DBMS > Blazegraph vs. searchxml vs. SiteWhere vs. Spark SQL

System Properties Comparison Blazegraph vs. searchxml vs. SiteWhere vs. Spark SQL

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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.

DBMS for structured and unstructured content wrapped with an application server

M2M integration platform for persisting/querying time series data

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

Primary database model

Graph DBMS
RDF store

Native XML DBMS
Search engine

Time Series DBMS

Relational DBMS

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

Score	0.91
Rank	#185	Overall
	#17	Graph DBMS
	#8	RDF stores

Score	0.00
Rank	#354	Overall
	#7	Native XML DBMS
	#21	Search engines

Score	0.00
Rank	#354	Overall
	#33	Time Series DBMS

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Website

blazegraph.com

www.searchxml.net/category/products

sitewhere.io

spark.apache.org/sql

Technical documentation

wiki.blazegraph.com

www.searchxml.net/support/handouts

sitewhere.io/docs/2.1.0

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

Developer

Blazegraph

informationpartners gmbh

SiteWhere

Apache Software Foundation

Initial release

2006

2015

2010

2014

Current release

2.1.5, March 2019

1.0

3.2.0, October 2021

License

Commercial or Open Source

Open Source

extended commercial license available

commercial

Open Source

Common Public Attribution License Version 1.0

Open Source

Apache 2.0

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++

Java

Scala

Server operating systems

Linux
OS X
Windows

Windows

Linux
OS X
Windows

Data scheme

schema-free

predefined scheme

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SPARQL is used as query language

SQL-like DML and DDL statements

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

RESTful HTTP API
WebDAV
XQuery
XSLT

HTTP REST

JDBC
ODBC

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

C++

most other programming languages supported via APIs

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

on the application server

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on HBase

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

sychronisation to multiple collections

selectable replication factor

based on HBase

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

Transaction concepts

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

ACID

multiple readers, single writer

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.

User concepts

Access control

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

Domain, group and role-based access control at the document level and for application services

Users with fine-grained authorization concept

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