DBMS > Atos Standard Common Repository vs. BigchainDB vs. H2 vs. Microsoft Azure Cosmos DB vs. RDF4J

System Properties Comparison Atos Standard Common Repository vs. BigchainDB vs. H2 vs. Microsoft Azure Cosmos DB vs. RDF4J

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Globally distributed, horizontally scalable, multi-model database service

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Document store
Key-value store

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

RDF store

Secondary database models

Spatial DBMS

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.bigchaindb.com

www.h2database.com

azure.microsoft.com/services/cosmos-db

rdf4j.org

Technical documentation

bigchaindb.readthedocs.io/en/latest

www.h2database.com/html/main.html

learn.microsoft.com/azure/cosmos-db

rdf4j.org/documentation

Developer

Atos Convergence Creators

Thomas Mueller

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2016

2005

2014

2004

Current release

1703

2.2.220, July 2023

License

Commercial or Open Source

commercial

Open Source

AGPL v3

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

Python

Java

Server operating systems

Linux

All OS with a Java VM

hosted

Linux
OS X
Unix
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

optional

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

LDAP

CLI Client
RESTful HTTP API

JDBC
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

All languages with LDAP bindings

Go
Haskell
Java
JavaScript
Python
Ruby

Java

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Java
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

JavaScript

yes

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

User concepts

Access control

LDAP bind authentication

yes

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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