DBMS > Amazon Aurora vs. CockroachDB vs. Hazelcast vs. IBM Cloudant vs. MySQL

System Properties Comparison Amazon Aurora vs. CockroachDB vs. Hazelcast vs. IBM Cloudant vs. MySQL

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

A widely adopted in-memory data grid

Database as a Service offering based on Apache CouchDB

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

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

Score	7.57
Rank	#51	Overall
	#32	Relational DBMS

Score	5.73
Rank	#58	Overall
	#34	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

aws.amazon.com/rds/aurora

www.cockroachlabs.com

hazelcast.com

www.ibm.com/products/cloudant

www.mysql.com

Technical documentation

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

www.cockroachlabs.com/docs

hazelcast.org/imdg/docs

cloud.ibm.com/docs/Cloudant

dev.mysql.com/doc

Developer

Amazon

Cockroach Labs

Hazelcast

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2015

2008

2010

1995

Current release

24.1.0, May 2024

5.3.6, November 2023

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0, commercial license available

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

Erlang

C and C++

Server operating systems

hosted

Linux
macOS
Windows

All OS with a Java VM

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

dynamic schema

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

yes

yes, wire compatible with PostgreSQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC

JCache
JPA
Memcached protocol
RESTful HTTP API

RESTful HTTP/JSON API

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

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C#
Java
JavaScript
Objective-C
PHP
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

Event Listeners, Executor Services

View functions (Map-Reduce) in JavaScript

yes

proprietary syntax

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication using RAFT

yes

Replicated Map

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

one or two-phase-commit; repeatable reads; read commited

atomic operations within a document possible

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

Role-based access control

Access rights for users can be defined per database

Users with fine-grained authorization concept

no user groups or roles

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