DBMS > Blueflood vs. FatDB vs. HEAVY.AI vs. Lovefield vs. Riak TS

System Properties Comparison Blueflood vs. FatDB vs. HEAVY.AI vs. Lovefield vs. Riak TS

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Scalable TimeSeries DBMS based on Cassandra

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Time Series DBMS

Document store
Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

blueflood.io

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

google.github.io/lovefield

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.heavy.ai

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

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

Developer

Rackspace

FatCloud

HEAVY.AI, Inc.

Google

Open Source, formerly Basho Technologies

Initial release

2013

2012

2016

2014

2015

Current release

5.10, January 2022

2.1.12, February 2017

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Open Source

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

Erlang

Server operating systems

Linux
OS X

Windows

Linux

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

tested with Chrome, Firefox, IE, Safari

Linux
OS X

Data scheme

predefined scheme

schema-free

yes

schema-free

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

restricted

SQL

Support of SQL

Via inetgration in SQL Server

yes

SQL-like query language

via JavaScript builder pattern

yes, limited

APIs and other access methods

HTTP REST

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

JDBC
ODBC
Thrift
Vega

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

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

via applications

Erlang

Triggers

yes

via applications

Using read-only observers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Round robin

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

selectable replication factor

Multi-source replication

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

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

User concepts

Access control

Can implement custom security layer via applications

fine grained access rights according to SQL-standard

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