DBMS > Amazon Redshift vs. EsgynDB vs. Hypertable vs. NuoDB vs. Oracle NoSQL

System Properties Comparison Amazon Redshift vs. EsgynDB vs. Hypertable vs. NuoDB vs. Oracle NoSQL

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

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

An open source BigTable implementation based on distributed file systems such as Hadoop

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

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Document store
Key-value store
Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Website

aws.amazon.com/redshift

www.esgyn.cn

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

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

docs.aws.amazon.com/redshift

doc.nuodb.com

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Amazon (based on PostgreSQL)

Esgyn

Hypertable Inc.

Dassault Systèmes

originally NuoDB, Inc.

Oracle

Initial release

2012

2015

2009

2013

2011

Current release

0.9.8.11, March 2016

23.3, December 2023

License

Commercial or Open Source

commercial

Open Source

GNU version 3. Commercial license available

commercial

limited edition free

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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++, Java

C++

Java

Server operating systems

hosted

Linux

Linux
OS X
Windows

an inofficial Windows port is available

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux
Solaris SPARC/x86

Data scheme

yes

schema-free

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

optional

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

restricted

yes

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

C++ API
Thrift

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC/ADO.Net

C++
Java
Perl
PHP
Python
Ruby

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

C
C#
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication between multi datacenters

selectable replication factor on file system level

yes

Managed transparently by NuoDB

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

tunable commit protocol

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Temporary table

yes

off heap cache

User concepts

Access control

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

Access rights for users and roles

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