DBMS > Drizzle vs. Hawkular Metrics vs. HEAVY.AI vs. SAP Adaptive Server

System Properties Comparison Drizzle vs. Hawkular Metrics vs. HEAVY.AI vs. SAP Adaptive Server

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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.

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

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

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	37.12
Rank	#25	Overall
	#17	Relational DBMS

Website

www.hawkular.org

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

www.sap.com/products/technology-platform/sybase-ase.html

Technical documentation

www.hawkular.org/hawkular-metrics/docs/user-guide

docs.heavy.ai

help.sap.com/docs/SAP_ASE

Developer

Drizzle project, originally started by Brian Aker

Community supported by Red Hat

HEAVY.AI, Inc.

SAP, Sybase

Initial release

2008

2014

2016

1987

Current release

7.2.4, September 2012

5.10, January 2022

16.0

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

commercial

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

C++

Java

C++ and CUDA

C and C++

Server operating systems

FreeBSD
Linux
OS X

Linux
OS X
Windows

Linux

AIX
HP-UX
Linux
Solaris
Unix
Windows

Data scheme

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

JDBC

HTTP REST

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

C
C++
Java
PHP

Go
Java
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Cobol
Java
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

Java and Transact-SQL

Triggers

hooks for callbacks inside the server can be used.

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Round robin

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

Multi-source replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

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