DBMS > Amazon Neptune vs. HugeGraph vs. Oracle vs. searchxml vs. Tibero

System Properties Comparison Amazon Neptune vs. HugeGraph vs. Oracle vs. searchxml vs. Tibero

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

A fast-speed and highly-scalable Graph DBMS

Widely used RDBMS

DBMS for structured and unstructured content wrapped with an application server

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Graph DBMS
RDF store

Graph DBMS

Relational DBMS

Native XML DBMS
Search engine

Relational DBMS

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

aws.amazon.com/neptune

github.com/hugegraph
hugegraph.apache.org

www.oracle.com/database

www.searchxml.net/category/products

us.tmaxsoft.com/products/tibero

Technical documentation

aws.amazon.com/neptune/developer-resources

hugegraph.apache.org/docs

docs.oracle.com/en/database

www.searchxml.net/support/handouts

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Amazon

Baidu

Oracle

informationpartners gmbh

TmaxSoft

Initial release

2017

2018

1980

2015

2003

Current release

0.9

23c, September 2023

1.0

6, April 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C and C++

C++

C and Assembler

Server operating systems

hosted

Linux
macOS
Unix

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

schema-free

yes

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

RESTful HTTP API
WebDAV
XQuery
XSLT

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

Groovy
Java
Python

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

C++

most other programming languages supported via APIs

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

PL/SQL

also stored procedures in Java possible

yes

on the application server

Persistent Stored Procedure (PSM)

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

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

Sharding, horizontal partitioning

none

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

yes

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

Multi-source replication
Source-replica replication

yes

sychronisation to multiple collections

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

edges in graph

yes

Transaction concepts

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

ACID

isolation level can be parameterized

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

planned for next version

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Users, roles and permissions

fine grained access rights according to SQL-standard

Domain, group and role-based access control at the document level and for application services

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.