DBMS > Amazon SimpleDB vs. atoti vs. Cubrid vs. Google Cloud Datastore vs. STSdb

System Properties Comparison Amazon SimpleDB vs. atoti vs. Cubrid vs. Google Cloud Datastore vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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

Score	1.88
Rank	#133	Overall
	#23	Key-value stores

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

aws.amazon.com/simpledb

atoti.io

cubrid.com (korean)
cubrid.org (english)

cloud.google.com/datastore

github.com/STSSoft/STSdb4

Technical documentation

docs.aws.amazon.com/simpledb

docs.atoti.io

cubrid.org/manuals

cloud.google.com/datastore/docs

Developer

Amazon

ActiveViam

CUBRID Corporation, CUBRID Foundation

Google

STS Soft SC

Initial release

2007

2008

2011

Current release

11.0, January 2021

4.0.8, September 2015

License

Commercial or Open Source

commercial

free versions available

Open Source

Apache Version 2.0

commercial

Open Source

GPLv2, 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

Java

C, C++, Java

Server operating systems

hosted

Linux
Windows

hosted

Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

primitive types and user defined types (classes)

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.

Secondary indexes

yes

All columns are indexed automatically

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

SQL-like query language (GQL)

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

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

.NET Client API

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

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

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

C#
Java

Server-side scripts

Stored procedures

Python

Java Stored Procedures

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding, horizontal partitioning

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication using Paxos

none

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

Eventual Consistency
Immediate Consistency

can be specified for read operations

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.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

Concurrent data updates can be detected by the application

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

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

fine grained access rights according to SQL-standard

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

More information provided by the system vendor

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Related products and services

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More resources

DB-Engines blog posts

The popularity of cloud-based DBMSs has increased tenfold in four years
7 February 2017, Matthias Gelbmann

Amazon - the rising star in the DBMS market
3 August 2015, Matthias Gelbmann

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