DBMS > Apache Pinot vs. Blazegraph vs. FeatureBase vs. SWC-DB

System Properties Comparison Apache Pinot vs. Blazegraph vs. FeatureBase vs. SWC-DB

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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

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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.

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Time Series DBMS

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

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	0.75
Rank	#219	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	0.22
Rank	#309	Overall
	#139	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

pinot.apache.org

blazegraph.com

www.featurebase.com

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.pinot.apache.org

wiki.blazegraph.com

docs.featurebase.com

Developer

Apache Software Foundation and contributors

Blazegraph

Molecula and Pilosa Open Source Contributors

Alex Kashirin

Initial release

2015

2006

2017

2020

Current release

1.0.0, September 2023

2.1.5, March 2019

2022, May 2022

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

extended commercial license available

commercial

Open Source

GPL V3

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++

Server operating systems

All OS with a Java JDK11 or higher

Linux
OS X
Windows

Linux
macOS

Linux

Data scheme

yes

schema-free

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SPARQL is used as query language

SQL queries

SQL-like query language

APIs and other access methods

JDBC

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

gRPC
JDBC
Kafka Connector
ODBC

Proprietary protocol
Thrift

Supported programming languages

Go
Java
Python

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

Java
Python

C++

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

Transaction concepts

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

ACID

yes

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)

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More resources

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