DBMS > 4D vs. Bangdb vs. Hawkular Metrics vs. InfluxDB vs. OrigoDB

System Properties Comparison 4D vs. Bangdb vs. Hawkular Metrics vs. InfluxDB vs. OrigoDB

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

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.

DBMS for storing time series, events and metrics

A fully ACID in-memory object graph database

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Time Series DBMS

Document store
Object oriented DBMS

Secondary database models

Spatial DBMS

with GEO package

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Website

www.4d.com

bangdb.com

www.hawkular.org

www.influxdata.com/products/influxdb-overview

origodb.com

Technical documentation

developer.4d.com

docs.bangdb.com

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

docs.influxdata.com/influxdb

origodb.com/docs

Developer

4D, Inc

Sachin Sinha, BangDB

Community supported by Red Hat

Robert Friberg et al

Initial release

1984

2012

2014

2013

2009

under the name LiveDB

Current release

v20, April 2023

BangDB 2.0, October 2021

2.7.6, April 2024

License

Commercial or Open Source

commercial

Open Source

BSD 3

Open Source

Apache 2.0

Open Source

MIT-License; commercial enterprise version available

Open Source

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++

Java

Server operating systems

OS X
Windows

Linux

Linux
OS X
Windows

Linux
OS X

through Homebrew

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

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

yes

Numeric data and Strings

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

yes

close to SQL 92

SQL like support with command line tool

SQL-like query language

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

Proprietary protocol
RESTful HTTP API

HTTP REST

HTTP API
JSON over UDP

.NET Client API
HTTP API
LINQ

Supported programming languages

4D proprietary IDE
PHP

C
C#
C++
Java
Python

Go
Java
Python
Ruby

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

.Net

Server-side scripts

Stored procedures

yes

Triggers

yes

yes, Notifications (with Streaming only)

yes

via Hawkular Alerting

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

based on Cassandra

Sharding

in enterprise version only

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

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

selectable replication factor

based on Cassandra

selectable replication factor

in enterprise version only

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

depending on model

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

Write ahead log

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

Depending on used storage engine

yes

User concepts

Access control

Users and groups

yes (enterprise version only)

simple rights management via user accounts

Role based authorization

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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