DBMS > Apache Druid vs. Apache IoTDB vs. Quasardb vs. RDF4J vs. Trafodion

System Properties Comparison Apache Druid vs. Apache IoTDB vs. Quasardb vs. RDF4J vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

Distributed, high-performance timeseries database

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS
Time Series DBMS

Time Series DBMS

RDF store

Relational DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

Technical documentation

druid.apache.org/docs/latest/design

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

doc.quasar.ai/master

rdf4j.org/documentation

trafodion.apache.org/documentation.html

Developer

Apache Software Foundation and contributors

Apache Software Foundation

quasardb

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

Apache Software Foundation, originally developed by HP

Initial release

2012

2018

2009

2004

2014

Current release

29.0.1, April 2024

1.1.0, April 2023

3.14.1, January 2024

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

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++, Java

Server operating systems

Linux
OS X
Unix

All OS with a Java VM (>= 1.8)

BSD
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Linux

Data scheme

yes

schema-less columns are supported

yes

schema-free

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

SQL for querying

SQL-like query language

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
Native API

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ADO.NET
JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Go
Java
Python
Scala

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Java
PHP
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

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

Sharding

consistent hashing

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

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

Source-replica replication with selectable replication factor

none

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

with Hadoop integration

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

by using LevelDB

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

Transient mode

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

yes

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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