DBMS > Atos Standard Common Repository vs. EJDB vs. IRONdb vs. Newts vs. TimesTen

System Properties Comparison Atos Standard Common Repository vs. EJDB vs. IRONdb vs. Newts vs. TimesTen

Editorial information provided by DB-Engines

Name

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

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Time Series DBMS based on Cassandra

In-Memory RDBMS compatible to Oracle

Primary database model

Document store
Key-value store

Document store

Time Series DBMS

Relational DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

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

github.com/Softmotions/ejdb

www.circonus.com/solutions/time-series-database/

opennms.github.io/newts

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

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

docs.circonus.com/irondb/category/getting-started

github.com/OpenNMS/newts/wiki

docs.oracle.com/database/timesten-18.1

Developer

Atos Convergence Creators

Softmotions

Circonus LLC.

OpenNMS Group

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2016

2012

2017

2014

1998

Current release

1703

V0.10.20, January 2018

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

GPLv2

commercial

Open Source

Apache 2.0

commercial

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++

Java

Server operating systems

Linux

server-less

Linux

Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

Typing

predefined data types such as float or date

optional

yes

string, integer, double, bool, date, object_id

yes

text, numeric, histograms

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 (Circonus Analytics Query Language: CAQL)

yes

APIs and other access methods

LDAP

in-process shared library

HTTP API

HTTP REST
Java API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages with LDAP bindings

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Java

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes, in Lua

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Automatic, metric affinity per node

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

configurable replication factor, datacenter aware

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica 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 per node, eventual consistency across nodes

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

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

Read/Write Locking

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

fine grained access rights according to SQL-standard

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