DBMS > Atos Standard Common Repository vs. Blueflood vs. mSQL vs. Postgres-XL vs. Teradata Aster

System Properties Comparison Atos Standard Common Repository vs. Blueflood vs. mSQL vs. Postgres-XL vs. Teradata Aster

Editorial information provided by DB-Engines

Name

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

Teradata Aster has been integrated into other Teradata systems and therefore 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

Scalable TimeSeries DBMS based on Cassandra

mSQL (Mini SQL) is a simple and lightweight RDBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Platform for big data analytics on multistructured data sources and types

Primary database model

Document store
Key-value store

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Website

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

blueflood.io

hughestech.com.au/products/msql

www.postgres-xl.org

Technical documentation

github.com/rax-maas/blueflood/wiki

www.postgres-xl.org/documentation

Developer

Atos Convergence Creators

Rackspace

Hughes Technologies

Teradata

Initial release

2016

2013

1994

2014

since 2012, originally named StormDB

2005

Current release

1703

4.4, October 2021

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

free licenses can be provided

Open Source

Mozilla public license

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

Server operating systems

Linux

Linux
OS X

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

Linux
macOS

Linux

Data scheme

Schema and schema-less with LDAP views

predefined scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

XML type, but no XML query functionality

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

distributed, parallel query execution

yes

APIs and other access methods

LDAP

HTTP REST

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

All languages with LDAP bindings

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

user defined functions

R packages

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

based on Cassandra

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

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

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

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