DBMS > CouchDB vs. HarperDB vs. OrigoDB vs. RDF4J

System Properties Comparison CouchDB vs. HarperDB vs. OrigoDB vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

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.

A fully ACID in-memory object graph database

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

Primary database model

Document store

Document store
Object oriented DBMS

RDF store

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

Technical documentation

docs.couchdb.org/en/stable

docs.harperdb.io/docs

origodb.com/docs

rdf4j.org/documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

HarperDB

Robert Friberg et al

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

Initial release

2005

2017

2009

under the name LiveDB

2004

Current release

3.3.3, December 2023

3.1, August 2021

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free community edition available

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

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Implementation language

Erlang

Node.js

Java

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux
OS X

Linux
Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free

dynamic schema

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

JSON data types

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

via views

yes

SQL

Support of SQL

SQL-like data manipulation statements

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

.NET Client API
HTTP API
LINQ

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.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

.Net

Java
PHP
Python

Server-side scripts

Stored procedures

View functions in JavaScript

Custom Functions

since release 3.1

yes

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

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

horizontal partitioning

client side managed; servers are not synchronized

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

the nodes on which a table resides can be defined

Source-replica replication

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

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

atomic operations within a single document possible

Atomic execution of specific operations

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

Write ahead log

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

Access rights for users can be defined per database

Access rights for users and roles

Role based authorization

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More resources

DB-Engines blog posts

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