DBMS > Amazon Redshift vs. Apache Impala vs. Newts vs. Riak TS vs. Solr

System Properties Comparison Amazon Redshift vs. Apache Impala vs. Newts vs. Riak TS vs. Solr

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

Time Series DBMS based on Cassandra

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

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

Primary database model

Relational DBMS

Time Series DBMS

Search engine

Secondary database models

Document store

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

impala.apache.org

opennms.github.io/newts

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

github.com/OpenNMS/newts/wiki

www.tiot.jp/riak-docs/riak/ts/latest

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

OpenNMS Group

Open Source, formerly Basho Technologies

Apache Software Foundation

Initial release

2012

2013

2014

2015

2006

Current release

4.1.0, June 2022

3.0.0, September 2022

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Apache Version 2

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

Erlang

Java

Server operating systems

hosted

Linux

Linux
OS X
Windows

Linux
OS X

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

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

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

yes, limited

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

HTTP REST
Java API

HTTP API
Native Erlang Interface

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

Java

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

.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

Erlang

Java plugins

Triggers

yes

pre-commit hooks and post-commit hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

selectable replication factor

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

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

links between datasets can be stored

Transaction concepts

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

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

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