DBMS > Amazon Redshift vs. Apache Druid vs. Hypertable vs. Riak TS vs. SWC-DB

System Properties Comparison Amazon Redshift vs. Apache Druid vs. Hypertable vs. Riak TS vs. SWC-DB

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

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

An open source BigTable implementation based on distributed file systems such as Hadoop

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

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Wide column store

Time Series DBMS

Wide column store

Secondary database models

Time Series DBMS

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	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

aws.amazon.com/redshift

druid.apache.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

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

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Hypertable Inc.

Open Source, formerly Basho Technologies

Alex Kashirin

Initial release

2012

2009

2015

2020

Current release

29.0.1, April 2024

0.9.8.11, March 2016

3.0.0, September 2022

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

GNU version 3. Commercial license available

Open Source

GPL V3

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++

Erlang

C++

Server operating systems

hosted

Linux
OS X
Unix

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X

Linux

Data scheme

yes

schema-less columns are supported

schema-free

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

restricted

only exact value or prefix value scans

restricted

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

yes, limited

SQL-like query language

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

C++ API
Thrift

HTTP API
Native Erlang Interface

Proprietary protocol
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C++
Java
Perl
PHP
Python
Ruby

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

C++

Server-side scripts

Stored procedures

user defined functions

in Python

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

selectable replication factor on file system level

selectable replication factor

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

Immediate 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

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

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