DBMS > Badger vs. Geode vs. HEAVY.AI vs. RDF4J

System Properties Comparison Badger vs. Geode vs. HEAVY.AI vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

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

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

Primary database model

Key-value store

Relational DBMS

RDF store

Secondary database models

Spatial DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

github.com/dgraph-io/badger

geode.apache.org

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

rdf4j.org

Technical documentation

godoc.org/github.com/dgraph-io/badger

geode.apache.org/docs

docs.heavy.ai

rdf4j.org/documentation

Developer

DGraph Labs

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

HEAVY.AI, Inc.

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

Initial release

2017

2002

2016

2004

Current release

1.1, February 2017

5.10, January 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

Apache Version 2; enterprise 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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++ and CUDA

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

the JDK (8 or later) is also required

Linux

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

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 query language (OQL)

yes

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

All languages supporting JDBC/ODBC/Thrift
Python

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes, on a single node

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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 per client and object definable

fine grained access rights according to SQL-standard

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