DBMS > Amazon SimpleDB vs. Drizzle vs. IRONdb vs. RDF4J vs. TimescaleDB

System Properties Comparison Amazon SimpleDB vs. Drizzle vs. IRONdb vs. RDF4J vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

IRONdb seems to be discontinued. Therefore it is excluded 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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Relational DBMS

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

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

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

aws.amazon.com/simpledb

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

rdf4j.org

www.timescale.com

Technical documentation

docs.aws.amazon.com/simpledb

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

rdf4j.org/documentation

docs.timescale.com

Developer

Amazon

Drizzle project, originally started by Brian Aker

Circonus LLC.

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

Timescale

Initial release

2007

2008

2017

2004

2017

Current release

7.2.4, September 2012

V0.10.20, January 2018

2.13.0, November 2023

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

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++

C and C++

Java

Server operating systems

hosted

FreeBSD
Linux
OS X

Linux

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

SQL

Support of SQL

yes

with proprietary extensions

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

yes

full PostgreSQL SQL syntax

APIs and other access methods

RESTful HTTP API

JDBC

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

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

C
C++
Java
PHP

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

Java
PHP
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes, in Lua

yes

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

Automatic, metric affinity per node

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

configurable replication factor, datacenter aware

none

Source-replica replication with hot standby and reads on replicas

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 per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Concurrent data updates can be detected by the application

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

In-memory capabilities

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

User concepts

Access control

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

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