DBMS > ArcadeDB vs. Hive vs. Quasardb vs. RDF4J vs. Riak KV

System Properties Comparison ArcadeDB vs. Hive vs. Quasardb vs. RDF4J vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Distributed, high-performance timeseries database

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

Distributed, fault tolerant key-value store

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

with links between data sets and object tags for the creation of secondary indexes

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

Score	0.02
Rank	#366	Overall
	#50	Document stores
	#38	Graph DBMS
	#53	Key-value stores
	#36	Time Series DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

Technical documentation

docs.arcadedb.com

cwiki.apache.org/confluence/display/Hive/Home

doc.quasar.ai/master

rdf4j.org/documentation

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Arcade Data

Apache Software Foundation

initially developed by Facebook

quasardb

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

OpenSource, formerly Basho Technologies

Initial release

2021

2012

2009

2004

2009

Current release

September 2021

3.1.3, April 2022

3.14.1, January 2024

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2

commercial

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

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache version 2, commercial enterprise edition

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

Java

C++

Java

Erlang

Server operating systems

All OS with a Java VM

BSD
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Linux
OS X

Data scheme

schema-free

yes

schema-free

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

with tags

yes

restricted

SQL

Support of SQL

SQL-like query language, no joins

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

JDBC
ODBC
Thrift

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

HTTP API
Native Erlang Interface

Supported programming languages

Java

C++
Java
PHP
Python

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

Java
PHP
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

none

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Source-replica replication with selectable replication factor

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

links between data sets can be stored

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

in-memory storage is supported as well

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Access rights for users, groups and roles

Cryptographically strong user authentication and audit trail

yes, using Riak Security

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More resources

DB-Engines blog posts

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