DBMS > EventStoreDB vs. Fujitsu Enterprise Postgres vs. Heroic vs. OushuDB

System Properties Comparison EventStoreDB vs. Fujitsu Enterprise Postgres vs. Heroic vs. OushuDB

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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.

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	1.07
Rank	#181	Overall
	#1	Event Stores

Score	0.22
Rank	#304	Overall
	#137	Relational DBMS

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	0.05
Rank	#357	Overall
	#151	Relational DBMS

Website

www.eventstore.com

www.postgresql.fastware.com

github.com/spotify/heroic

www.oushu.com/product/oushuDB

Technical documentation

developers.eventstore.com

www.postgresql.fastware.com/product-manuals

spotify.github.io/heroic

www.oushu.com/documentation

Developer

Event Store Limited

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Spotify

Oushu

Initial release

2012

2014

Current release

21.2, February 2021

Fujitsu Enterprise Postgres 14, January 2022

4.0.1, August 2020

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

Linux
Windows

Linux

Data scheme

yes

schema-free

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

via Elasticsearch

yes

SQL

Support of SQL

yes

Full-featured ANSI SQL support

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C++

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

partitioning by range, list and by hash

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
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, multi-version concurrency control (MVCC)

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

Kerberos, SSL and role based access

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