DBMS > Amazon Redshift vs. Drizzle vs. Galaxybase vs. Hawkular Metrics vs. HBase

System Properties Comparison Amazon Redshift vs. Drizzle vs. Galaxybase vs. Hawkular Metrics vs. HBase

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

Large scale data warehouse service for use with business intelligence tools

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

Scalable, ACID-compliant native distributed parallel graph platform

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

Primary database model

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.00
Rank	#383	Overall
	#39	Graph DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Website

aws.amazon.com/redshift

galaxybase.com

www.hawkular.org

hbase.apache.org

Technical documentation

docs.aws.amazon.com/redshift

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

hbase.apache.org/book.html

Developer

Amazon (based on PostgreSQL)

Drizzle project, originally started by Brian Aker

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

Community supported by Red Hat

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

2012

2008

2017

2014

2008

Current release

7.2.4, September 2012

Nov 20, November 2021

2.3.4, January 2021

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

Open Source

Apache 2.0

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

C and Java

Java

Server operating systems

hosted

FreeBSD
Linux
OS X

Linux

Linux
OS X
Windows

Linux
Unix
Windows

using Cygwin

Data scheme

yes

Strong typed schema

schema-free

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

JDBC

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

HTTP REST

Java API
RESTful HTTP API
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

Go
Java
Python

Go
Java
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined procedures and functions

yes

Coprocessors in Java

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Relationships in graphs

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

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

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Role-based access control

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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