DBMS > Amazon Redshift vs. DataFS vs. Graph Engine vs. Hypertable vs. Kdb

System Properties Comparison Amazon Redshift vs. DataFS vs. Graph Engine vs. Hypertable vs. Kdb

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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

High performance Time Series DBMS

Primary database model

Relational DBMS

Object oriented DBMS

Graph DBMS
Key-value store

Wide column store

Time Series DBMS
Vector DBMS

Secondary database models

Graph DBMS

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Website

aws.amazon.com/redshift

newdatabase.com

www.graphengine.io

kx.com

Technical documentation

docs.aws.amazon.com/redshift

dev.mobiland.com/Overview.xsp

www.graphengine.io/docs/manual

code.kx.com

Developer

Amazon (based on PostgreSQL)

Mobiland AG

Microsoft

Hypertable Inc.

Kx Systems, a division of First Derivatives plc

Initial release

2012

2018

2010

2009

2000

kdb was released 2000, kdb+ in 2003

Current release

1.1.263, October 2022

0.9.8.11, March 2016

3.6, May 2018

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

GNU version 3. Commercial license available

commercial

free 32-bit version

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

.NET and C

C++

Server operating systems

hosted

Windows

.NET

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X
Solaris
Windows

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

schema-free

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

restricted

only exact value or prefix value scans

yes

table attribute 'grouped'

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language (q)

APIs and other access methods

JDBC
ODBC

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP API

C++ API
Thrift

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
VB.Net

C#
C++
F#
Visual Basic

C++
Java
Perl
PHP
Python
Ruby

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions

Triggers

no, except callback-events from server when changes happened

yes

with views

Partitioning methods

Methods for storing different data on different nodes

Sharding

Proprietary Sharding system

horizontal partitioning

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor on file system level

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

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

Windows-Profile

rights management via user accounts

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
» more

Licensing and pricing models

upon request
» more

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.