DBMS > BigchainDB vs. EJDB vs. HEAVY.AI vs. RDF4J

System Properties Comparison BigchainDB vs. EJDB vs. HEAVY.AI vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Document store

Relational DBMS

RDF store

Secondary database models

Spatial DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.bigchaindb.com

github.com/Softmotions/ejdb

github.com/heavyai/heavydb
www.heavy.ai

rdf4j.org

Technical documentation

bigchaindb.readthedocs.io/en/latest

github.com/Softmotions/ejdb/blob/master/README.md

docs.heavy.ai

rdf4j.org/documentation

Developer

Softmotions

HEAVY.AI, Inc.

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2016

2012

2016

2004

Current release

5.10, January 2022

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

GPLv2

Open Source

Apache Version 2; enterprise edition available

Open Source

Eclipse Distribution License (EDL), v1.0.

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++ and CUDA

Java

Server operating systems

Linux

server-less

Linux

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

SQL

Support of SQL

yes

APIs and other access methods

CLI Client
RESTful HTTP API

in-process shared library

JDBC
ODBC
Thrift
Vega

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

in-memory storage is supported as well

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

fine grained access rights according to SQL-standard

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More resources

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