DBMS > Amazon DynamoDB vs. Amazon SimpleDB vs. IRONdb vs. Kinetica vs. RDF4J

System Properties Comparison Amazon DynamoDB vs. Amazon SimpleDB vs. IRONdb vs. Kinetica vs. RDF4J

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Fully vectorized database across both GPUs and CPUs

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Document store
Key-value store

Secondary database models

Spatial DBMS
Time Series DBMS

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

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

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

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

aws.amazon.com/dynamodb

aws.amazon.com/simpledb

www.circonus.com/solutions/time-series-database/

www.kinetica.com

rdf4j.org

Technical documentation

docs.aws.amazon.com/dynamodb

docs.aws.amazon.com/simpledb

docs.circonus.com/irondb/category/getting-started

docs.kinetica.com

rdf4j.org/documentation

Developer

Amazon

Circonus LLC.

Kinetica

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2012

2007

2017

2012

2004

Current release

V0.10.20, January 2018

7.1, August 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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 and C++

C, C++

Java

Server operating systems

hosted

Linux

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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.

Secondary indexes

yes

All columns are indexed automatically

yes

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

HTTP API

JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C++
Java
JavaScript (Node.js)
Python

Java
PHP
Python

Server-side scripts

Stored procedures

yes, in Lua

user defined functions

yes

Triggers

yes

by integration with AWS Lambda

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding must be implemented in the application

Automatic, metric affinity per node

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

configurable replication factor, datacenter aware

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

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

Concurrent data updates can be detected by the application

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

GPU vRAM or System RAM

User concepts

Access control

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

Access rights for users and roles on table level

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