DBMS > Amazon Aurora vs. Drizzle vs. Ehcache vs. Google BigQuery vs. IBM Cloudant

System Properties Comparison Amazon Aurora vs. Drizzle vs. Ehcache vs. Google BigQuery vs. IBM Cloudant

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL and PostgreSQL compatible cloud service by Amazon

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A widely adopted Java cache with tiered storage options

Large scale data warehouse service with append-only tables

Database as a Service offering based on Apache CouchDB

Primary database model

Secondary database models

Document store

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

Score	8.50
Rank	#49	Overall
	#31	Relational DBMS

Score	5.23
Rank	#68	Overall
	#8	Key-value stores

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	2.73
Rank	#108	Overall
	#20	Document stores

Website

aws.amazon.com/rds/aurora

www.ehcache.org

cloud.google.com/bigquery

www.ibm.com/products/cloudant

Technical documentation

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

www.ehcache.org/documentation

cloud.google.com/bigquery/docs

cloud.ibm.com/docs/Cloudant

Developer

Amazon

Drizzle project, originally started by Brian Aker

Terracotta Inc, owned by Software AG

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Initial release

2015

2008

2009

2010

Current release

7.2.4, September 2012

3.10.0, March 2022

License

Commercial or Open Source

commercial

Open Source

GNU GPL

Open Source

Apache Version 2; commercial licenses available

commercial

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.

Implementation language

C++

Java

Erlang

Server operating systems

hosted

FreeBSD
Linux
OS X

All OS with a Java VM

hosted

Data scheme

yes

schema-free

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

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC

JCache

RESTful HTTP/JSON 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++
Java
PHP

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

View functions (Map-Reduce) in JavaScript

Triggers

yes

hooks for callbacks inside the server can be used.

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

by using Terracotta Server

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

yes

by using Terracotta Server

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

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

Since BigQuery is designed for querying data

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users can be defined per database

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