DBMS > DataFS vs. Heroic vs. Oracle Coherence vs. Quasardb

System Properties Comparison DataFS vs. Heroic vs. Oracle Coherence vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Oracles in-memory data grid solution

Distributed, high-performance timeseries database

Primary database model

Secondary database models

Graph DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

newdatabase.com

github.com/spotify/heroic

www.oracle.com/java/coherence

quasar.ai

Technical documentation

dev.mobiland.com/Overview.xsp

spotify.github.io/heroic

docs.oracle.com/en/middleware/standalone/coherence

doc.quasar.ai/master

Developer

Mobiland AG

Spotify

Oracle

quasardb

Initial release

2018

2014

2007

2009

Current release

1.1.263, October 2022

14.1, August 2023

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

C++

Server operating systems

Windows

All OS with a Java VM

BSD
Linux
OS X
Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

SQL-like query language

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

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

JCache
JPA
RESTful HTTP API

HTTP API

Supported programming languages

.Net
C
C#
C++
VB.Net

.Net
C++
Java

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

Triggers

no, except callback-events from server when changes happened

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, with selectable consistency level

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

yes

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

Windows-Profile

authentification to access the cache via certificates or http basic authentication

Cryptographically strong user authentication and audit trail

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