DBMS > OrigoDB vs. Spark SQL vs. Transbase vs. Yanza

System Properties Comparison OrigoDB vs. Spark SQL vs. Transbase vs. Yanza

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Editorial information provided by DB-Engines

Name

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

Description

A fully ACID in-memory object graph database

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

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

Time Series DBMS for IoT Applications

Primary database model

Document store
Object oriented DBMS

Relational DBMS

Time Series DBMS

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

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

origodb.com

spark.apache.org/sql

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

yanza.com

Technical documentation

origodb.com/docs

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

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

Developer

Robert Friberg et al

Apache Software Foundation

Transaction Software GmbH

Yanza

Initial release

2009

under the name LiveDB

2014

1987

2015

Current release

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free development license

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

Scala

C and C++

Server operating systems

Linux
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

.NET Client API
HTTP API
LINQ

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

Supported programming languages

.Net

Java
Python
R
Scala

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

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

Triggers

yes

Domain Events

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

client side managed; servers are not synchronized

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

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

depending on model

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

Write ahead log

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

Role based authorization

fine grained access rights according to SQL-standard

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