DBMS > EsgynDB vs. HarperDB vs. NuoDB vs. OpenQM vs. Riak KV

System Properties Comparison EsgynDB vs. HarperDB vs. NuoDB vs. OpenQM vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

NuoDB is a webscale distributed database that supports SQL and ACID transactions

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

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

www.esgyn.cn

www.harperdb.io

www.3ds.com/nuodb-distributed-sql-database

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

Technical documentation

docs.harperdb.io/docs

doc.nuodb.com

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Esgyn

HarperDB

Dassault Systèmes

originally NuoDB, Inc.

Rocket Software, originally Martin Phillips

OpenSource, formerly Basho Technologies

Initial release

2015

2017

2013

1993

2009

Current release

3.1, August 2021

3.4-12

3.2.0, December 2022

License

Commercial or Open Source

commercial

free community edition available

commercial

limited edition free

Open Source

GPLv2, extended commercial license available

Open Source

Apache version 2, commercial enterprise edition

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

Node.js

C++

Erlang

Server operating systems

Linux

Linux
OS X

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
OS X

Data scheme

yes

dynamic schema

yes

with some exceptions

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

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

restricted

SQL

Support of SQL

yes

SQL-like data manipulation statements

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

ADO.NET
JDBC
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

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

.Net
Basic
C
Java
Objective C
PHP
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Java Stored Procedures

Custom Functions

since release 3.1

Java, SQL

yes

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

data is dynamically stored/cached on the nodes where it is read/written

yes

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

the nodes on which a table resides can be defined

yes

Managed transparently by NuoDB

yes

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Atomic execution of specific operations

ACID

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

In-memory capabilities

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

yes

Temporary table

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

Standard SQL roles/ privileges, Administrative Users

Access rights can be defined down to the item level

yes, using Riak Security

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More resources

DB-Engines blog posts

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