DBMS > AnzoGraph DB vs. HugeGraph vs. Oracle vs. PouchDB

System Properties Comparison AnzoGraph DB vs. HugeGraph vs. Oracle vs. PouchDB

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Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

A fast-speed and highly-scalable Graph DBMS

Widely used RDBMS

JavaScript DBMS with an API inspired by CouchDB

Primary database model

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	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Website

cambridgesemantics.com/anzograph

github.com/hugegraph
hugegraph.apache.org

www.oracle.com/database

pouchdb.com

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

hugegraph.apache.org/docs

docs.oracle.com/en/database

pouchdb.com/guides

Developer

Cambridge Semantics

Baidu

Oracle

Apache Software Foundation

Initial release

2018

1980

2012

Current release

2.3, January 2021

0.9

23c, September 2023

7.1.1, June 2019

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

JavaScript

Server operating systems

Linux

Linux
macOS
Unix

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

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

Schema-free and OWL/RDFS-schema support

yes

Schemaless in JSON and XML columns

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

via views

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

with proprietary extensions

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

C++
Java
Python

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

JavaScript

Server-side scripts

Stored procedures

user defined functions and aggregates

asynchronous Gremlin script jobs

PL/SQL

also stored procedures in Java possible

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

yes

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

Sharding, horizontal partitioning

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes

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

Multi-source replication
Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

via hugegraph-spark

can be realized in PL/SQL

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

not needed 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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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'

yes

User concepts

Access control

Access rights for users and roles

Users, roles and permissions

fine grained access rights according to SQL-standard

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