DBMS > FatDB vs. GeoSpock vs. Oracle Berkeley DB vs. PouchDB vs. STSdb

System Properties Comparison FatDB vs. GeoSpock vs. Oracle Berkeley DB vs. PouchDB vs. STSdb

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.

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

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

Spatial and temporal data processing engine for extreme data scale

Widely used in-process key-value store

JavaScript DBMS with an API inspired by CouchDB

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store

Key-value store

Secondary database models

Time Series DBMS

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

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

geospock.com

www.oracle.com/database/technologies/related/berkeleydb.html

pouchdb.com

github.com/STSSoft/STSdb4

Technical documentation

docs.oracle.com/cd/E17076_05/html/index.html

pouchdb.com/guides

Developer

FatCloud

GeoSpock

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Apache Software Foundation

STS Soft SC

Initial release

2012

1994

2012

2011

Current release

2.0, September 2019

18.1.40, May 2020

7.1.1, June 2019

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

commercial license available

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java, Javascript

C, Java, C++ (depending on the Berkeley DB edition)

JavaScript

Server operating systems

Windows

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

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

Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

temporal, categorical

yes

via views

SQL

Support of SQL

Via inetgration in SQL Server

ANSI SQL for query only (using Presto)

yes

SQL interfaced based on SQLite is available

APIs and other access methods

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

JDBC

HTTP REST

only for PouchDB Server
JavaScript API

.NET Client API

Supported programming languages

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

JavaScript

C#
Java

Server-side scripts

Stored procedures

yes

via applications

View functions in JavaScript

Triggers

yes

via applications

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

none

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

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

by using IndexedDB, WebSQL or LevelDB as backend

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

Can implement custom security layer via applications

Access rights for users can be defined per table

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