DBMS > atoti vs. Fujitsu Enterprise Postgres vs. Postgres-XL vs. PostgreSQL vs. Yanza

System Properties Comparison atoti vs. Fujitsu Enterprise Postgres vs. Postgres-XL vs. PostgreSQL vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

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

Widely used open source RDBMS

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

Time Series DBMS for IoT Applications

Primary database model

Object oriented DBMS

Relational DBMS

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

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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	0.45
Rank	#253	Overall
	#13	Object oriented DBMS

Score	0.22
Rank	#304	Overall
	#137	Relational DBMS

Score	0.43
Rank	#260	Overall
	#119	Relational DBMS

Score	644.36
Rank	#4	Overall
	#4	Relational DBMS

Website

atoti.io

www.postgresql.fastware.com

www.postgres-xl.org

www.postgresql.org

yanza.com

Technical documentation

docs.atoti.io

www.postgresql.fastware.com/product-manuals

www.postgres-xl.org/documentation

www.postgresql.org/docs

Developer

ActiveViam

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

PostgreSQL Global Development Group

www.postgresql.org/developer

Yanza

Initial release

2014

since 2012, originally named StormDB

1989

1989: Postgres, 1996: PostgreSQL

2015

Current release

Fujitsu Enterprise Postgres 14, January 2022

10 R1, October 2018

16.4, August 2024

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

Mozilla public license

Open Source

BSD

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

Java

Server operating systems

Linux
Windows

Linux
macOS

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

Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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

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

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

distributed, parallel query execution

yes

standard with numerous extensions

APIs and other access methods

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

HTTP API

Supported programming languages

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

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

any language that supports HTTP calls

Server-side scripts

Stored procedures

Python

user defined functions

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

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

partitioning by range, list and by hash

horizontal partitioning

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

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