DBMS > Blazegraph vs. Microsoft Azure Cosmos DB vs. NCache vs. Solr

System Properties Comparison Blazegraph vs. Microsoft Azure Cosmos DB vs. NCache 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.

Globally distributed, horizontally scalable, multi-model database service

Open-Source and Enterprise in-memory Key-Value Store

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

Primary database model

Graph DBMS
RDF store

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Search engine

Secondary database models

Spatial DBMS

Document store
Search engine

Using distributed Lucene

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	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.94
Rank	#195	Overall
	#29	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

blazegraph.com

azure.microsoft.com/services/cosmos-db

www.alachisoft.com/ncache

solr.apache.org

Technical documentation

wiki.blazegraph.com

learn.microsoft.com/azure/cosmos-db

www.alachisoft.com/resources/docs

solr.apache.org/resources.html

Developer

Blazegraph

Microsoft

Alachisoft

Apache Software Foundation

Initial release

2006

2014

2005

2006

Current release

2.1.5, March 2019

5.3.3, April 2024

9.6.0, April 2024

License

Commercial or Open Source

Open Source

extended commercial license available

commercial

Open Source

Enterprise Edition available

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C#, .NET, .NET Core, Java

Java

Server operating systems

Linux
OS X
Windows

hosted

Linux
Windows

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

JSON types

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

Secondary indexes

yes

All properties auto-indexed by default

yes

All search fields are automatically indexed

SQL

Support of SQL

SPARQL is used as query language

SQL-like query language

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

Solr Parallel SQL Interface

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

IDistributedCache
JCache
LINQ
Proprietary native API

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

.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

JavaScript

support for stored procedures with SQL-Server CLR

Java plugins

Triggers

JavaScript

yes

Notifications

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes, with selectable consistency level

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

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

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking and pessimistic locking

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)

Access rights can be defined down to the item level

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

yes

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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