DBMS > NuoDB vs. Oracle Berkeley DB vs. Snowflake vs. Solr

System Properties Comparison NuoDB vs. Oracle Berkeley DB vs. Snowflake vs. Solr

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Editorial information provided by DB-Engines

Name

Description

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Widely used in-process key-value store

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

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

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

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

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.3ds.com/nuodb-distributed-sql-database

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

www.snowflake.com

solr.apache.org

Technical documentation

doc.nuodb.com

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

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Dassault Systèmes

originally NuoDB, Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2013

1994

2014

2006

Current release

18.1.40, May 2020

9.6.0, April 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

commercial license available

commercial

Open Source

Apache Version 2

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++

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

Java

Server operating systems

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

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

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

Solr Parallel SQL Interface

APIs and other access methods

ADO.NET
JDBC
ODBC

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

.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 (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Java, SQL

user defined functions

Java plugins

Triggers

yes

only for the SQL API

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

data is dynamically stored/cached on the nodes where it is read/written

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Managed transparently by NuoDB

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

tunable commit protocol

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

Temporary table

yes

User concepts

Access control

Standard SQL roles/ privileges, Administrative Users

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

yes

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