DBMS > AlaSQL vs. HEAVY.AI vs. OpenQM vs. SiteWhere

System Properties Comparison AlaSQL vs. HEAVY.AI vs. OpenQM vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

QpenQM is a high-performance, self-tuning, multi-value DBMS

M2M integration platform for persisting/querying time series data

Primary database model

Document store
Relational DBMS

Relational DBMS

Multivalue DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.49
Rank	#259	Overall
	#40	Document stores
	#120	Relational DBMS

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	0.33
Rank	#286	Overall
	#10	Multivalue DBMS

Score	0.06
Rank	#367	Overall
	#36	Time Series DBMS

Website

alasql.org

github.com/heavyai/heavydb
www.heavy.ai

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

github.com/sitewhere/sitewhere

Technical documentation

github.com/AlaSQL/alasql

docs.heavy.ai

sitewhere1.sitewhere.io/index.html

Developer

Andrey Gershun & Mathias R. Wulff

HEAVY.AI, Inc.

Rocket Software, originally Martin Phillips

SiteWhere

Initial release

2014

2016

1993

2010

Current release

5.10, January 2022

3.4-12

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Apache Version 2; enterprise edition available

Open Source

GPLv2, extended commercial license available

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

JavaScript

C++ and CUDA

Java

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

Linux

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

with some exceptions

predefined scheme

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

yes

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes

APIs and other access methods

JavaScript API

JDBC
ODBC
Thrift
Vega

HTTP REST

Supported programming languages

JavaScript

All languages supporting JDBC/ODBC/Thrift
Python

.Net
Basic
C
Java
Objective C
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

yes

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

only for local storage and DOM-storage

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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

Access rights can be defined down to the item level

Users with fine-grained authorization concept

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