DBMS > EventStoreDB vs. Spark SQL vs. ToroDB vs. Yanza

System Properties Comparison EventStoreDB vs. Spark SQL vs. ToroDB vs. Yanza

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Editorial information provided by DB-Engines

Name

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

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

Description

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

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Time Series DBMS for IoT Applications

Primary database model

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

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.eventstore.com

spark.apache.org/sql

github.com/torodb/server

yanza.com

Technical documentation

developers.eventstore.com

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

Developer

Event Store Limited

Apache Software Foundation

8Kdata

Yanza

Initial release

2012

2014

2016

2015

Current release

21.2, February 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

AGPL-V3

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

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Implementation language

Scala

Java

Server operating systems

Linux
Windows

Linux
OS X
Windows

All OS with a Java 7 VM

Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

HTTP API

Supported programming languages

Java
Python
R
Scala

any language that supports HTTP calls

Server-side scripts

Stored procedures

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

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

Access rights for users and roles

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