DBMS > Badger vs. Blazegraph vs. Cubrid vs. HugeGraph

System Properties Comparison Badger vs. Blazegraph vs. Cubrid vs. HugeGraph

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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.

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

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

A fast-speed and highly-scalable Graph DBMS

Primary database model

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	0.81
Rank	#213	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Website

github.com/dgraph-io/badger

blazegraph.com

cubrid.com (korean)
cubrid.org (english)

github.com/hugegraph
hugegraph.apache.org

Technical documentation

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

wiki.blazegraph.com

cubrid.org/manuals

hugegraph.apache.org/docs

Developer

DGraph Labs

Blazegraph

CUBRID Corporation, CUBRID Foundation

Baidu

Initial release

2017

2006

2008

2018

Current release

2.1.5, March 2019

11.0, January 2021

0.9

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

extended commercial license available

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

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

Java

C, C++, Java

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux
Windows

Linux
macOS
Unix

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

also supports composite index and range index

SQL

Support of SQL

SPARQL is used as query language

yes

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

ADO.NET
JDBC
ODBC
OLE DB

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Groovy
Java
Python

Server-side scripts

Stored procedures

yes

Java Stored Procedures

asynchronous Gremlin script jobs

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes

depending on used storage backend, e.g. Cassandra and HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Source-replica replication

yes

depending on used storage backend, e.g. Cassandra and HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

edges in graph

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

In-memory capabilities

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

yes

User concepts

Access control

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

fine grained access rights according to SQL-standard

Users, roles and permissions

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More resources

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