DBMS > Amazon Redshift vs. Apache Druid vs. EXASOL vs. HEAVY.AI vs. Solr

System Properties Comparison Amazon Redshift vs. Apache Druid vs. EXASOL vs. HEAVY.AI vs. Solr

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Search engine

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	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

druid.apache.org

www.exasol.com

github.com/heavyai/heavydb
www.heavy.ai

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

www.exasol.com/resources

docs.heavy.ai

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Exasol

HEAVY.AI, Inc.

Apache Software Foundation

Initial release

2012

2000

2016

2006

Current release

29.0.1, April 2024

5.10, January 2022

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache license v2

commercial

Open Source

Apache Version 2; enterprise edition available

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

Java

C++ and CUDA

Java

Server operating systems

hosted

Linux
OS X
Unix

Linux

All OS with a Java VM

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

Data scheme

yes

schema-less columns are supported

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

All search fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

yes

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC
Thrift
Vega

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Java
Lua
Python
R

All languages supporting JDBC/ODBC/Thrift
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

user defined functions

in Python

user defined functions

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

yes

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