DBMS > Ingres vs. Oracle Berkeley DB vs. Postgres-XL vs. RDF4J vs. Yanza

System Properties Comparison Ingres vs. Oracle Berkeley DB vs. Postgres-XL vs. RDF4J vs. Yanza

Editorial information provided by DB-Engines

Name

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

Description

Well established RDBMS

Widely used in-process key-value store

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

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Time Series DBMS for IoT Applications

Primary database model

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

RDF store

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

www.actian.com/databases/ingres

www.oracle.com/database/technologies/related/berkeleydb.html

www.postgres-xl.org

rdf4j.org

yanza.com

Technical documentation

docs.actian.com/ingres

docs.oracle.com/cd/E17076_05/html/index.html

www.postgres-xl.org/documentation

rdf4j.org/documentation

Developer

Actian Corporation

Oracle

originally developed by Sleepycat, which was acquired by Oracle

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

Yanza

Initial release

1974

originally developed at University Berkely in early 1970s

1994

2014

since 2012, originally named StormDB

2004

2015

Current release

11.2, May 2022

18.1.40, May 2020

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

commercial license available

Open Source

Mozilla public license

Open Source

Eclipse Distribution License (EDL), v1.0.

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.

Implementation language

C, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS

Linux
OS X
Unix
Windows

Windows

Data scheme

yes

schema-free

yes

RDF Schemas

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.

but tools for importing/exporting data from/to XML-files available

yes

only with the Berkeley DB XML edition

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

distributed, parallel query execution

APIs and other access methods

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

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

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

HTTP API

Supported programming languages

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

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

Java
PHP
Python

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

user defined functions

yes

Triggers

yes

only for the SQL API

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Ingres Star to access multiple databases simultaneously

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Ingres Replicator

Source-replica replication

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

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

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