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DBMS > FatDB vs. InfinityDB vs. Postgres-XL vs. PouchDB vs. SQL.JS

System Properties Comparison FatDB vs. InfinityDB vs. Postgres-XL vs. PouchDB vs. SQL.JS

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

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

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

JavaScript DBMS with an API inspired by CouchDB

Port of SQLite to JavaScript

Primary database model

Document store
Key-value store

Key-value store

Relational DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.53
Rank	#252	Overall
	#116	Relational DBMS

Website

www.postgres-xl.org

Technical documentation

boilerbay.com/infinitydb/manual

www.postgres-xl.org/documentation

pouchdb.com/guides

sql.js.org/documentation/index.html

Developer

FatCloud

Boiler Bay Inc.

Apache Software Foundation

Alon Zakai

enhancements implemented by others

Initial release

2012

2002

2014

since 2012, originally named StormDB

2012

2012

Current release

4.0

10 R1, October 2018

7.1.1, June 2019

License

Commercial or Open Source

commercial

commercial

Open Source

Mozilla public license

Open Source

Open Source

Cloud-based only

Only available as a cloud service

no

no

no

no

no

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C#

Java

C

JavaScript

JavaScript

Server operating systems

Windows

All OS with a Java VM

Linux
macOS

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

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

Data scheme

schema-free

yes

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

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes

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

yes

no

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.

no

yes

XML type, but no XML query functionality

no

no

Secondary indexes

yes

no

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

yes

yes

via views

SQL

Support of SQL

no

Via inetgration in SQL Server

no

yes

distributed, parallel query execution

no

yes

SQL-92 is not fully supported

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)

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP REST

only for PouchDB Server
JavaScript API

JavaScript API

Supported programming languages

C#

Java

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

JavaScript

JavaScript

Server-side scripts

Stored procedures

yes

via applications

no

user defined functions

View functions in JavaScript

no

Triggers

yes

via applications

no

yes

yes

no

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

no

no

yes

no

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Eventual Consistency

none

Foreign keys

Referential integrity

no

no

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

yes

no

yes

Transaction concepts

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

no

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

MVCC

no

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes

yes

Durability

Support for making data persistent

yes

yes

yes

yes

by using IndexedDB, WebSQL or LevelDB as backend

no

except by serializing a db to a file

In-memory capabilities

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

no

no

yes

yes

User concepts

Access control

no

Can implement custom security layer via applications

no

fine grained access rights according to SQL-standard

no

no

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More resources

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