DBMS > Amazon Redshift vs. Apache Druid vs. EsgynDB vs. PouchDB vs. RDF4J

System Properties Comparison Amazon Redshift vs. Apache Druid vs. EsgynDB vs. PouchDB vs. RDF4J

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

JavaScript DBMS with an API inspired by CouchDB

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Document store

RDF store

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

pouchdb.com/guides

rdf4j.org/documentation

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Esgyn

Apache Software Foundation

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2012

2015

2012

2004

Current release

29.0.1, April 2024

7.1.1, June 2019

License

Commercial or Open Source

commercial

Open Source

Apache license v2

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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++, Java

JavaScript

Java

Server operating systems

hosted

Linux
OS X
Unix

Linux

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Unix
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

restricted

yes

via views

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

yes

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

HTTP REST

only for PouchDB Server
JavaScript API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

JavaScript

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures

View functions in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

Multi-source replication between multi datacenters

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

in-memory storage is supported as well

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

fine grained access rights according to SQL-standard

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