DBMS > Amazon SimpleDB vs. Elasticsearch vs. Firebase Realtime Database vs. RocksDB vs. SQLite

System Properties Comparison Amazon SimpleDB vs. Elasticsearch vs. Firebase Realtime Database vs. RocksDB vs. SQLite

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

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

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

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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

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

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	14.29
Rank	#39	Overall
	#6	Document stores

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

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

aws.amazon.com/simpledb

www.elastic.co/elasticsearch

firebase.google.com/products/realtime-database

rocksdb.org

www.sqlite.org

Technical documentation

docs.aws.amazon.com/simpledb

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

firebase.google.com/docs/database

github.com/facebook/rocksdb/wiki

www.sqlite.org/docs.html

Developer

Amazon

Elastic

Google

acquired by Google 2014

Facebook, Inc.

Dwayne Richard Hipp

Initial release

2007

2010

2012

2013

2000

Current release

8.6, January 2023

8.11.4, April 2024

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

Elastic License

commercial

Open Source

BSD

Open Source

Public Domain

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

All OS with a Java VM

hosted

Linux

server-less

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP/JSON API

Android
iOS
JavaScript API
RESTful HTTP API

C++ API
Java API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

Java
JavaScript
Objective-C

C
C++
Go
Java
Perl
Python
Ruby

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

limited functionality with using 'rules'

Triggers

yes

by using the 'percolation' feature

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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.

Memcached and Redis integration

yes

User concepts

Access control

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

yes, based on authentication and database rules

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