DBMS > etcd vs. HEAVY.AI vs. OrigoDB vs. RDFox

System Properties Comparison etcd vs. HEAVY.AI vs. OrigoDB vs. RDFox

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Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A fully ACID in-memory object graph database

High performance knowledge graph and semantic reasoning engine

Primary database model

Key-value store

Relational DBMS

Document store
Object oriented DBMS

Graph DBMS
RDF store

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	7.64
Rank	#54	Overall
	#5	Key-value stores

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	0.03
Rank	#378	Overall
	#51	Document stores
	#18	Object oriented DBMS

Score	0.24
Rank	#309	Overall
	#25	Graph DBMS
	#14	RDF stores

Website

etcd.io
github.com/etcd-io/etcd

github.com/heavyai/heavydb
www.heavy.ai

origodb.com

www.oxfordsemantic.tech

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

docs.heavy.ai

origodb.com/docs

docs.oxfordsemantic.tech

Developer

HEAVY.AI, Inc.

Robert Friberg et al

Oxford Semantic Technologies

Initial release

2016

2009

under the name LiveDB

2017

Current release

3.4, August 2019

5.10, January 2022

6.0, Septermber 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

commercial

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 CUDA

C++

Server operating systems

FreeBSD
Linux
Windows

experimental

Linux

Linux
Windows

Linux
macOS
Windows

Data scheme

schema-free

yes

RDF schemas

Typing

predefined data types such as float or date

yes

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

SQL

Support of SQL

yes

APIs and other access methods

gRPC
JSON over HTTP

JDBC
ODBC
Thrift
Vega

.NET Client API
HTTP API
LINQ

RESTful HTTP API
SPARQL 1.1

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

.Net

C
Java

Server-side scripts

Stored procedures

yes

Triggers

yes, watching key changes

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication

Source-replica replication

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

depending on model

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

Write ahead log

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

fine grained access rights according to SQL-standard

Role based authorization

Roles, resources, and access types

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