DBMS > DataFS vs. Quasardb vs. RisingWave vs. STSdb

System Properties Comparison DataFS vs. Quasardb vs. RisingWave vs. STSdb

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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.

Distributed, high-performance timeseries database

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Graph DBMS

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

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	0.64
Rank	#238	Overall
	#110	Relational DBMS

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

Website

newdatabase.com

quasar.ai

www.risingwave.com/database

github.com/STSSoft/STSdb4

Technical documentation

dev.mobiland.com/Overview.xsp

doc.quasar.ai/master

docs.risingwave.com/docs/current/intro

Developer

Mobiland AG

quasardb

RisingWave Labs

STS Soft SC

Initial release

2018

2009

2022

2011

Current release

1.1.263, October 2022

3.14.1, January 2024

1.2, September 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

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

Open Source

Apache Version 2.0

Open Source

GPLv2, commercial license available

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

C++

Rust

Server operating systems

Windows

BSD
Linux
OS X
Windows

Docker
Linux
macOS

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

yes

Typing

predefined data types such as float or date

yes

integer and binary

Standard SQL-types and JSON

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.

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

HTTP API

JDBC
PostgreSQL wire protocol

.NET Client API

Supported programming languages

.Net
C
C#
C++
VB.Net

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

Go
Java
JavaScript (Node.js)
Python

C#
Java

Server-side scripts

Stored procedures

UDFs in Python or Java

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with selectable replication factor

none

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

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

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

Windows-Profile

Cryptographically strong user authentication and audit trail

Users and Roles

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