DBMS > OushuDB vs. PostGIS vs. Spark SQL vs. Transbase

System Properties Comparison OushuDB vs. PostGIS vs. Spark SQL vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Spatial extension of PostgreSQL

Spark SQL is a component on top of 'Spark Core' for structured data processing

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Relational DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

www.oushu.com/product/oushuDB

postgis.net

spark.apache.org/sql

www.transaction.de/en/products/transbase.html

Technical documentation

www.oushu.com/documentation

postgis.net/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

www.transaction.de/en/products/transbase/features.html

Developer

Oushu

Apache Software Foundation

Transaction Software GmbH

Initial release

2005

2014

1987

Current release

4.0.1, August 2020

3.4.2, February 2024

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

GPL v2.0

Open Source

Apache 2.0

commercial

free development 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

Scala

C and C++

Server operating systems

Linux

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

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

Secondary indexes

yes

SQL

Support of SQL

Full-featured ANSI SQL support

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C++

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

based on PostgreSQL

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on PostgreSQL

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

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

yes

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.

User concepts

Access control

Kerberos, SSL and role based access

yes

based on PostgreSQL

fine grained access rights according to SQL-standard

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More resources

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