DBMS > Amazon Redshift vs. Hive vs. RethinkDB vs. Solr

System Properties Comparison Amazon Redshift vs. Hive vs. RethinkDB vs. Solr

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Primary database model

Relational DBMS

Document store

Search engine

Secondary database models

Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

hive.apache.org

rethinkdb.com

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

cwiki.apache.org/confluence/display/Hive/Home

rethinkdb.com/docs

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

initially developed by Facebook

The Linux Foundation

since July 2017

Apache Software Foundation

Initial release

2012

2009

2006

Current release

3.1.3, April 2022

2.4.1, August 2020

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2

Open Source

Apache Version 2

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++

Java

Server operating systems

hosted

All OS with a Java VM

Linux
OS X
Windows

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

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

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

Secondary indexes

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Thrift

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
PHP
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

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

Java plugins

Triggers

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

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

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

Atomic single-document operations

optimistic locking

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

yes

users and table-level permissions

yes

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