DBMS > Amazon Aurora vs. Apache Impala vs. Microsoft SQL Server vs. Riak KV vs. SwayDB

System Properties Comparison Amazon Aurora vs. Apache Impala vs. Microsoft SQL Server vs. Riak KV vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Analytic DBMS for Hadoop

Microsofts flagship relational DBMS

Distributed, fault tolerant key-value store

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Key-value store

Secondary database models

Document store

Document store
Graph DBMS
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	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

aws.amazon.com/rds/aurora

impala.apache.org

www.microsoft.com/en-us/sql-server

swaydb.simer.au

Technical documentation

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

impala.apache.org/impala-docs.html

learn.microsoft.com/en-US/sql/sql-server

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Microsoft

OpenSource, formerly Basho Technologies

Simer Plaha

Initial release

2015

2013

1989

2009

2018

Current release

4.1.0, June 2022

SQL Server 2022, November 2022

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

Apache version 2, commercial enterprise edition

Open Source

GNU Affero GPL V3.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Erlang

Scala

Server operating systems

hosted

Linux

Linux
Windows

Linux
OS X

Data scheme

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

restricted

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP API
Native Erlang Interface

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++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

yes, but depending on the SQL-Server Edition

selectable replication factor

none

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

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

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

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

fine grained access rights according to SQL-standard

yes, using Riak Security

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