DBMS > Blazegraph vs. OrigoDB vs. Spark SQL vs. SpatiaLite

System Properties Comparison Blazegraph vs. OrigoDB vs. Spark SQL vs. SpatiaLite

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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

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

A fully ACID in-memory object graph database

Spark SQL is a component on top of 'Spark Core' for structured data processing

Spatial extension of SQLite

Primary database model

Graph DBMS
RDF store

Document store
Object oriented DBMS

Relational DBMS

Spatial DBMS

Secondary database models

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	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Website

blazegraph.com

origodb.com

spark.apache.org/sql

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

wiki.blazegraph.com

origodb.com/docs

spark.apache.org/docs/latest/sql-programming-guide.html

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Blazegraph

Robert Friberg et al

Apache Software Foundation

Alessandro Furieri

Initial release

2006

2009

under the name LiveDB

2014

2008

Current release

2.1.5, March 2019

3.5.0 ( 2.13), September 2023

5.0.0, August 2020

License

Commercial or Open Source

Open Source

extended commercial license available

Open Source

Apache 2.0

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

C++

Server operating systems

Linux
OS X
Windows

Linux
Windows

Linux
OS X
Windows

server-less

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SPARQL is used as query language

SQL-like DML and DDL statements

yes

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

.NET Client API
HTTP API
LINQ

JDBC
ODBC

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

.Net

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

client side managed; servers are not synchronized

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Relationships in Graphs

depending on model

yes

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

Write ahead log

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)

Role based authorization

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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