DBMS > Amazon Redshift vs. Google Cloud Firestore vs. RethinkDB vs. Solr vs. SQream DB

System Properties Comparison Amazon Redshift vs. Google Cloud Firestore vs. RethinkDB vs. Solr vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

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

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Secondary database models

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

aws.amazon.com/redshift

firebase.google.com/products/firestore

rethinkdb.com

solr.apache.org

sqream.com

Technical documentation

docs.aws.amazon.com/redshift

firebase.google.com/docs/firestore

rethinkdb.com/docs

solr.apache.org/resources.html

docs.sqream.com

Developer

Amazon (based on PostgreSQL)

Google

The Linux Foundation

since July 2017

Apache Software Foundation

SQream Technologies

Initial release

2012

2017

2009

2006

2017

Current release

2.4.1, August 2020

9.6.0, April 2024

2022.1.6, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++, CUDA, Haskell, Java, Scala

Server operating systems

hosted

Linux
OS X
Windows

All OS with a Java VM

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

Linux

Data scheme

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

string, binary, float, bool, date, geometry

yes

supports customizable data types and automatic typing

yes, ANSI Standard SQL 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

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

Solr Parallel SQL Interface

yes

APIs and other access methods

JDBC
ODBC

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

Java API
RESTful HTTP/JSON API

.Net
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

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

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes, Firebase Rules & Cloud Functions

Java plugins

user defined functions in Python

Triggers

yes, with Cloud Functions

Client-side triggers through changefeeds

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

range based

Sharding

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

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

Immediate 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

Atomic single-document operations

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

yes

users and table-level permissions

yes

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