DBMS > Blazegraph vs. EJDB vs. Hypertable vs. LeanXcale

System Properties Comparison Blazegraph vs. EJDB vs. Hypertable vs. LeanXcale

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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.

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

Description

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

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

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Primary database model

Graph DBMS
RDF store

Document store

Wide column store

Key-value store
Relational DBMS

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

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Website

blazegraph.com

github.com/Softmotions/ejdb

www.leanxcale.com

Technical documentation

wiki.blazegraph.com

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

Developer

Blazegraph

Softmotions

Hypertable Inc.

LeanXcale

Initial release

2006

2012

2009

2015

Current release

2.1.5, March 2019

0.9.8.11, March 2016

License

Commercial or Open Source

Open Source

extended commercial license available

Open Source

GPLv2

Open Source

GNU version 3. Commercial license available

commercial

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

server-less

Linux
OS X
Windows

an inofficial Windows port is available

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

yes

string, integer, double, bool, date, object_id

Secondary indexes

yes

restricted

only exact value or prefix value scans

SQL

Support of SQL

SPARQL is used as query language

yes

through Apache Derby

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

in-process shared library

C++ API
Thrift

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

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

C++
Java
Perl
PHP
Python
Ruby

C
Java
Scala

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

selectable replication factor on file system level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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)

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