DBMS > Coveo vs. MySQL vs. NuoDB vs. Oracle Berkeley DB vs. Spark SQL

System Properties Comparison Coveo vs. MySQL vs. NuoDB vs. Oracle Berkeley DB vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

Widely used open source RDBMS

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

Widely used in-process key-value store

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Search engine

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	2.28
Rank	#114	Overall
	#11	Search engines

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.coveo.com

www.mysql.com

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

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

spark.apache.org/sql

Technical documentation

docs.coveo.com

dev.mysql.com/doc

doc.nuodb.com

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

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Coveo

Oracle

since 2010, originally MySQL AB, then Sun

Dassault Systèmes

originally NuoDB, Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Apache Software Foundation

Initial release

2012

1995

2013

1994

2014

Current release

8.4.0, April 2024

18.1.40, May 2020

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

limited edition free

Open Source

commercial license available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C and C++

C++

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

Scala

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Windows

Data scheme

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

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

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL interfaced based on SQLite is available

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

C#
Java
JavaScript
Python

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

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

proprietary syntax

Java, SQL

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

yes

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

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

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

Managed transparently by NuoDB

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

yes

ACID

not for MyISAM storage engine

ACID

tunable commit protocol

ACID

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

granular access controls, API key management, content filters

Users with fine-grained authorization concept

no user groups or roles

Standard SQL roles/ privileges, Administrative Users

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