DBMS > EventStoreDB vs. GBase vs. MonetDB vs. Spark SQL vs. Transbase

System Properties Comparison EventStoreDB vs. GBase vs. MonetDB vs. Spark SQL vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

A relational database management system that stores data in columns

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

Event Store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

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

Technical documentation

developers.eventstore.com

www.monetdb.org/Documentation

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

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

Developer

Event Store Limited

General Data Technology Co., Ltd.

MonetDB BV

Apache Software Foundation

Transaction Software GmbH

Initial release

2012

2004

2014

1987

Current release

21.2, February 2021

GBase 8a, GBase 8s, GBase 8c

Dec2023 (11.49), December 2023

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

commercial

Open Source

Mozilla Public License 2.0

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

C, Java, Python

Scala

C and C++

Server operating systems

Linux
Windows

Linux

FreeBSD
Linux
OS X
Solaris
Windows

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

Standard with numerous extensions

yes

SQL 2003 with some extensions

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Java
Python
R
Scala

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

Server-side scripts

Stored procedures

user defined functions

yes, in SQL, C, R

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding via remote tables

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication available in experimental status

none

Source-replica replication

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

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

yes

fine grained access rights according to SQL-standard

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