DBMS > Alibaba Cloud AnalyticDB for PostgreSQL vs. Graphite vs. RDF4J vs. Sadas Engine

System Properties Comparison Alibaba Cloud AnalyticDB for PostgreSQL vs. Graphite vs. RDF4J vs. Sadas Engine

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Editorial information provided by DB-Engines

Name

Description

An online MPP (Massively Parallel Processing) data warehousing service based on Greenplum

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

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

Score	0.62
Rank	#241	Overall
	#110	Relational DBMS

Score	4.75
Rank	#75	Overall
	#5	Time Series DBMS

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	0.03
Rank	#379	Overall
	#156	Relational DBMS

Website

www.alibabacloud.com/product/hybriddb-postgresql

github.com/graphite-project/graphite-web

rdf4j.org

www.sadasengine.com

Technical documentation

www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview

graphite.readthedocs.io

rdf4j.org/documentation

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Alibaba / Pivotal Software Inc. / PostgreSQL Global Development Group

Chris Davis

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

SADAS s.r.l.

Initial release

2016

2006

2004

2006

Current release

8.0

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

free trial version available

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

Python

Java

C++

Server operating systems

hosted

Linux
Unix

Linux
OS X
Unix
Windows

AIX
Linux
Windows

Data scheme

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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

JDBC
ODBC

HTTP API
Sockets

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC
Proprietary protocol

Supported programming languages

C
Java
Perl
Python
R

JavaScript (Node.js)
Python

Java
PHP
Python

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

locking

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

In-memory capabilities

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

yes

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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