DBMS > Bangdb vs. Hawkular Metrics vs. Hyprcubd vs. MonetDB vs. SwayDB

System Properties Comparison Bangdb vs. Hawkular Metrics vs. Hyprcubd vs. MonetDB vs. SwayDB

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

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

Serverless Time Series DBMS

A relational database management system that stores data in columns

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Document store
Graph DBMS
Time Series DBMS

Time Series DBMS

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

Document store
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	1.72
Rank	#148	Overall
	#68	Relational DBMS

Score	0.04
Rank	#373	Overall
	#57	Key-value stores

Website

bangdb.com

www.hawkular.org

hyprcubd.com (offline)

www.monetdb.org

swaydb.simer.au

Technical documentation

docs.bangdb.com

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

www.monetdb.org/Documentation

Developer

Sachin Sinha, BangDB

Community supported by Red Hat

Hyprcubd, Inc.

MonetDB BV

Simer Plaha

Initial release

2012

2014

2004

2018

Current release

BangDB 2.0, October 2021

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0

commercial

Open Source

Mozilla Public License 2.0

Open Source

GNU Affero GPL V3.0

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++

Java

Scala

Server operating systems

Linux

Linux
OS X
Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

yes

time, int, uint, float, string

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language

yes

SQL 2003 with some extensions

APIs and other access methods

Proprietary protocol
RESTful HTTP API

HTTP REST

gRPC (https)

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

C
C#
C++
Java
Python

Go
Java
Python
Ruby

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

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes, in SQL, C, R

Triggers

yes, Notifications (with Streaming only)

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 via remote tables

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

selectable replication factor

based on Cassandra

none

Source-replica replication available in experimental status

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of operations

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)

token access

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Waiting for Red Hat OpenShift 4.0? Too late, 4.1 has already arrived… • DEVCLASS
5 June 2019, DevClass

provided by Google News

In 2024 the MonetDB Foundation was established for the preservation, maintenance and further development of the ...
31 January 2024, Centrum Wiskunde & Informatica (CWI)

MonetDB Secures Investment From (and Partners With) ServiceNow
9 December 2021, Datanami

How MonetDB Exploits Modern CPU Performance | by Dwi Prasetyo Adi Nugroho
14 January 2020, Towards Data Science

PostgreSQL, MonetDB, and Too-Big-for-Memory Data in R - Part I - DataScienceCentral.com
6 April 2018, Data Science Central

MonetDB Solutions secures investment from ServiceNow
30 September 2019, Centrum Wiskunde & Informatica (CWI)

provided by Google News

Share this page