DBMS > Apache IoTDB vs. IRONdb vs. RDF4J vs. TerarkDB vs. Vitess

System Properties Comparison Apache IoTDB vs. IRONdb vs. RDF4J vs. TerarkDB vs. Vitess

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded 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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

iotdb.apache.org

www.circonus.com/solutions/time-series-database/

rdf4j.org

github.com/bytedance/terarkdb

vitess.io

Technical documentation

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

docs.circonus.com/irondb/category/getting-started

rdf4j.org/documentation

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

vitess.io/docs

Developer

Apache Software Foundation

Circonus LLC.

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

ByteDance, originally Terark

The Linux Foundation, PlanetScale

Initial release

2018

2017

2004

2016

2013

Current release

1.1.0, April 2023

V0.10.20, January 2018

15.0.2, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

restricted open source version available

Open Source

Apache Version 2.0, commercial licenses available

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 and C++

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
OS X
Unix
Windows

Docker
Linux
macOS

Data scheme

yes

schema-free

yes

RDF Schemas

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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 (Circonus Analytics Query Language: CAQL)

yes

with proprietary extensions

APIs and other access methods

JDBC
Native API

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

C++ API
Java API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Java
PHP
Python

C++
Java

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

yes, in Lua

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Automatic, metric affinity per node

none

Sharding

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

configurable replication factor, datacenter aware

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 per node, eventual consistency across nodes

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Isolation support depends on the API used

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

yes

Users with fine-grained authorization concept

no user groups or roles

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