DBMS > Amazon SimpleDB vs. Postgres-XL vs. RocksDB vs. Solr vs. STSdb

System Properties Comparison Amazon SimpleDB vs. Postgres-XL vs. RocksDB vs. Solr 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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

A widely used distributed, scalable search engine based on Apache Lucene

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

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

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

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

Website

aws.amazon.com/simpledb

www.postgres-xl.org

rocksdb.org

solr.apache.org

github.com/STSSoft/STSdb4

Technical documentation

docs.aws.amazon.com/simpledb

www.postgres-xl.org/documentation

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

Amazon

Facebook, Inc.

Apache Software Foundation

STS Soft SC

Initial release

2007

2014

since 2012, originally named StormDB

2013

2006

2011

Current release

10 R1, October 2018

9.2.1, May 2024

9.6.1, May 2024

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Mozilla public license

Open Source

BSD

Open Source

Apache Version 2

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

C++

Java

Server operating systems

hosted

Linux
macOS

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Windows

Data scheme

schema-free

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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.

yes

XML type, but no XML query functionality

yes

Secondary indexes

yes

All columns are indexed automatically

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

distributed, parallel query execution

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP API

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

C++ API
Java API

Java API
RESTful HTTP/JSON API

.NET Client API

Supported programming languages

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C++
Go
Java
Perl
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C#
Java

Server-side scripts

Stored procedures

user defined functions

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual 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

MVCC

yes

optimistic locking

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)

fine grained access rights according to SQL-standard

yes

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