DBMS > DolphinDB vs. IRONdb vs. RDF4J vs. TerarkDB vs. WakandaDB

System Properties Comparison DolphinDB vs. IRONdb vs. RDF4J vs. TerarkDB vs. WakandaDB

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

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

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Secondary database models

Relational DBMS

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

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

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

www.dolphindb.com

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

rdf4j.org

github.com/bytedance/terarkdb

wakanda.github.io

Technical documentation

docs.dolphindb.cn/en/help200/index.html

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

rdf4j.org/documentation

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

wakanda.github.io/doc

Developer

DolphinDB, Inc

Circonus LLC.

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

ByteDance, originally Terark

Wakanda SAS

Initial release

2018

2017

2004

2016

2012

Current release

v2.00.4, January 2022

V0.10.20, January 2018

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

free community version available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

restricted open source version available

Open Source

AGPLv3, extended commercial license 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

C++

C and C++

Java

C++

C++, JavaScript

Server operating systems

Linux
Windows

Linux

Linux
OS X
Unix
Windows

Linux
OS X
Windows

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)

APIs and other access methods

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

C++ API
Java API

RESTful HTTP API

Supported programming languages

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

.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

JavaScript

Server-side scripts

Stored procedures

yes

yes, in Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

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

User concepts

Access control

Administrators, Users, Groups

yes

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