DBMS > Axibase vs. FatDB vs. Oracle Berkeley DB vs. PouchDB

System Properties Comparison Axibase vs. FatDB vs. Oracle Berkeley DB vs. PouchDB

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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.

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

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

Widely used in-process key-value store

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Time Series DBMS

Document store
Key-value store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store

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

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Website

axibase.com/docs/atsd/finance

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

pouchdb.com

Technical documentation

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

pouchdb.com/guides

Developer

Axibase Corporation

FatCloud

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Apache Software Foundation

Initial release

2013

2012

1994

2012

Current release

15585

18.1.40, May 2020

7.1.1, June 2019

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

Open Source

commercial license available

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

JavaScript

Server operating systems

Linux

Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

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

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

via views

SQL

Support of SQL

SQL-like query language

Via inetgration in SQL Server

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

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

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

Go
Java
PHP
Python
R
Ruby

.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

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

none

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

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

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

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

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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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