DBMS > EXASOL vs. RDF4J vs. RocksDB vs. Spark SQL vs. SwayDB

System Properties Comparison EXASOL vs. RDF4J vs. RocksDB vs. Spark SQL vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Primary database model

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

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

Technical documentation

www.exasol.com/resources

rdf4j.org/documentation

github.com/facebook/rocksdb/wiki

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

Developer

Exasol

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

Facebook, Inc.

Apache Software Foundation

Simer Plaha

Initial release

2000

2004

2013

2014

2018

Current release

8.11.4, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

BSD

Open Source

Apache 2.0

Open Source

GNU Affero GPL V3.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Scala

Server operating systems

Linux
OS X
Unix
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

RDF Schemas

schema-free

yes

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

SQL-like DML and DDL statements

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

C++ API
Java API

JDBC
ODBC

Supported programming languages

Java
Lua
Python
R

Java
PHP
Python

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

yes

Atomic execution of operations

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

Access rights for users, groups and roles according to SQL-standard

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