DBMS > HugeGraph vs. Oracle NoSQL vs. OrigoDB vs. Sadas Engine

System Properties Comparison HugeGraph vs. Oracle NoSQL vs. OrigoDB vs. Sadas Engine

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Editorial information provided by DB-Engines

Name

Description

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

A fully ACID in-memory object graph database

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Graph DBMS

Document store
Key-value store
Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

github.com/hugegraph
hugegraph.apache.org

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

origodb.com

www.sadasengine.com

Technical documentation

hugegraph.apache.org/docs

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

origodb.com/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Baidu

Oracle

Robert Friberg et al

SADAS s.r.l.

Initial release

2018

2011

2009

under the name LiveDB

2006

Current release

0.9

23.3, December 2023

8.0

License

Commercial or Open Source

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

commercial

free trial version available

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++

Server operating systems

Linux
macOS
Unix

Linux
Solaris SPARC/x86

Linux
Windows

AIX
Linux
Windows

Data scheme

yes

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

yes

Typing

predefined data types such as float or date

yes

optional

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

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

RESTful HTTP API

.NET Client API
HTTP API
LINQ

JDBC
ODBC
Proprietary protocol

Supported programming languages

Groovy
Java
Python

C
C#
Go
Java
JavaScript (Node.js)
Python

.Net

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding

horizontal partitioning

client side managed; servers are not synchronized

horizontal partitioning

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

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

depending on model

yes

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

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

off heap cache

yes

managed by 'Learn by Usage'

User concepts

Access control

Users, roles and permissions

Access rights for users and roles

Role based authorization

Access rights for users, groups and roles according to SQL-standard

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More resources

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