DBMS > Amazon Aurora vs. etcd vs. Hive vs. MySQL vs. Spark SQL

System Properties Comparison Amazon Aurora vs. etcd vs. Hive vs. MySQL vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

A distributed reliable key-value store

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Widely used open source RDBMS

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

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store

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	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/rds/aurora

etcd.io
github.com/etcd-io/etcd

hive.apache.org

www.mysql.com

spark.apache.org/sql

Technical documentation

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

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

cwiki.apache.org/confluence/display/Hive/Home

dev.mysql.com/doc

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

Developer

Amazon

Apache Software Foundation

initially developed by Facebook

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2015

2012

1995

2014

Current release

3.4, August 2019

3.1.3, April 2022

8.4.0, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

Scala

Server operating systems

hosted

FreeBSD
Linux
Windows

experimental

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

gRPC
JSON over HTTP

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

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++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

C++
Java
PHP
Python

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

Triggers

yes

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

selectable replication factor

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

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

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

Users with fine-grained authorization concept

no user groups or roles

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