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

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

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

Serverless Time Series DBMS

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

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

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

Website

bangdb.com

www.hawkular.org

hbase.apache.org

hyprcubd.com (offline)

swaydb.simer.au

Technical documentation

docs.bangdb.com

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

hbase.apache.org/book.html

Developer

Sachin Sinha, BangDB

Community supported by Red Hat

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Hyprcubd, Inc.

Simer Plaha

Initial release

2012

2014

2008

2018

Current release

BangDB 2.0, October 2021

2.3.4, January 2021

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0

Open Source

Apache version 2

commercial

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

Linux
Unix
Windows

using Cygwin

hosted

Data scheme

schema-free

schema-free, schema definition possible

yes

schema-free

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

yes

time, int, uint, float, string

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

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

HTTP REST

Java API
RESTful HTTP API
Thrift

gRPC (https)

Supported programming languages

C
C#
C++
Java
Python

Go
Java
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

Coprocessors in Java

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

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

Multi-source replication
Source-replica replication

none

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

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)

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

token access

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