DBMS > Atos Standard Common Repository vs. Kdb vs. Newts vs. Oracle Berkeley DB vs. RDF4J

System Properties Comparison Atos Standard Common Repository vs. Kdb vs. Newts vs. Oracle Berkeley 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

High performance Time Series DBMS

Time Series DBMS based on Cassandra

Widely used in-process key-value store

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

Time Series DBMS
Vector DBMS

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

RDF store

Secondary database models

Relational DBMS

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

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

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

kx.com

opennms.github.io/newts

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

rdf4j.org

Technical documentation

code.kx.com

github.com/OpenNMS/newts/wiki

docs.oracle.com/cd/E17076_05/html/index.html

rdf4j.org/documentation

Developer

Atos Convergence Creators

Kx Systems, a division of First Derivatives plc

OpenNMS Group

Oracle

originally developed by Sleepycat, which was acquired by Oracle

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

Initial release

2016

2000

kdb was released 2000, kdb+ in 2003

2014

1994

2004

Current release

1703

3.6, May 2018

18.1.40, May 2020

License

Commercial or Open Source

commercial

free 32-bit version

Open Source

Apache 2.0

Open Source

commercial license available

Open Source

Eclipse Distribution License (EDL), v1.0.

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, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Unix
Windows

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

yes

RDF Schemas

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

only with the Berkeley DB XML edition

Secondary indexes

yes

table attribute 'grouped'

yes

SQL

Support of SQL

SQL-like query language (q)

yes

SQL interfaced based on SQLite is available

APIs and other access methods

LDAP

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

HTTP REST
Java 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

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

Java

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

with views

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

horizontal partitioning

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

selectable replication factor

based on Cassandra

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

rights management via user accounts

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
» more

Licensing and pricing models

upon request
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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