DBMS > Atos Standard Common Repository vs. DataFS vs. HEAVY.AI vs. SiriDB

System Properties Comparison Atos Standard Common Repository vs. DataFS vs. HEAVY.AI vs. SiriDB

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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.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Open Source Time Series DBMS

Primary database model

Document store
Key-value store

Object oriented DBMS

Relational DBMS

Time Series 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	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Website

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

newdatabase.com

github.com/heavyai/heavydb
www.heavy.ai

siridb.com

Technical documentation

dev.mobiland.com/Overview.xsp

docs.heavy.ai

docs.siridb.com

Developer

Atos Convergence Creators

Mobiland AG

HEAVY.AI, Inc.

Cesbit

Initial release

2016

2018

2016

2017

Current release

1703

1.1.263, October 2022

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++ and CUDA

Server operating systems

Linux

Windows

Linux

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)

yes

Typing

predefined data types such as float or date

optional

yes

Numeric data

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

yes

APIs and other access methods

LDAP

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC
Thrift
Vega

HTTP API

Supported programming languages

All languages with LDAP bindings

.Net
C
C#
C++
VB.Net

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

Triggers

yes

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Proprietary Sharding system

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate 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

Concurrency

Support for concurrent manipulation of data

yes

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

fine grained access rights according to SQL-standard

simple rights management via user accounts

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