DBMS > Bangdb vs. DataFS vs. H2 vs. HarperDB

System Properties Comparison Bangdb vs. DataFS vs. H2 vs. HarperDB

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Editorial information provided by DB-Engines

Name

Description

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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Primary database model

Document store
Graph DBMS
Time Series DBMS

Object oriented DBMS

Relational DBMS

Document store

Secondary database models

Spatial DBMS

Graph DBMS

Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Website

Technical documentation

docs.bangdb.com

dev.mobiland.com/Overview.xsp

www.h2database.com/html/main.html

docs.harperdb.io/docs

Developer

Sachin Sinha, BangDB

Mobiland AG

Thomas Mueller

HarperDB

Initial release

2012

2018

2005

2017

Current release

BangDB 2.0, October 2021

1.1.263, October 2022

2.2.220, July 2023

3.1, August 2021

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

free community edition available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Java

Node.js

Server operating systems

Linux

Windows

All OS with a Java VM

Linux
OS X

Data scheme

schema-free

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

dynamic schema

Typing

predefined data types such as float or date

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

yes

JSON data types

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

yes

SQL-like data manipulation statements

APIs and other access methods

Proprietary protocol
RESTful HTTP API

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C#
C++
VB.Net

Java

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

Custom Functions

since release 3.1

Triggers

yes, Notifications (with Streaming only)

no, except callback-events from server when changes happened

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

Proprietary Sharding system

none

A table resides as a whole on one (or more) nodes in a cluster

Replication methods

Methods for redundantly storing data on multiple nodes

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

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

the nodes on which a table resides can be defined

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

yes, using LMDB

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)

Windows-Profile

fine grained access rights according to SQL-standard

Access rights for users and roles

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