C++, Java

Java

C++

Scala

Server operating systems

Linux

Linux
OS X
Unix
Windows

Windows

Linux
OS X
Windows

Data scheme

yes

RDF Schemas

schema-free

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

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

RESTful HTTP API
WebDAV
XQuery
XSLT

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Java
PHP
Python

C++

most other programming languages supported via APIs

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

yes

on the application server

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

none

yes

sychronisation to multiple collections

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Isolation support depends on the API used

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Domain, group and role-based access control at the document level and for application services

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