DBMS > Apache Kylin vs. EXASOL vs. OceanBase vs. RDF4J vs. SQLite

System Properties Comparison Apache Kylin vs. EXASOL vs. OceanBase vs. RDF4J vs. SQLite

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

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

A distributed, high available RDBMS compatible with Oracle and MySQL

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

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

RDF store

Relational DBMS

Secondary database models

Document store
Wide column store

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	1.57
Rank	#149	Overall
	#69	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

Technical documentation

kylin.apache.org/docs

www.exasol.com/resources

en.oceanbase.com/docs/oceanbase-database

rdf4j.org/documentation

www.sqlite.org/docs.html

Developer

Apache Software Foundation, originally contributed from eBay Inc

Exasol

OceanBase

previously Alibaba and Ant Group

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

Dwayne Richard Hipp

Initial release

2015

2000

2010

2004

2000

Current release

3.1.0, July 2020

4.3.0, April 2024

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Commercial license available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Public Domain

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

C++

Java

Server operating systems

Linux

Linux
OS X
Unix
Windows

server-less

Data scheme

yes

RDF Schemas

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

ANSI SQL for queries (using Apache Calcite)

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Java
Lua
Python
R

Ada

in MySQL-compatible model
C

in Oracle- and MySQL- compatible models
C++

in Oracle- and MySQL- compatible models
D

in MySQL-compatible model
Delphi

in MySQL-compatible model
Eiffel

in MySQL-compatible model
Erlang

in MySQL-compatible model
Haskell

in MySQL-compatible model
Java

in Oracle- and MySQL- compatible models
JavaScript (Node.js)

in MySQL-compatible model
Objective-C

in MySQL-compatible model
OCaml

in MySQL-compatible model
Perl

in MySQL-compatible model
PHP

in MySQL-compatible model
Python

in MySQL-compatible model
Ruby

in MySQL-compatible model
Scheme

in MySQL-compatible model
Tcl

in MySQL-compatible model

Java
PHP
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

user defined functions

PL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible mode

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by hash, key, range, range columns, list, and list columns)

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.