DBMS > EsgynDB vs. OpenQM vs. Trafodion vs. WakandaDB

System Properties Comparison EsgynDB vs. OpenQM vs. Trafodion vs. WakandaDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

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

QpenQM is a high-performance, self-tuning, multi-value DBMS

Transactional SQL-on-Hadoop DBMS

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

www.esgyn.cn

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

trafodion.apache.org

wakanda.github.io

Technical documentation

trafodion.apache.org/documentation.html

wakanda.github.io/doc

Developer

Esgyn

Rocket Software, originally Martin Phillips

Apache Software Foundation, originally developed by HP

Wakanda SAS

Initial release

2015

1993

2014

2012

Current release

3.4-12

2.3.0, February 2019

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

GPLv2, extended commercial license available

Open Source

Apache 2.0

Open Source

AGPLv3, extended 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++, Java

C++, JavaScript

Server operating systems

Linux

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

with some exceptions

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
Basic
C
Java
Objective C
PHP
Python

All languages supporting JDBC/ODBC/ADO.Net

JavaScript

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

yes, via HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Evaluating HTAP Databases for Machine Learning Applications
2 November 2016, KDnuggets

Low-latency, distributed database architectures are critical for emerging fog applications
7 April 2022, Embedded Computing Design

provided by Google News

Share this page