DBMS > Atos Standard Common Repository vs. BigchainDB vs. Newts vs. RRDtool

System Properties Comparison Atos Standard Common Repository vs. BigchainDB vs. Newts vs. RRDtool

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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

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Time Series DBMS based on Cassandra

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

Document store
Key-value store

Document store

Time Series DBMS

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

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	2.14
Rank	#124	Overall
	#10	Time Series DBMS

Website

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

www.bigchaindb.com

opennms.github.io/newts

oss.oetiker.ch/rrdtool

Technical documentation

bigchaindb.readthedocs.io/en/latest

github.com/OpenNMS/newts/wiki

oss.oetiker.ch/rrdtool/doc

Developer

Atos Convergence Creators

OpenNMS Group

Tobias Oetiker

Initial release

2016

2014

1999

Current release

1703

1.8.0, 2022

License

Commercial or Open Source

commercial

Open Source

AGPL v3

Open Source

Apache 2.0

Open Source

GPL V2 and FLOSS

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

Python

Java

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

Linux

Linux
OS X
Windows

HP-UX
Linux

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

Typing

predefined data types such as float or date

optional

yes

Numeric data only

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

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

APIs and other access methods

LDAP

CLI Client
RESTful HTTP API

HTTP REST
Java API

in-process shared library
Pipes

Supported programming languages

All languages with LDAP bindings

Go
Haskell
Java
JavaScript
Python
Ruby

Java

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

none

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

none

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

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

yes

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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