DBMS > Bangdb vs. Drizzle vs. Hawkular Metrics vs. HBase

System Properties Comparison Bangdb vs. Drizzle vs. Hawkular Metrics vs. HBase

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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

Converged and high performance database for device data, events, time series, document and graph

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.

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

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

Wide column store

Secondary database models

Spatial DBMS

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

Score	0.15
Rank	#334	Overall
	#45	Document stores
	#31	Graph DBMS
	#31	Time Series DBMS

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Website

bangdb.com

www.hawkular.org

hbase.apache.org

Technical documentation

docs.bangdb.com

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

hbase.apache.org/book.html

Developer

Sachin Sinha, BangDB

Drizzle project, originally started by Brian Aker

Community supported by Red Hat

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

2012

2008

2014

2008

Current release

BangDB 2.0, October 2021

7.2.4, September 2012

2.3.4, January 2021

License

Commercial or Open Source

Open Source

BSD 3

Open Source

GNU GPL

Open Source

Apache 2.0

Open Source

Apache version 2

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, C++

C++

Java

Server operating systems

Linux

FreeBSD
Linux
OS X

Linux
OS X
Windows

Linux
Unix
Windows

using Cygwin

Data scheme

schema-free

yes

schema-free

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

with proprietary extensions

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC

HTTP REST

Java API
RESTful HTTP API
Thrift

Supported programming languages

C
C#
C++
Java
Python

C
C++
Java
PHP

Go
Java
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Triggers

yes, Notifications (with Streaming only)

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 (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

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

Tunable consistency, set CAP knob accordingly

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

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, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

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More resources

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