DBMS > Apache IoTDB vs. jBASE vs. RDF4J vs. StarRocks vs. Tibero

System Properties Comparison Apache IoTDB vs. jBASE vs. RDF4J vs. StarRocks vs. Tibero

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

A robust multi-value DBMS comprising development tools and middleware

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

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

A secure RDBMS, designed for easy portability from Oracle

Primary database model

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	1.04
Rank	#189	Overall
	#88	Relational DBMS

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

iotdb.apache.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

rdf4j.org

www.starrocks.io

us.tmaxsoft.com/products/tibero

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

rdf4j.org/documentation

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Apache Software Foundation

Rocket Software (formerly Zumasys)

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

The Linux Foundation

since Feb 2023

TmaxSoft

Initial release

2018

1991

2004

2020

2003

Current release

1.1.0, April 2023

5.7

2.5.3, March 2023

6, April 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache Version 2.0

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

C++, Java

C and Assembler

Server operating systems

All OS with a Java VM (>= 1.8)

AIX
Linux
Windows

Linux
OS X
Unix
Windows

Linux

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

optional

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

SQL-like query language

Embedded SQL for jBASE in BASIC

yes

APIs and other access methods

JDBC
Native API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
MySQL protocol

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
Basic
Jabbascript
Java

Java
PHP
Python

Java

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions

Persistent Stored Procedure (PSM)

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

none

horizontal partitioning (by range and hash)

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

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

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

planned for next version

User concepts

Access control

yes

Access rights can be defined down to the item level

Role based access control and fine grained access rights

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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