DBMS > Atos Standard Common Repository vs. DataFS vs. Ehcache vs. IBM Db2 Event Store

System Properties Comparison Atos Standard Common Repository vs. DataFS vs. Ehcache vs. IBM Db2 Event Store

Please select another system to include it in the comparison.

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 widely adopted Java cache with tiered storage options

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Document store
Key-value store

Object oriented DBMS

Key-value store

Event Store
Time Series DBMS

Secondary database models

Graph DBMS

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

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	4.79
Rank	#66	Overall
	#8	Key-value stores

Score	0.18
Rank	#315	Overall
	#2	Event Stores
	#26	Time Series DBMS

Website

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

newdatabase.com

www.ehcache.org

www.ibm.com/products/db2-event-store

Technical documentation

dev.mobiland.com/Overview.xsp

www.ehcache.org/documentation

www.ibm.com/docs/en/db2-event-store

Developer

Atos Convergence Creators

Mobiland AG

Terracotta Inc, owned by Software AG

IBM

Initial release

2016

2018

2009

2017

Current release

1703

1.1.263, October 2022

3.10.0, March 2022

2.0

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

free developer edition available

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 C++

Server operating systems

Linux

Windows

All OS with a Java VM

Linux

Linux, macOS, Windows for the developer addition

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

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

through the embedded Spark runtime

APIs and other access methods

LDAP

.NET Client API
Proprietary client DLL
WinRT client

JCache

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

All languages with LDAP bindings

.Net
C
C#
C++
VB.Net

Java

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Server-side scripts

Stored procedures

yes

Triggers

yes

no, except callback-events from server when changes happened

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Proprietary Sharding system

Sharding

by using Terracotta Server

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

Active-active shard replication

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

Tunable Consistency (Strong, Eventual, Weak)

Eventual 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

yes

supports JTA and can work as an XA resource

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Jira Data Center user? Here's a critical Ehcache vulnerability to spoil your day
22 July 2021, The Register

Atlassian asks customers to patch critical Jira vulnerability
22 July 2021, BleepingComputer

Critical Jira Flaw in Atlassian Could Lead to RCE
22 July 2021, Threatpost

How to partition Sonatype Nexus Repository: Targets, privileges, and roles
9 February 2010, Sonatype Blog

Implementing fallback with cached data
8 October 2018, O'Reilly Media

provided by Google News

Mudhakar Srivatsa
11 December 2023, IBM Research

Best cloud databases of 2022
4 October 2022, ITPro

Why a robust data management strategy is essential today | IBM HDM
19 September 2019, Express Computer

How IBM Is Turning Db2 into an ‘AI Database’
3 June 2019, Datanami

provided by Google News

Share this page