DBMS > NSDb vs. Oracle Berkeley DB vs. OrigoDB vs. Sphinx

System Properties Comparison NSDb vs. Oracle Berkeley DB vs. OrigoDB vs. Sphinx

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Widely used in-process key-value store

A fully ACID in-memory object graph database

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

Document store
Object oriented DBMS

Search engine

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

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Score	0.00
Rank	#385	Overall
	#54	Document stores
	#21	Object oriented DBMS

Score	5.97
Rank	#56	Overall
	#5	Search engines

Website

nsdb.io

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

origodb.com

sphinxsearch.com

Technical documentation

nsdb.io/Architecture

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

origodb.com/docs

sphinxsearch.com/docs

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Robert Friberg et al

Sphinx Technologies Inc.

Initial release

2017

1994

2009

under the name LiveDB

2001

Current release

18.1.40, May 2020

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 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, Scala

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

C++

Server operating systems

Linux
macOS

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes: int, bigint, decimal, string

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

all fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

yes

SQL interfaced based on SQLite is available

SQL-like query language (SphinxQL)

APIs and other access methods

gRPC
HTTP REST
WebSocket

.NET Client API
HTTP API
LINQ

Proprietary protocol

Supported programming languages

Java
Scala

.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

.Net

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

Triggers

yes

only for the SQL API

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

client side managed; servers are not synchronized

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

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

Foreign keys

Referential integrity

depending on model

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

Using Apache Lucene

yes

Write ahead log

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

Role based authorization

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

What is NoSQL (Not Only SQL database)?
28 February 2022, TechTarget

Margo I. Seltzer
18 August 2020, Berkman Klein Center

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

How to store financial market data for backtesting
26 January 2019, Towards Data Science

A complete beginners guide to installing a Bitcoin Full Node on Linux (2018 Edition)
3 May 2018, hackernoon.com

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

Beyond the Concert Hall: 5 Organizations Making a Difference in Classical Music in 2018
22 December 2018, WQXR Radio

Here’s Why Russia Is Cracking Down on Google
23 February 2015, TIME

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

provided by Google News

Share this page