DBMS > Blazegraph vs. FeatureBase vs. Google Cloud Datastore vs. Riak KV

System Properties Comparison Blazegraph vs. FeatureBase vs. Google Cloud Datastore vs. Riak KV

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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.

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

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	0.31
Rank	#292	Overall
	#135	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

blazegraph.com

www.featurebase.com

cloud.google.com/datastore

Technical documentation

wiki.blazegraph.com

docs.featurebase.com

cloud.google.com/datastore/docs

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Blazegraph

Molecula and Pilosa Open Source Contributors

Google

OpenSource, formerly Basho Technologies

Initial release

2006

2017

2008

2009

Current release

2.1.5, March 2019

2022, May 2022

3.2.0, December 2022

License

Commercial or Open Source

Open Source

extended commercial license available

commercial

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

Server operating systems

Linux
OS X
Windows

Linux
macOS

hosted

Linux
OS X

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

RDF literal types

yes

yes, details here

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

restricted

SQL

Support of SQL

SPARQL is used as query language

SQL queries

SQL-like query language (GQL)

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

gRPC
JDBC
Kafka Connector
ODBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP API
Native Erlang Interface

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

Java
Python

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

using Google App Engine

Erlang

Triggers

Callbacks using the Google Apps Engine

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

yes

via ReferenceProperties or Ancestor paths

links between data sets can be stored

Transaction concepts

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

ACID

yes

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using Linux fsync

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 for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes, using Riak Security

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