DBMS > Bangdb vs. Hyprcubd vs. IRONdb vs. VoltDB

System Properties Comparison Bangdb vs. Hyprcubd vs. IRONdb vs. VoltDB

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Editorial information provided by DB-Engines

Name

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

IRONdb 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

Serverless Time Series DBMS

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Document store
Graph DBMS
Time Series DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.07
Rank	#346	Overall
	#47	Document stores
	#35	Graph DBMS
	#32	Time Series DBMS

Score	1.38
Rank	#156	Overall
	#72	Relational DBMS

Website

bangdb.com

hyprcubd.com (offline)

www.circonus.com/solutions/time-series-database/

www.voltdb.com

Technical documentation

docs.bangdb.com

docs.circonus.com/irondb/category/getting-started

docs.voltdb.com

Developer

Sachin Sinha, BangDB

Hyprcubd, Inc.

Circonus LLC.

VoltDB Inc.

Initial release

2012

2017

2010

Current release

BangDB 2.0, October 2021

V0.10.20, January 2018

11.3, April 2022

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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++

C and C++

Java, C++

Server operating systems

Linux

hosted

Linux

Linux
OS X

for development

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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

yes

time, int, uint, float, string

yes

text, numeric, histograms

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

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

only a subset of SQL 99

APIs and other access methods

Proprietary protocol
RESTful HTTP API

gRPC (https)

HTTP API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes, in Lua

Java

Triggers

yes, Notifications (with Streaming only)

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

Automatic, metric affinity per node

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

configurable replication factor, datacenter aware

Multi-source replication
Source-replica replication

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

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

Snapshots and command logging

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

User concepts

Access control

yes (enterprise version only)

token access

Users and roles with access to stored procedures

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More resources

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