DBMS > AgensGraph vs. Amazon Aurora vs. Apache Impala vs. MySQL vs. Riak TS

System Properties Comparison AgensGraph vs. Amazon Aurora vs. Apache Impala vs. MySQL vs. Riak TS

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

MySQL and PostgreSQL compatible cloud service by Amazon

Analytic DBMS for Hadoop

Widely used open source RDBMS

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Graph DBMS
Relational DBMS

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	0.16
Rank	#333	Overall
	#30	Graph DBMS
	#146	Relational DBMS

Score	8.50
Rank	#49	Overall
	#31	Relational DBMS

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	0.25
Rank	#308	Overall
	#26	Time Series DBMS

Website

bitnine.net/agensgraph

aws.amazon.com/rds/aurora

impala.apache.org

www.mysql.com

Technical documentation

bitnine.net/documentation

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

impala.apache.org/impala-docs.html

dev.mysql.com/doc

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

Developer

Bitnine Global Inc.

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Oracle

since 2010, originally MySQL AB, then Sun

Open Source, formerly Basho Technologies

Initial release

2016

2015

2013

1995

2015

Current release

2.1, December 2018

4.1.0, June 2022

8.3.0, January 2024

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

Apache Version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

C and C++

Erlang

Server operating systems

Linux
OS X
Windows

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X

Data scheme

depending on used data model

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

with proprietary extensions

yes, limited

APIs and other access methods

Cypher Query Language
JDBC

ADO.NET
JDBC
ODBC

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API
Native Erlang Interface

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

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

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

horizontal partitioning

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Multi-source replication
Source-replica replication

selectable replication factor

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

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

links between datasets can be stored

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

based on Apache Sentry and Kerberos

Users with fine-grained authorization concept

no user groups or roles

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