DBMS > KairosDB vs. Oracle Berkeley DB vs. Sphinx

System Properties Comparison KairosDB vs. Oracle Berkeley DB vs. Sphinx

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Distributed Time Series DBMS based on Cassandra or H2

Widely used in-process key-value store

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

Primary database model

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Search engine

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

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

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

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

github.com/kairosdb/kairosdb

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

sphinxsearch.com

Technical documentation

kairosdb.github.io

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

sphinxsearch.com/docs

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Sphinx Technologies Inc.

Initial release

2013

1994

2001

Current release

1.2.2, November 2018

18.1.40, May 2020

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

commercial license available

Open Source

GPL version 2, commercial licence available

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)

C++

Server operating systems

Linux
OS X
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

full-text index on all search fields

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

SQL-like query language (SphinxQL)

APIs and other access methods

Graphite protocol
HTTP REST
Telnet API

Proprietary protocol

Supported programming languages

Java
JavaScript

Node.js
PHP
Python

.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++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

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

In-memory capabilities

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

yes

User concepts

Access control

simple password-based access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

show all

Recent citations in the news

Expo: Real Time A/B Testing and Monitoring with Spark Streaming and Kafka at Walmart Labs
24 May 2019, InfoQ.com

provided by Google News

Margo Seltzer Named ACM Athena Lecturer for Technical and Mentoring Contributions
26 April 2023, HPCwire

EC will investigate the Oracle/Sun takeover due to concerns about MySQL
3 September 2009, The Guardian

Database Trends Report: SQL Beats NoSQL, MySQL Most Popular -- ADTmag
5 March 2019, ADT Magazine

The importance of bitcoin nodes and how to start one
9 May 2014, The Merkle News

A Quick Look at Open Source Databases for Mobile App Development
29 April 2018, Open Source For You

provided by Google News

Switching From Sphinx to MkDocs Documentation — What Did I Gain and Lose
2 February 2024, Towards Data Science

Manticore is a Faster Alternative to Elasticsearch in C++
25 July 2022, hackernoon.com

Perplexity AI: From Its Use To Operation, Everything You Need To Know About Googles Newest Challenger
11 January 2024, Free Press Journal

The Pirate Bay was recently down for over a week due to a DDoS attack
29 October 2019, The Hacker News

How to Build 600+ Links in One Month
4 September 2020, Search Engine Journal

provided by Google News

Share this page