DBMS > BigObject vs. Blueflood vs. Drizzle vs. Hawkular Metrics vs. SAP SQL Anywhere

System Properties Comparison BigObject vs. Blueflood vs. Drizzle vs. Hawkular Metrics vs. SAP SQL Anywhere

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

Analytic DBMS for real-time computations and queries

Scalable TimeSeries DBMS based on Cassandra

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.

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

bigobject.io

blueflood.io

www.hawkular.org

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

docs.bigobject.io

github.com/rax-maas/blueflood/wiki

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

help.sap.com/docs/SAP_SQL_Anywhere

Developer

BigObject, Inc.

Rackspace

Drizzle project, originally started by Brian Aker

Community supported by Red Hat

SAP

formerly Sybase

Initial release

2015

2013

2008

2014

1992

Current release

7.2.4, September 2012

17, July 2015

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

Open Source

GNU GPL

Open Source

Apache 2.0

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

Java

C++

Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X

FreeBSD
Linux
OS X

Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

predefined 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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HTTP REST

JDBC

HTTP REST

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

Go
Java
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

Lua

yes, in C/C++, Java, .Net or Perl

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

none

Sharding

based on Cassandra

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

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