DBMS > Atos Standard Common Repository vs. Blueflood vs. PostgreSQL vs. SAP SQL Anywhere vs. Yaacomo

System Properties Comparison Atos Standard Common Repository vs. Blueflood vs. PostgreSQL vs. SAP SQL Anywhere vs. Yaacomo

Editorial information provided by DB-Engines

Name

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

Yaacomo seems to be discontinued and is 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

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store
Key-value store

Time Series DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Relational DBMS

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	0.06
Rank	#352	Overall
	#36	Time Series DBMS

Score	644.36
Rank	#4	Overall
	#4	Relational DBMS

Score	3.50
Rank	#81	Overall
	#44	Relational DBMS

Website

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

blueflood.io

www.postgresql.org

www.sap.com/products/technology-platform/sql-anywhere.html

yaacomo.com

Technical documentation

github.com/rax-maas/blueflood/wiki

www.postgresql.org/docs

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Atos Convergence Creators

Rackspace

PostgreSQL Global Development Group

www.postgresql.org/developer

SAP

formerly Sybase

Q2WEB GmbH

Initial release

2016

2013

1989

1989: Postgres, 1996: PostgreSQL

1992

2009

Current release

1703

16.4, August 2024

17, July 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

Java

Server operating systems

Linux

Linux
OS X

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Android
Linux
Windows

Data scheme

Schema and schema-less with LDAP views

predefined scheme

yes

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

specific XML-type available, but no XML query functionality.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

standard with numerous extensions

yes

APIs and other access methods

LDAP

HTTP REST

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

ADO.NET
HTTP API
JDBC
ODBC

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

based on Cassandra

partitioning by range, list and (since PostgreSQL 11) by hash

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Source-replica replication

other methods possible by using 3rd party extensions

Source-replica replication

Database mirroring

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

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

More information provided by the system vendor

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