DBMS > MySQL vs. NuoDB vs. OrientDB vs. Solr

System Properties Comparison MySQL vs. NuoDB vs. OrientDB vs. Solr

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Editorial information provided by DB-Engines

Name

Description

Widely used open source RDBMS

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

Multi-model DBMS (Document, Graph, Key/Value)

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

Primary database model

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Document store
Graph DBMS
Key-value store

Search engine

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	0.99
Rank	#195	Overall
	#92	Relational DBMS

Score	3.27
Rank	#97	Overall
	#16	Document stores
	#6	Graph DBMS
	#15	Key-value stores

Score	44.28
Rank	#24	Overall
	#3	Search engines

Website

www.mysql.com

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

orientdb.org

solr.apache.org

Technical documentation

dev.mysql.com/doc

doc.nuodb.com

www.orientdb.com/docs/last/index.html

solr.apache.org/resources.html

Developer

Oracle

since 2010, originally MySQL AB, then Sun

Dassault Systèmes

originally NuoDB, Inc.

OrientDB LTD; CallidusCloud; SAP

Apache Software Foundation

Initial release

1995

2013

2010

2006

Current release

8.3.0, January 2024

3.2.29, March 2024

9.5.0, February 2024

License

Commercial or Open Source

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

limited edition free

Open Source

Apache version 2

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

C++

Java

Server operating systems

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

All OS with a Java JDK (>= JDK 6)

All OS with a Java VM

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

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL-like query language, no joins

Solr Parallel SQL Interface

APIs and other access methods

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
ODBC

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

yes

proprietary syntax

Java, SQL

Java, Javascript

Java plugins

Triggers

yes

Hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Managed transparently by NuoDB

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

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

not for MyISAM storage engine

yes

relationship in graphs

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

tunable commit protocol

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Users with fine-grained authorization concept

no user groups or roles

Standard SQL roles/ privileges, Administrative Users

Access rights for users and roles; record level security configurable

yes

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