DBMS > AnzoGraph DB vs. ArcadeDB vs. IBM Cloudant vs. Oracle Berkeley DB

System Properties Comparison AnzoGraph DB vs. ArcadeDB vs. IBM Cloudant vs. Oracle Berkeley DB

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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

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

Database as a Service offering based on Apache CouchDB

Widely used in-process key-value store

Primary database model

Graph DBMS
RDF store

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Document store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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	0.02
Rank	#366	Overall
	#50	Document stores
	#38	Graph DBMS
	#53	Key-value stores
	#36	Time Series DBMS

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Website

cambridgesemantics.com/anzograph

arcadedb.com

www.ibm.com/products/cloudant

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

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

docs.arcadedb.com

cloud.ibm.com/docs/Cloudant

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Cambridge Semantics

Arcade Data

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2018

2021

2010

1994

Current release

2.3, January 2021

September 2021

18.1.40, May 2020

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2.0

commercial

Open Source

commercial license available

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

Erlang

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux

All OS with a Java VM

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like query language, no joins

yes

SQL interfaced based on SQLite is available

APIs and other access methods

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

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

RESTful HTTP/JSON API

Supported programming languages

C++
Java
Python

Java

C#
Java
JavaScript
Objective-C
PHP
Ruby

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

View functions (Map-Reduce) in JavaScript

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

relationship in graphs

Transaction concepts

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

ACID

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

Durability

Support for making data persistent

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 for users and roles

Access rights for users can be defined per database

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More resources

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