DBMS > Amazon Aurora vs. Apache Impala vs. Kdb vs. MonetDB vs. MySQL

System Properties Comparison Amazon Aurora vs. Apache Impala vs. Kdb vs. MonetDB vs. MySQL

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Analytic DBMS for Hadoop

High performance Time Series DBMS

A relational database management system that stores data in columns

Widely used open source RDBMS

Primary database model

Relational DBMS

Time Series DBMS
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store

Relational DBMS

Document store
Spatial DBMS

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

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

aws.amazon.com/rds/aurora

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

impala.apache.org/impala-docs.html

code.kx.com

www.monetdb.org/Documentation

dev.mysql.com/doc

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Kx Systems, a division of First Derivatives plc

MonetDB BV

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2015

2013

2000

kdb was released 2000, kdb+ in 2003

2004

1995

Current release

4.1.0, June 2022

3.6, May 2018

Dec2023 (11.49), December 2023

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

free 32-bit version

Open Source

Mozilla Public License 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C and C++

Server operating systems

hosted

Linux

Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

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

Secondary indexes

yes

table attribute 'grouped'

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language (q)

yes

SQL 2003 with some extensions

yes

with proprietary extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET
JDBC
ODBC
Proprietary native 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

All languages supporting JDBC/ODBC

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

yes, in SQL, C, R

yes

proprietary syntax

Triggers

yes

with views

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning

Sharding via remote tables

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Source-replica replication

none

Source-replica replication available in experimental status

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

rights management via user accounts

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.