DBMS > DataFS vs. HEAVY.AI vs. SiriDB vs. StarRocks

System Properties Comparison DataFS vs. HEAVY.AI vs. SiriDB vs. StarRocks

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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.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Open Source Time Series DBMS

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Secondary database models

Graph DBMS

Spatial DBMS

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

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

newdatabase.com

github.com/heavyai/heavydb
www.heavy.ai

siridb.com

www.starrocks.io

Technical documentation

dev.mobiland.com/Overview.xsp

docs.heavy.ai

docs.siridb.com

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Mobiland AG

HEAVY.AI, Inc.

Cesbit

The Linux Foundation

since Feb 2023

Initial release

2018

2016

2017

2020

Current release

1.1.263, October 2022

5.10, January 2022

3.3, June 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

MIT License

Open Source

Apache Version 2.0

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

C++ and CUDA

C++, Java

Server operating systems

Windows

Linux

Data scheme

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

yes

Typing

predefined data types such as float or date

yes

Numeric data

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

SQL

Support of SQL

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC
Thrift
Vega

HTTP API

JDBC
MySQL protocol

Supported programming languages

.Net
C
C#
C++
VB.Net

All languages supporting JDBC/ODBC/Thrift
Python

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

Java

Server-side scripts

Stored procedures

user defined functions

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

Round robin

Sharding

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

In-memory capabilities

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

yes

User concepts

Access control

Windows-Profile

fine grained access rights according to SQL-standard

simple rights management via user accounts

Role based access control and fine grained access rights

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More resources

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