DBMS > Amazon Aurora vs. Datomic vs. Google Cloud Datastore vs. KeyDB vs. MySQL

System Properties Comparison Amazon Aurora vs. Datomic vs. Google Cloud Datastore vs. KeyDB vs. MySQL

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

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	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

aws.amazon.com/rds/aurora

www.datomic.com

cloud.google.com/datastore

github.com/Snapchat/KeyDB
keydb.dev

www.mysql.com

Technical documentation

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

docs.datomic.com

cloud.google.com/datastore/docs

docs.keydb.dev

dev.mysql.com/doc

Developer

Amazon

Cognitect

Google

EQ Alpha Technology Ltd.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2015

2012

2008

2019

1995

Current release

1.0.6735, June 2023

8.4.0, April 2024

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

BSD-3

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

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

Java, Clojure

C++

C and C++

Server operating systems

hosted

All OS with a Java VM

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

yes

SQL

Support of SQL

yes

SQL-like query language (GQL)

yes

with proprietary extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Proprietary protocol

RESP - REdis Serialization Protoco

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

Clojure
Java

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

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

Transaction Functions

using Google App Engine

Lua

yes

proprietary syntax

Triggers

yes

By using transaction functions

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

But extensive use of caching in the application peers

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

But extensive use of caching in the application peers

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Optimistic locking, atomic execution of commands blocks and scripts

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple password-based access control and ACL

Users with fine-grained authorization concept

no user groups or roles

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