DBMS > Atos Standard Common Repository vs. DataFS vs. etcd vs. QuestDB vs. RDF4J

System Properties Comparison Atos Standard Common Repository vs. DataFS vs. etcd vs. QuestDB 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

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 distributed reliable key-value store

A high performance open source SQL database for time series data

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

Secondary database models

Graph DBMS

Relational DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

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

newdatabase.com

etcd.io
github.com/etcd-io/etcd

questdb.io

rdf4j.org

Technical documentation

dev.mobiland.com/Overview.xsp

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

questdb.io/docs

rdf4j.org/documentation

Developer

Atos Convergence Creators

Mobiland AG

QuestDB Technology Inc

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

Initial release

2016

2018

2014

2004

Current release

1703

1.1.263, October 2022

3.4, August 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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

Java (Zero-GC), C++, Rust

Java

Server operating systems

Linux

Windows

FreeBSD
Linux
Windows

experimental

Linux
macOS
Windows

Linux
OS X
Unix
Windows

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

schema-free via InfluxDB Line Protocol

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

Secondary indexes

yes

SQL

Support of SQL

SQL with time-series extensions

APIs and other access methods

LDAP

.NET Client API
Proprietary client DLL
WinRT client

gRPC
JSON over HTTP

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

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

.Net
C
C#
C++
VB.Net

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

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

no, except callback-events from server when changes happened

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Proprietary Sharding system

horizontal partitioning (by timestamps)

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Source-replica replication with eventual consistency

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

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

ACID for single-table writes

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

through memory mapped files

User concepts

Access control

LDAP bind authentication

Windows-Profile

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

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

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