DBMS > EventStoreDB vs. GBase vs. Spark SQL vs. Transwarp KunDB

System Properties Comparison EventStoreDB vs. GBase vs. Spark SQL vs. Transwarp KunDB

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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.

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

OLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle

Primary database model

Event Store

Relational 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	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.10
Rank	#344	Overall
	#151	Relational DBMS

Website

www.eventstore.com

www.gbase.cn

spark.apache.org/sql

www.transwarp.cn/en/product/kundb

Technical documentation

developers.eventstore.com

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

Developer

Event Store Limited

General Data Technology Co., Ltd.

Apache Software Foundation

Transwarp

Initial release

2012

2004

2014

Current release

21.2, February 2021

GBase 8a, GBase 8s, GBase 8c

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, Java, Python

Scala

Server operating systems

Linux
Windows

Linux

Linux
OS X
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

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

yes, utilizing Spark Core

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

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

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