DBMS > Blazegraph vs. Microsoft SQL Server vs. Postgres-XL vs. Solr

System Properties Comparison Blazegraph vs. Microsoft SQL Server vs. Postgres-XL vs. Solr

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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.

Microsofts flagship relational DBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Graph DBMS
RDF store

Relational DBMS

Search engine

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

Spatial 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	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

blazegraph.com

www.microsoft.com/en-us/sql-server

www.postgres-xl.org

solr.apache.org

Technical documentation

wiki.blazegraph.com

learn.microsoft.com/en-US/sql/sql-server

www.postgres-xl.org/documentation

solr.apache.org/resources.html

Developer

Blazegraph

Microsoft

Apache Software Foundation

Initial release

2006

1989

2014

since 2012, originally named StormDB

2006

Current release

2.1.5, March 2019

SQL Server 2022, November 2022

10 R1, October 2018

9.6.0, April 2024

License

Commercial or Open Source

Open Source

extended commercial license available

commercial

restricted free version is available

Open Source

Mozilla public license

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Java

Server operating systems

Linux
OS X
Windows

Linux
Windows

Linux
macOS

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

RDF literal types

yes

supports customizable data types and automatic typing

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

XML type, but no XML query functionality

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

SPARQL is used as query language

yes

distributed, parallel query execution

Solr Parallel SQL Interface

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

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

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

user defined functions

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

Transaction concepts

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

ACID

MVCC

optimistic locking

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

yes

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