DBMS > Blueflood vs. HEAVY.AI vs. Lovefield vs. RDFox

System Properties Comparison Blueflood vs. HEAVY.AI vs. Lovefield vs. RDFox

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Embeddable relational database for web apps written in pure JavaScript

High performance knowledge graph and semantic reasoning engine

Primary database model

Time Series DBMS

Relational DBMS

Graph DBMS
RDF store

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.09
Rank	#353	Overall
	#33	Time Series DBMS

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	0.32
Rank	#290	Overall
	#132	Relational DBMS

Score	0.24
Rank	#309	Overall
	#25	Graph DBMS
	#14	RDF stores

Website

blueflood.io

github.com/heavyai/heavydb
www.heavy.ai

google.github.io/lovefield

www.oxfordsemantic.tech

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.heavy.ai

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.oxfordsemantic.tech

Developer

Rackspace

HEAVY.AI, Inc.

Google

Oxford Semantic Technologies

Initial release

2013

2016

2014

2017

Current release

5.10, January 2022

2.1.12, February 2017

6.0, Septermber 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++ and CUDA

JavaScript

C++

Server operating systems

Linux
OS X

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
macOS
Windows

Data scheme

predefined scheme

yes

RDF schemas

Typing

predefined data types such as float or date

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

yes

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

HTTP REST

JDBC
ODBC
Thrift
Vega

RESTful HTTP API
SPARQL 1.1

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

C
Java

Server-side scripts

Stored procedures

Triggers

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication

none

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Roles, resources, and access types

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