DBMS > Atos Standard Common Repository vs. EventStoreDB vs. Heroic vs. STSdb vs. SwayDB

System Properties Comparison Atos Standard Common Repository vs. EventStoreDB vs. Heroic vs. STSdb vs. SwayDB

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Document store
Key-value store

Event Store

Time Series DBMS

Key-value store

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

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.eventstore.com

github.com/spotify/heroic

github.com/STSSoft/STSdb4

swaydb.simer.au

Technical documentation

developers.eventstore.com

spotify.github.io/heroic

Developer

Atos Convergence Creators

Event Store Limited

Spotify

STS Soft SC

Simer Plaha

Initial release

2016

2012

2014

2011

2018

Current release

1703

21.2, February 2021

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Open Source

GNU Affero GPL V3.0

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

Scala

Server operating systems

Linux

Linux
Windows

Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

schema-free

Typing

predefined data types such as float or date

optional

yes

primitive types and user defined types (classes)

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

via Elasticsearch

SQL

Support of SQL

APIs and other access methods

LDAP

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

.NET Client API

Supported programming languages

All languages with LDAP bindings

C#
Java

Java
Kotlin
Scala

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

none

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 or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

Atomic execution of operations

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.

yes

User concepts

Access control

LDAP bind authentication

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