DBMS > Badger vs. BigchainDB vs. Blazegraph vs. HEAVY.AI

System Properties Comparison Badger vs. BigchainDB vs. Blazegraph vs. HEAVY.AI

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Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

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

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.75
Rank	#219	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Website

github.com/dgraph-io/badger

www.bigchaindb.com

blazegraph.com

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

Technical documentation

godoc.org/github.com/dgraph-io/badger

bigchaindb.readthedocs.io/en/latest

wiki.blazegraph.com

docs.heavy.ai

Developer

DGraph Labs

Blazegraph

HEAVY.AI, Inc.

Initial release

2017

2016

2006

2016

Current release

2.1.5, March 2019

5.10, January 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

AGPL v3

Open Source

extended commercial license available

Open Source

Apache Version 2; enterprise edition available

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

Java

C++ and CUDA

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Windows

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

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

SPARQL is used as query language

yes

APIs and other access methods

CLI Client
RESTful HTTP API

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

JDBC
ODBC
Thrift
Vega

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

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

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

Security and Authentication via Web Application Container (Tomcat, Jetty)

fine grained access rights according to SQL-standard

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