DBMS > FatDB vs. HarperDB vs. HyperSQL vs. IRONdb vs. RDF4J

System Properties Comparison FatDB vs. HarperDB vs. HyperSQL vs. IRONdb vs. RDF4J

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

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

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

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.

Primary database model

Document store
Key-value store

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

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.harperdb.io

hsqldb.org

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

rdf4j.org

Technical documentation

docs.harperdb.io/docs

hsqldb.org/web/hsqlDocsFrame.html

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

rdf4j.org/documentation

Developer

FatCloud

HarperDB

Circonus LLC.

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

Initial release

2012

2017

2001

2017

2004

Current release

3.1, August 2021

2.7.2, June 2023

V0.10.20, January 2018

License

Commercial or Open Source

commercial

free community edition available

Open Source

based on BSD license

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

Node.js

Java

C and C++

Java

Server operating systems

Windows

Linux
OS X

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux

Linux
OS X
Unix
Windows

Data scheme

schema-free

dynamic schema

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

JSON data types

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

Via inetgration in SQL Server

SQL-like data manipulation statements

yes

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

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP API

JDBC via HTTP
JDBC
ODBC

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

All languages supporting JDBC/ODBC
Java

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

Java
PHP
Python

Server-side scripts

Stored procedures

yes

via applications

Custom Functions

since release 3.1

Java, SQL

yes, in Lua

yes

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

none

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

the nodes on which a table resides can be defined

none

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

User concepts

Access control

Can implement custom security layer via applications

Access rights for users and roles

fine grained access rights according to SQL-standard

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More resources

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