DBMS > BigObject vs. IRONdb vs. RDF4J vs. SingleStore

System Properties Comparison BigObject vs. IRONdb vs. RDF4J vs. SingleStore

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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

Analytic DBMS for real-time computations and queries

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.

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

RDF store

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	5.60
Rank	#62	Overall
	#35	Relational DBMS

Website

bigobject.io

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

rdf4j.org

www.singlestore.com

Technical documentation

docs.bigobject.io

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

rdf4j.org/documentation

docs.singlestore.com

Developer

BigObject, Inc.

Circonus LLC.

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

SingleStore Inc.

Initial release

2015

2017

2004

2013

Current release

V0.10.20, January 2018

8.5, January 2024

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

free developer edition 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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

C and C++

Java

C++, Go

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

Linux
OS X
Unix
Windows

Linux

64 bit version required

Data scheme

yes

schema-free

yes

RDF Schemas

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 DML and DDL statements

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

yes

but no triggers and foreign keys

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

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

Java
PHP
Python

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Lua

yes, in Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic, metric affinity per node

none

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

configurable replication factor, datacenter aware

none

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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

Read/write lock on objects (tables, trees)

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

In-memory capabilities

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

yes

User concepts

Access control

Fine grained access control via users, groups and roles

More information provided by the system vendor

Specific characteristics

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Typical application scenarios

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Key customers

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Market metrics

Customers in various industries worldwide including US and International Industry...
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Licensing and pricing models

F ree Tier and Enterprise Edition
» more

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