DBMS > Blazegraph vs. Citus vs. HugeGraph vs. RethinkDB vs. RocksDB

System Properties Comparison Blazegraph vs. Citus vs. HugeGraph vs. RethinkDB vs. RocksDB

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

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

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

A fast-speed and highly-scalable Graph DBMS

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Secondary database models

Document store

Spatial DBMS

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

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

Score	2.15
Rank	#117	Overall
	#56	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

blazegraph.com

www.citusdata.com

github.com/hugegraph
hugegraph.apache.org

rethinkdb.com

rocksdb.org

Technical documentation

wiki.blazegraph.com

docs.citusdata.com

hugegraph.apache.org/docs

rethinkdb.com/docs

github.com/facebook/rocksdb/wiki

Developer

Blazegraph

Baidu

The Linux Foundation

since July 2017

Facebook, Inc.

Initial release

2006

2010

2018

2009

2013

Current release

2.1.5, March 2019

8.1, December 2018

0.9

2.4.1, August 2020

9.2.1, May 2024

License

Commercial or Open Source

Open Source

extended commercial license available

Open Source

AGPL, commercial license also available

Open Source

Apache Version 2.0

Open Source

Apache Version 2

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Windows

Linux

Linux
macOS
Unix

Linux
OS X
Windows

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

RDF literal types

yes

string, binary, float, bool, date, geometry

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.

yes

specific XML type available, but no XML query functionality

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

SPARQL is used as query language

yes

standard, with numerous extensions

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
RESTful HTTP API
TinkerPop Gremlin

C++ API
Java API

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Groovy
Java
Python

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

asynchronous Gremlin script jobs

Triggers

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

Sharding

range based

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

other methods possible by using 3rd party extensions

yes

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

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Immediate 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

Atomic single-document operations

yes

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

yes

users and table-level permissions

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