DBMS > EsgynDB vs. EventStoreDB vs. Spark SQL

System Properties Comparison EsgynDB vs. EventStoreDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

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

Primary database model

Relational DBMS

Event Store

Relational DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.esgyn.cn

www.eventstore.com

spark.apache.org/sql

Technical documentation

developers.eventstore.com

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

Developer

Esgyn

Event Store Limited

Apache Software Foundation

Initial release

2015

2012

2014

Current release

21.2, February 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

Scala

Server operating systems

Linux

Linux
Windows

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

fine grained access rights according to SQL-standard

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