DBMS > Apache Impala vs. FatDB vs. MySQL vs. NuoDB vs. RavenDB

System Properties Comparison Apache Impala vs. FatDB vs. MySQL vs. NuoDB vs. RavenDB

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Analytic DBMS for Hadoop

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Widely used open source RDBMS

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Relational DBMS

Document store
Key-value store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Document store

Secondary database models

Document store

Document store
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Website

impala.apache.org

www.mysql.com

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

ravendb.net

Technical documentation

impala.apache.org/impala-docs.html

dev.mysql.com/doc

doc.nuodb.com

ravendb.net/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

FatCloud

Oracle

since 2010, originally MySQL AB, then Sun

Dassault Systèmes

originally NuoDB, Inc.

Hibernating Rhinos

Initial release

2013

2012

1995

2013

2010

Current release

4.1.0, June 2022

8.4.0, April 2024

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

limited edition free

Open Source

AGPL version 3, commercial license available

Cloud-based only

Only available as a cloud service

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Implementation language

C++

C and C++

C++

Server operating systems

Linux

Windows

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux
macOS
Raspberry Pi
Windows

Data scheme

yes

schema-free

yes

schema-free

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Via inetgration in SQL Server

yes

with proprietary extensions

yes

SQL-like query language (RQL)

APIs and other access methods

JDBC
ODBC

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
ODBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

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#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

via applications

yes

proprietary syntax

Java, SQL

yes

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

selectable replication factor

Multi-source replication
Source-replica replication

yes

Managed transparently by NuoDB

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

tunable commit protocol

ACID, Cluster-wide transaction available

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Can implement custom security layer via applications

Users with fine-grained authorization concept

no user groups or roles

Standard SQL roles/ privileges, Administrative Users

Authorization levels configured per client per database

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