DBMS > Amazon SimpleDB vs. Atos Standard Common Repository vs. Google BigQuery vs. InfinityDB vs. RocksDB

System Properties Comparison Amazon SimpleDB vs. Atos Standard Common Repository vs. Google BigQuery vs. InfinityDB vs. RocksDB

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

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

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Large scale data warehouse service with append-only tables

A Java embedded Key-Value Store which extends the Java Map interface

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Key-value store

Document store
Key-value store

Relational DBMS

Key-value store

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

Score	1.85
Rank	#138	Overall
	#24	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

aws.amazon.com/simpledb

atos.net/en/convergence-creators/portfolio/standard-common-repository

cloud.google.com/bigquery

boilerbay.com

rocksdb.org

Technical documentation

docs.aws.amazon.com/simpledb

cloud.google.com/bigquery/docs

boilerbay.com/infinitydb/manual

github.com/facebook/rocksdb/wiki

Developer

Amazon

Atos Convergence Creators

Google

Boiler Bay Inc.

Facebook, Inc.

Initial release

2007

2016

2010

2002

2013

Current release

1703

4.0

8.11.4, April 2024

License

Commercial or Open Source

commercial

Open Source

BSD

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++

Server operating systems

hosted

Linux

hosted

All OS with a Java VM

Linux

Data scheme

schema-free

Schema and schema-less with LDAP views

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

Typing

predefined data types such as float or date

optional

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

All columns are indexed automatically

yes

manual creation possible, using inversions based on multi-value capability

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

LDAP

RESTful HTTP/JSON API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

C++ API
Java API

Supported programming languages

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

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

cell division

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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 configuration

Immediate Consistency

READ-COMMITTED or SERIALIZED

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

Concurrent data updates can be detected by the application

Atomic execution of specific operations

Since BigQuery is designed for querying data

ACID

Optimistic locking for transactions; no isolation for bulk loads

yes

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

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

LDAP bind authentication

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

Google Cloud Identity & Access Management (IAM)

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