DBMS > Derby vs. Kyligence Enterprise vs. RDF4J vs. Spark SQL vs. STSdb

System Properties Comparison Derby vs. Kyligence Enterprise vs. RDF4J vs. Spark SQL vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

A distributed analytics engine for big data, built on top of Apache Kylin

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

db.apache.org/derby

kyligence.io/kyligence-enterprise

rdf4j.org

spark.apache.org/sql

github.com/STSSoft/STSdb4

Technical documentation

db.apache.org/derby/manuals/index.html

rdf4j.org/documentation

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

Developer

Apache Software Foundation

Kyligence, Inc.

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

Apache Software Foundation

STS Soft SC

Initial release

1997

2016

2004

2014

2011

Current release

10.17.1.0, November 2023

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

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

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Windows

Data scheme

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

ANSI SQL for queries (using Apache Calcite)

SQL-like DML and DDL statements

APIs and other access methods

JDBC

JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

.NET Client API

Supported programming languages

Java

Java
PHP
Python

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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