DBMS > Oracle Berkeley DB vs. PouchDB vs. Snowflake vs. Sphinx vs. STSdb

System Properties Comparison Oracle Berkeley DB vs. PouchDB vs. Snowflake vs. Sphinx vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Widely used in-process key-value store

JavaScript DBMS with an API inspired by CouchDB

Cloud-based data warehousing service for structured and semi-structured data

Open source search engine for searching in data from different sources, e.g. relational databases

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

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	2.34
Rank	#112	Overall
	#21	Document stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

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

pouchdb.com

www.snowflake.com

sphinxsearch.com

github.com/STSSoft/STSdb4

Technical documentation

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

pouchdb.com/guides

docs.snowflake.net/manuals/index.html

sphinxsearch.com/docs

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Apache Software Foundation

Snowflake Computing Inc.

Sphinx Technologies Inc.

STS Soft SC

Initial release

1994

2012

2014

2001

2011

Current release

18.1.40, May 2020

7.1.1, June 2019

3.5.1, February 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

commercial license available

Open Source

commercial

Open Source

GPL version 2, commercial licence 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

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

JavaScript

C++

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

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

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

yes

Secondary indexes

yes

via views

yes

full-text index on all search fields

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

SQL-like query language (SphinxQL)

APIs and other access methods

HTTP REST

only for PouchDB Server
JavaScript API

CLI Client
JDBC
ODBC

Proprietary protocol

.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

JavaScript (Node.js)
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
Java

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

Triggers

yes

only for the SQL API

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

with a proxy-based framework, named couchdb-lounge

yes

Sharding

Partitioning is done manually, search queries against distributed index is supported

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

yes

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

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

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

The original contents of fields are not stored in the Sphinx index.

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

Users with fine-grained authorization concept, user roles and pluggable authentication

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