DBMS > Apache IoTDB vs. RDF4J vs. TempoIQ vs. TerminusDB

System Properties Comparison Apache IoTDB vs. RDF4J vs. TempoIQ vs. TerminusDB

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Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

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

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

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Secondary database models

Document store
RDF store

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

iotdb.apache.org

rdf4j.org

tempoiq.com (offline)

terminusdb.com

Technical documentation

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

rdf4j.org/documentation

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation

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

TempoIQ

DataChemist Ltd.

Initial release

2018

2004

2012

2018

Current release

1.1.0, April 2023

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Prolog, Rust

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Unix
Windows

Linux

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (WOQL)

APIs and other access methods

JDBC
Native API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

HTTP API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java
PHP
Python

C#
Java
JavaScript

Node.js
Python
Ruby

JavaScript
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Realtime Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

Graph Partitioning

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

none

Journaling Streams

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

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 journaling

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

yes

simple authentication-based access control

Role-based access control

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