DBMS > Amazon CloudSearch vs. Amazon DynamoDB vs. Google Cloud Datastore vs. Oracle Berkeley DB

System Properties Comparison Amazon CloudSearch vs. Amazon DynamoDB vs. Google Cloud Datastore vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

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

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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

Widely used in-process key-value store

Primary database model

Search engine

Document store
Key-value store

Document store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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

Score	1.85
Rank	#137	Overall
	#12	Search engines

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

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

Website

aws.amazon.com/cloudsearch

aws.amazon.com/dynamodb

cloud.google.com/datastore

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

Technical documentation

docs.aws.amazon.com/cloudsearch

docs.aws.amazon.com/dynamodb

cloud.google.com/datastore/docs

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

Developer

Amazon

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2012

2008

1994

Current release

18.1.40, May 2020

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

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, 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

Typing

predefined data types such as float or date

yes

yes, details here

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

SQL

Support of SQL

SQL-like query language (GQL)

yes

SQL interfaced based on SQLite is available

APIs and other access methods

HTTP API

RESTful HTTP API

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

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
Go
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

using Google App Engine

Triggers

yes

by integration with AWS Lambda

Callbacks using the Google Apps Engine

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

yes

Multi-source replication using Paxos

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

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.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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 for users and roles can be defined via the AWS Identity and Access Management (IAM)

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

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