DBMS > Oracle Berkeley DB vs. OushuDB vs. PouchDB vs. SwayDB vs. XTDB

System Properties Comparison Oracle Berkeley DB vs. OushuDB vs. PouchDB vs. SwayDB vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Widely used in-process key-value store

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

JavaScript DBMS with an API inspired by CouchDB

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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

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

Score	0.10
Rank	#355	Overall
	#154	Relational DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

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

www.oushu.com/product/oushuDB

pouchdb.com

swaydb.simer.au

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

www.oushu.com/documentation

pouchdb.com/guides

www.xtdb.com/docs

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Oushu

Apache Software Foundation

Simer Plaha

Juxt Ltd.

Initial release

1994

2012

2018

2019

Current release

18.1.40, May 2020

4.0.1, August 2020

7.1.1, June 2019

1.19, September 2021

License

Commercial or Open Source

Open Source

commercial license available

commercial

Open Source

GNU Affero GPL V3.0

Open Source

MIT License

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

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

JavaScript

Scala

Clojure

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

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

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

Full-featured ANSI SQL support

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC

HTTP REST

only for PouchDB Server
JavaScript API

HTTP REST
JDBC

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

C
C++

JavaScript

Java
Kotlin
Scala

Clojure
Java

Server-side scripts

Stored procedures

yes

View functions in JavaScript

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Atomic execution of operations

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

Kerberos, SSL and role based access

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