DBMS > Atos Standard Common Repository vs. DataFS vs. Google Cloud Datastore vs. H2

System Properties Comparison Atos Standard Common Repository vs. DataFS vs. Google Cloud Datastore vs. H2

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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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

Primary database model

Document store
Key-value store

Object oriented DBMS

Document store

Relational DBMS

Secondary database models

Graph DBMS

Spatial DBMS

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

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Website

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

newdatabase.com

cloud.google.com/datastore

www.h2database.com

Technical documentation

dev.mobiland.com/Overview.xsp

cloud.google.com/datastore/docs

www.h2database.com/html/main.html

Developer

Atos Convergence Creators

Mobiland AG

Google

Thomas Mueller

Initial release

2016

2018

2008

2005

Current release

1703

1.1.263, October 2022

2.2.220, July 2023

License

Commercial or Open Source

commercial

Open Source

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

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

Server operating systems

Linux

Windows

hosted

All OS with a Java VM

Data scheme

Schema and schema-less with LDAP views

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

yes

Typing

predefined data types such as float or date

optional

yes

yes, details here

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

SQL

Support of SQL

SQL-like query language (GQL)

yes

APIs and other access methods

LDAP

.NET Client API
Proprietary client DLL
WinRT client

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

.Net
C
C#
C++
VB.Net

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java

Server-side scripts

Stored procedures

using Google App Engine

Java Stored Procedures and User-Defined Functions

Triggers

yes

no, except callback-events from server when changes happened

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Proprietary Sharding system

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

LDAP bind authentication

Windows-Profile

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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