DBMS > HEAVY.AI vs. Hyprcubd vs. mSQL vs. OpenMLDB vs. OrigoDB

System Properties Comparison HEAVY.AI vs. Hyprcubd vs. mSQL vs. OpenMLDB vs. OrigoDB

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

Serverless Time Series DBMS

mSQL (Mini SQL) is a simple and lightweight RDBMS

An open-source machine learning database that provides a feature platform for training and inference

A fully ACID in-memory object graph database

Primary database model

Document store
Object oriented DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.02
Rank	#367	Overall
	#37	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

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

hyprcubd.com (offline)

hughestech.com.au/products/msql

openmldb.ai

origodb.com

Technical documentation

docs.heavy.ai

openmldb.ai/docs/zh/main

origodb.com/docs

Developer

HEAVY.AI, Inc.

Hyprcubd, Inc.

Hughes Technologies

4 Paradigm Inc.

Robert Friberg et al

Initial release

2016

1994

2020

2009

under the name LiveDB

Current release

5.10, January 2022

4.4, October 2021

2024-2 February 2024

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

free licenses can be provided

Open Source

Cloud-based only

Only available as a cloud service

yes

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, Scala

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux

Linux
Windows

Data scheme

yes

Fixed schema

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

yes

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

gRPC (https)

JDBC
ODBC

JDBC
SQLAlchemy

.NET Client API
HTTP API
LINQ

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

C++
Go
Java
Python
Scala

.Net

Server-side scripts

Stored procedures

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

depending on model

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

Write ahead log

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

token access

fine grained access rights according to SQL-standard

Role based authorization

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