DBMS > BigchainDB vs. DataFS vs. Hypertable vs. LMDB

System Properties Comparison BigchainDB vs. DataFS vs. Hypertable vs. LMDB

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

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.

An open source BigTable implementation based on distributed file systems such as Hadoop

A high performant, light-weight, embedded key-value database library

Primary database model

Secondary database models

Graph DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	1.99
Rank	#125	Overall
	#21	Key-value stores

Website

www.bigchaindb.com

newdatabase.com

www.symas.com/symas-embedded-database-lmdb

Technical documentation

bigchaindb.readthedocs.io/en/latest

dev.mobiland.com/Overview.xsp

www.lmdb.tech/doc

Developer

Mobiland AG

Hypertable Inc.

Symas

Initial release

2016

2018

2009

2011

Current release

1.1.263, October 2022

0.9.8.11, March 2016

0.9.32, January 2024

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Open Source

GNU version 3. Commercial license 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

Python

C++

Server operating systems

Linux

Windows

Linux
OS X
Windows

an inofficial Windows port is available

Linux
Unix
Windows

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)

schema-free

Typing

predefined data types such as float or date

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

restricted

only exact value or prefix value scans

SQL

Support of SQL

APIs and other access methods

CLI Client
RESTful HTTP API

.NET Client API
Proprietary client DLL
WinRT client

C++ API
Thrift

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
C
C#
C++
VB.Net

C++
Java
Perl
PHP
Python
Ruby

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

Server-side scripts

Stored procedures

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Sharding

Proprietary Sharding system

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

selectable replication factor on file system level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

yes

Windows-Profile

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