DBMS > Drizzle vs. Hawkular Metrics vs. OpenQM vs. Oracle Coherence vs. TimescaleDB

System Properties Comparison Drizzle vs. Hawkular Metrics vs. OpenQM vs. Oracle Coherence vs. TimescaleDB

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.

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

Oracles in-memory data grid solution

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Relational DBMS

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

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

www.hawkular.org

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

www.oracle.com/java/coherence

www.timescale.com

Technical documentation

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

docs.oracle.com/en/middleware/standalone/coherence

docs.timescale.com

Developer

Drizzle project, originally started by Brian Aker

Community supported by Red Hat

Rocket Software, originally Martin Phillips

Oracle

Timescale

Initial release

2008

2014

1993

2007

2017

Current release

7.2.4, September 2012

3.4-12

14.1, August 2023

2.15.0, May 2024

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache 2.0

Open Source

GPLv2, extended commercial license available

commercial

Open Source

Apache 2.0

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

C++

Java

Server operating systems

FreeBSD
Linux
OS X

Linux
OS X
Windows

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

with some exceptions

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

full PostgreSQL SQL syntax

APIs and other access methods

JDBC

HTTP REST

JCache
JPA
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C
C++
Java
PHP

Go
Java
Python
Ruby

.Net
Basic
C
Java
Objective C
PHP
Python

.Net
C++
Java

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

hooks for callbacks inside the server can be used.

yes

via Hawkular Alerting

yes

Live Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

yes

Sharding

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

yes

yes, with selectable consistency level

Source-replica replication with hot standby and reads on replicas

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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

Access rights can be defined down to the item level

authentification to access the cache via certificates or http basic authentication

fine grained access rights according to SQL-standard

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