DBMS > Adabas vs. Fujitsu Enterprise Postgres vs. InfinityDB vs. Newts vs. Postgres-XL

System Properties Comparison Adabas vs. Fujitsu Enterprise Postgres vs. InfinityDB vs. Newts vs. Postgres-XL

Editorial information provided by DB-Engines

Name

Description

OLTP - DBMS for mainframes and Linux/Unix/Windows environments

used typically together with the Natural programming platform

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

A Java embedded Key-Value Store which extends the Java Map interface

Time Series DBMS based on Cassandra

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

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	3.17
Rank	#94	Overall
	#1	Multivalue DBMS

Score	0.31
Rank	#285	Overall
	#129	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

www.softwareag.com/en_corporate/platform/adabas-natural.html

www.postgresql.fastware.com

boilerbay.com

opennms.github.io/newts

www.postgres-xl.org

Technical documentation

www.postgresql.fastware.com/product-manuals

boilerbay.com/infinitydb/manual

github.com/OpenNMS/newts/wiki

www.postgres-xl.org/documentation

Developer

Software AG

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Boiler Bay Inc.

OpenNMS Group

Initial release

1971

2002

2014

since 2012, originally named StormDB

Current release

Fujitsu Enterprise Postgres 14, January 2022

4.0

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Mozilla public license

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

BS2000
Linux
Unix
Windows
z/OS
z/VSE

Linux
Windows

All OS with a Java VM

Linux
OS X
Windows

Linux
macOS

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

Secondary indexes

yes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

distributed, parallel query execution

APIs and other access methods

HTTP API

with add-on software Adabas SOA Gateway
SOAP-based API

with add-on software Adabas SOA Gateway

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

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

HTTP REST
Java API

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

Supported programming languages

Natural

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

Java

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

Server-side scripts

Stored procedures

in Natural

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas Vista

partitioning by range, list and by hash

none

Sharding

based on Cassandra

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

Source-replica replication

none

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

READ-COMMITTED or SERIALIZED

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

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.

User concepts

Access control

only with OS-specific tools (e.g. IBM RACF, CA Top Secret)

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

100% compatible with community PostgreSQL
» more

Competitive advantages

Built-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more

Typical application scenarios

Transactional payments applications, reporting and mixed workloads.
» more

Market metrics

Over 30 years experience in database technology. Over 20 years in Postgres development...
» more

Licensing and pricing models

Core based licensing
» more

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