DBMS > Geode vs. JanusGraph vs. OrientDB vs. Riak KV

System Properties Comparison Geode vs. JanusGraph vs. OrientDB vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Multi-model DBMS (Document, Graph, Key/Value)

Distributed, fault tolerant key-value store

Primary database model

Key-value store

Graph DBMS

Document store
Graph DBMS
Key-value store

Key-value store

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	1.51
Rank	#147	Overall
	#25	Key-value stores

Score	1.85
Rank	#134	Overall
	#12	Graph DBMS

Score	3.02
Rank	#88	Overall
	#16	Document stores
	#6	Graph DBMS
	#12	Key-value stores

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Website

geode.apache.org

janusgraph.org

orientdb.org

Technical documentation

geode.apache.org/docs

docs.janusgraph.org

www.orientdb.com/docs/last/index.html

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

Developer

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

Linux Foundation; originally developed as Titan by Aurelius

OrientDB LTD; CallidusCloud; SAP

OpenSource, formerly Basho Technologies

Initial release

2002

2017

2010

2009

Current release

1.1, February 2017

1.0.0, October 2023

3.2.29, March 2024

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

Apache 2.0

Open Source

Apache version 2

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

Erlang

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

Linux
OS X
Unix
Windows

All OS with a Java JDK (>= JDK 6)

Linux
OS X

Data scheme

schema-free

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

schema-free

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

restricted

SQL

Support of SQL

SQL-like query language (OQL)

SQL-like query language, no joins

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

HTTP API
Native Erlang Interface

Supported programming languages

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

Clojure
Java
Python

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

user defined functions

yes

Java, Javascript

Erlang

Triggers

yes

Cache Event Listeners

yes

Hooks

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Multi-source replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

could be achieved with distributed queries

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

relationship in graphs

links between data sets can be stored

Transaction concepts

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

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

Access rights per client and object definable

User authentification and security via Rexster Graph Server

Access rights for users and roles; record level security configurable

yes, using Riak Security

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More resources

DB-Engines blog posts

Graph DBMS increased their popularity by 500% within the last 2 years
3 March 2015, Paul Andlinger

Graph DBMSs are gaining in popularity faster than any other database category
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