DBMS > Oracle Berkeley DB vs. PouchDB vs. Splice Machine vs. SQLite vs. Valentina Server

System Properties Comparison Oracle Berkeley DB vs. PouchDB vs. Splice Machine vs. SQLite vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

Widely used in-process key-value store

JavaScript DBMS with an API inspired by CouchDB

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Widely used embeddable, in-process RDBMS

Object-relational database and reports server

Primary database model

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store

Relational 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	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

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

Technical documentation

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

pouchdb.com/guides

splicemachine.com/how-it-works

www.sqlite.org/docs.html

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Apache Software Foundation

Splice Machine

Dwayne Richard Hipp

Paradigma Software

Initial release

1994

2012

2014

2000

1999

Current release

18.1.40, May 2020

7.1.1, June 2019

3.1, March 2021

3.45.3 (15 April 2024), April 2024

5.7.5

License

Commercial or Open Source

Open Source

commercial license available

Open Source

AGPL 3.0, commercial license available

Open Source

Public Domain

commercial

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

Java

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

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

Linux
OS X
Solaris
Windows

server-less

Linux
OS X
Windows

Data scheme

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

SQL-92 is not fully supported

yes

APIs and other access methods

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
Native Spark Datasource
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ODBC

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#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

View functions in JavaScript

yes

Java

yes

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

with a proxy-based framework, named couchdb-lounge

Shared Nothhing Auto-Sharding, Columnar Partitioning

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

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

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

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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