DBMS > Graph Engine vs. HugeGraph vs. Oracle NoSQL vs. Riak KV

System Properties Comparison Graph Engine vs. HugeGraph vs. Oracle NoSQL vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A fast-speed and highly-scalable Graph DBMS

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Distributed, fault tolerant key-value store

Primary database model

Graph DBMS
Key-value store

Graph DBMS

Document store
Key-value store
Relational DBMS

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	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

www.graphengine.io

github.com/hugegraph
hugegraph.apache.org

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

www.graphengine.io/docs/manual

hugegraph.apache.org/docs

docs.oracle.com/en/database/other-databases/nosql-database/index.html

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

Developer

Microsoft

Baidu

Oracle

OpenSource, formerly Basho Technologies

Initial release

2010

2018

2011

2009

Current release

0.9

24.1, May 2024

3.2.0, December 2022

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2.0

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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

.NET and C

Java

Erlang

Server operating systems

.NET

Linux
macOS
Unix

Linux
Solaris SPARC/x86

Linux
OS X

Data scheme

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

Typing

predefined data types such as float or date

yes

optional

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

also supports composite index and range index

yes

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

C#
C++
F#
Visual Basic

Groovy
Java
Python

C
C#
Go
Java
JavaScript (Node.js)
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

asynchronous Gremlin script jobs

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

links between data sets can be stored

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

In-memory capabilities

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

yes

off heap cache

User concepts

Access control

Users, roles and permissions

Access rights for users and roles

yes, using Riak Security

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More resources

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