DBMS > Amazon CloudSearch vs. Firebase Realtime Database vs. Microsoft SQL Server vs. Rockset vs. Solr

System Properties Comparison Amazon CloudSearch vs. Firebase Realtime Database vs. Microsoft SQL Server vs. Rockset vs. Solr

Editorial information provided by DB-Engines

Name

Description

A hosted search engine service by Amazon with the data stored in Amazons cloud

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

Microsofts flagship relational DBMS

A scalable, reliable search and analytics service in the cloud, built on RocksDB

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

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS
Search engine

Spatial DBMS

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

Score	1.81
Rank	#137	Overall
	#12	Search engines

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/cloudsearch

firebase.google.com/products/realtime-database

www.microsoft.com/en-us/sql-server

rockset.com

solr.apache.org

Technical documentation

docs.aws.amazon.com/cloudsearch

firebase.google.com/docs/database

learn.microsoft.com/en-US/sql/sql-server

docs.rockset.com

solr.apache.org/resources.html

Developer

Amazon

Google

acquired by Google 2014

Microsoft

Rockset

Apache Software Foundation

Initial release

2012

1989

2019

2006

Current release

SQL Server 2022, November 2022

9.6.1, May 2024

License

Commercial or Open Source

commercial

restricted free version is available

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

C++

Java

Server operating systems

hosted

Linux
Windows

hosted

All OS with a Java VM

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

Data scheme

yes

schema-free

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

dynamic typing

yes

supports customizable data types and automatic typing

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

ingestion from XML files supported

yes

Secondary indexes

yes

all search fields are automatically indexed

yes

all fields are automatically indexed

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

Read-only SQL queries, including JOINs

Solr Parallel SQL Interface

APIs and other access methods

HTTP API

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST

Java API
RESTful HTTP/JSON API

Supported programming languages

Java
JavaScript
Objective-C

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

limited functionality with using 'rules'

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Java plugins

Triggers

Callbacks are triggered when data changes

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

tables can be distributed across several files (horizontal partitioning); sharding through federation

Automatic sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

yes, but depending on the SQL-Server Edition

yes

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

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

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

authentication via encrypted signatures

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

Access rights for users and organizations can be defined via Rockset console

yes

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