DBMS > EsgynDB vs. HEAVY.AI vs. openGemini vs. OrigoDB

System Properties Comparison EsgynDB vs. HEAVY.AI vs. openGemini vs. OrigoDB

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

A fully ACID in-memory object graph database

Primary database model

Relational DBMS

Time Series DBMS

Document store
Object oriented DBMS

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.01
Rank	#375	Overall
	#39	Time Series DBMS

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

Website

www.esgyn.cn

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

www.opengemini.org
github.com/openGemini

origodb.com

Technical documentation

docs.heavy.ai

docs.opengemini.org/guide

origodb.com/docs

Developer

Esgyn

HEAVY.AI, Inc.

Huawei and openGemini community

Robert Friberg et al

Initial release

2015

2016

2022

2009

under the name LiveDB

Current release

5.10, January 2022

1.1, July 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2.0

Open Source

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++, Java

C++ and CUDA

Server operating systems

Linux

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Integer, Float, Boolean, String

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

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC
Thrift
Vega

HTTP REST

.NET Client API
HTTP API
LINQ

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

.Net

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Multi-source replication

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

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

Administrators and common users accounts

Role based authorization

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