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

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

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Object oriented DBMS

Graph DBMS
Key-value store

Wide column store

Search engine

Secondary database models

Graph DBMS

Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

newdatabase.com

www.graphengine.io

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

dev.mobiland.com/Overview.xsp

www.graphengine.io/docs/manual

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Mobiland AG

Microsoft

Hypertable Inc.

Apache Software Foundation

Initial release

2012

2018

2010

2009

2006

Current release

1.1.263, October 2022

0.9.8.11, March 2016

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

GNU version 3. Commercial license available

Open Source

Apache Version 2

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++

Java

Server operating systems

hosted

Windows

.NET

Linux
OS X
Windows

an inofficial Windows port is available

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

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

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP API

C++ API
Thrift

Java API
RESTful HTTP/JSON API

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Java plugins

Triggers

no, except callback-events from server when changes happened

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Proprietary Sharding system

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor on file system level

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

optimistic locking

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

yes

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