DBMS > Blazegraph vs. IRONdb vs. Microsoft Azure Cosmos DB vs. Pinecone

System Properties Comparison Blazegraph vs. IRONdb vs. Microsoft Azure Cosmos DB vs. Pinecone

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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.

IRONdb seems to be discontinued. Therefore it is excluded 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.

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Globally distributed, horizontally scalable, multi-model database service

A managed, cloud-native vector database

Primary database model

Graph DBMS
RDF store

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Vector DBMS

Secondary database models

Spatial DBMS

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

Score	0.81
Rank	#213	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.23
Rank	#92	Overall
	#3	Vector DBMS

Website

blazegraph.com

www.circonus.com/solutions/time-series-database/

azure.microsoft.com/services/cosmos-db

www.pinecone.io

Technical documentation

wiki.blazegraph.com

docs.circonus.com/irondb/category/getting-started

learn.microsoft.com/azure/cosmos-db

docs.pinecone.io/docs/overview

Developer

Blazegraph

Circonus LLC.

Microsoft

Pinecone Systems, Inc

Initial release

2006

2017

2014

2019

Current release

2.1.5, March 2019

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

extended commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Server operating systems

Linux
OS X
Windows

Linux

hosted

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

RDF literal types

yes

text, numeric, histograms

yes

JSON types

String, Number, Boolean

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.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SPARQL is used as query language

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

HTTP API

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

RESTful HTTP API

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

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

Python

Server-side scripts

Stored procedures

yes

yes, in Lua

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

configurable replication factor, datacenter aware

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate consistency per node, eventual consistency across nodes

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session 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

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)

Access rights can be defined down to the item level

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