DBMS > IRONdb vs. jBASE vs. Quasardb vs. Stardog

System Properties Comparison IRONdb vs. jBASE vs. Quasardb vs. Stardog

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Editorial information provided by DB-Engines

Name

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

Description

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

A robust multi-value DBMS comprising development tools and middleware

Distributed, high-performance timeseries database

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

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

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

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

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

quasar.ai

www.stardog.com

Technical documentation

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

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

doc.quasar.ai/master

docs.stardog.com

Developer

Circonus LLC.

Rocket Software (formerly Zumasys)

quasardb

Stardog-Union

Initial release

2017

1991

2009

2010

Current release

V0.10.20, January 2018

5.7

3.14.1, January 2024

7.3.0, May 2020

License

Commercial or Open Source

commercial

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

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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 and C++

C++

Java

Server operating systems

Linux

AIX
Linux
Windows

BSD
Linux
OS X
Windows

Linux
macOS
Windows

Data scheme

schema-free

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

text, numeric, histograms

optional

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.

yes

Import/export of XML data possible

Secondary indexes

yes

with tags

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

Embedded SQL for jBASE in BASIC

SQL-like query language

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

HTTP API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

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

.Net
Basic
Jabbascript
Java

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes, in Lua

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

yes

Source-replica replication with selectable replication factor

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

Access rights can be defined down to the item level

Cryptographically strong user authentication and audit trail

Access rights for users and roles

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