DBMS > Hyprcubd vs. OushuDB vs. SiteWhere

System Properties Comparison Hyprcubd vs. OushuDB vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Serverless Time Series DBMS

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

M2M integration platform for persisting/querying time series data

Primary database model

Time Series DBMS

Relational DBMS

Time Series DBMS

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

Score	0.10
Rank	#355	Overall
	#154	Relational DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Website

hyprcubd.com (offline)

www.oushu.com/product/oushuDB

github.com/sitewhere/sitewhere

Technical documentation

www.oushu.com/documentation

sitewhere1.sitewhere.io/index.html

Developer

Hyprcubd, Inc.

Oushu

SiteWhere

Initial release

2010

Current release

4.0.1, August 2020

License

Commercial or Open Source

commercial

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux

Linux
OS X
Windows

Data scheme

yes

predefined scheme

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

Full-featured ANSI SQL support

APIs and other access methods

gRPC (https)

JDBC
ODBC

HTTP REST

Supported programming languages

C
C++

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

In-memory capabilities

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

User concepts

Access control

token access

Kerberos, SSL and role based access

Users with fine-grained authorization concept

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More resources

Recent citations in the news

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8 September 2021, PingWest

provided by Google News

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