DBMS > Amazon Aurora vs. Amazon Redshift vs. Firebase Realtime Database vs. Solr vs. XTDB

System Properties Comparison Amazon Aurora vs. Amazon Redshift vs. Firebase Realtime Database vs. Solr vs. XTDB

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Large scale data warehouse service for use with business intelligence tools

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

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Document store

Spatial DBMS

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Trend Chart

Score	7.57
Rank	#51	Overall
	#32	Relational DBMS

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

aws.amazon.com/rds/aurora

aws.amazon.com/redshift

firebase.google.com/products/realtime-database

solr.apache.org

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

docs.aws.amazon.com/redshift

firebase.google.com/docs/database

solr.apache.org/resources.html

www.xtdb.com/docs

Developer

Amazon

Amazon (based on PostgreSQL)

Google

acquired by Google 2014

Apache Software Foundation

Juxt Ltd.

Initial release

2015

2012

2006

2019

Current release

9.6.1, May 2024

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

MIT License

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

Clojure

Server operating systems

hosted

All OS with a Java VM

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

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

yes, extensible-data-notation format

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

restricted

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

Solr Parallel SQL Interface

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

Android
iOS
JavaScript API
RESTful HTTP API

Java API
RESTful HTTP/JSON API

HTTP REST
JDBC

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC

Java
JavaScript
Objective-C

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

Clojure
Java

Server-side scripts

Stored procedures

yes

user defined functions

in Python

limited functionality with using 'rules'

Java plugins

Triggers

yes

Callbacks are triggered when data changes

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

yes, each node contains all data

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

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

yes

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

yes

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