DBMS > Drizzle vs. HEAVY.AI vs. OpenQM vs. SAP HANA

System Properties Comparison Drizzle vs. HEAVY.AI vs. OpenQM vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Relational DBMS

Multivalue DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

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

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

www.sap.com/products/hana.html

Technical documentation

docs.heavy.ai

help.sap.com/hana

Developer

Drizzle project, originally started by Brian Aker

HEAVY.AI, Inc.

Rocket Software, originally Martin Phillips

SAP

Initial release

2008

2016

1993

2010

Current release

7.2.4, September 2012

5.10, January 2022

3.4-12

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache Version 2; enterprise edition available

Open Source

GPLv2, extended commercial license available

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

C++ and CUDA

Server operating systems

FreeBSD
Linux
OS X

Linux

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Appliance or cloud-service

Data scheme

yes

with some exceptions

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

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

JDBC

JDBC
ODBC
Thrift
Vega

JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

All languages supporting JDBC/ODBC/Thrift
Python

.Net
Basic
C
Java
Objective C
PHP
Python

Server-side scripts

Stored procedures

yes

SQLScript, R

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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

In-memory capabilities

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

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

yes

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