DBMS > Amazon CloudSearch vs. Microsoft Azure Cosmos DB vs. Microsoft Azure SQL Database vs. Microsoft Azure Table Storage vs. Oracle Berkeley DB

System Properties Comparison Amazon CloudSearch vs. Microsoft Azure Cosmos DB vs. Microsoft Azure SQL Database vs. Microsoft Azure Table Storage vs. Oracle Berkeley DB

Editorial information provided by DB-Engines

Name

Description

A hosted search engine service by Amazon with the data stored in Amazons cloud

Globally distributed, horizontally scalable, multi-model database service

Database as a Service offering with high compatibility to Microsoft SQL Server

A Wide Column Store for rapid development using massive semi-structured datasets

Widely used in-process key-value store

Primary database model

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Wide column store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	1.85
Rank	#137	Overall
	#12	Search engines

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Website

aws.amazon.com/cloudsearch

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/products/azure-sql/database

azure.microsoft.com/en-us/services/storage/tables

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

docs.aws.amazon.com/cloudsearch

learn.microsoft.com/azure/cosmos-db

docs.microsoft.com/en-us/azure/azure-sql

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Amazon

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2012

2014

2010

2012

1994

Current release

V12

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

commercial license 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.

Implementation language

C++

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

only with the Berkeley DB XML edition

Secondary indexes

yes

all search fields are automatically indexed

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

SQL interfaced based on SQLite is available

APIs and other access methods

HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

JavaScript

Transact SQL

Triggers

JavaScript

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

yes

Implicit feature of the cloud service

yes, with always 3 replicas available

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

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

authentication via encrypted signatures

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

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