DBMS > FatDB vs. InfinityDB vs. IRONdb vs. Lovefield

System Properties Comparison FatDB vs. InfinityDB vs. IRONdb vs. Lovefield

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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.

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A Java embedded Key-Value Store which extends the Java Map interface

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

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Document store
Key-value store

Key-value store

Time Series DBMS

Relational DBMS

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

Score	0.08
Rank	#365	Overall
	#55	Key-value stores

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Website

boilerbay.com

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

google.github.io/lovefield

Technical documentation

boilerbay.com/infinitydb/manual

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

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

Developer

FatCloud

Boiler Bay Inc.

Circonus LLC.

Google

Initial release

2012

2002

2017

2014

Current release

4.0

V0.10.20, January 2018

2.1.12, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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 C++

JavaScript

Server operating systems

Windows

All OS with a Java VM

Linux

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

tested with Chrome, Firefox, IE, Safari

Data scheme

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

text, numeric, histograms

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

Via inetgration in SQL Server

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

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

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

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

HTTP API

Supported programming languages

Java

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

JavaScript

Server-side scripts

Stored procedures

yes

via applications

yes, in Lua

Triggers

yes

via applications

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

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

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More resources

Recent citations in the news

Application observability firm Apica buys telemetry data startup Circonus and adds more funding
21 February 2024, SiliconANGLE News

Apica Acquires Telemetry Data Management Pioneer Circonus And Lands New Funding
22 February 2024, Datanami

Apica gets $6 million in funding and buys Circonus -
21 February 2024, Enterprise Times

provided by Google News

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