DBMS > Apache Druid vs. Apache Kylin vs. Riak TS

System Properties Comparison Apache Druid vs. Apache Kylin vs. Riak TS

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Editorial information provided by DB-Engines

Name

Description

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

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

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

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Time Series DBMS

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

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	1.20
Rank	#175	Overall
	#80	Relational DBMS

Score	0.25
Rank	#308	Overall
	#26	Time Series DBMS

Website

druid.apache.org

kylin.apache.org

Technical documentation

druid.apache.org/docs/latest/design

kylin.apache.org/docs

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

Developer

Apache Software Foundation and contributors

Apache Software Foundation, originally contributed from eBay Inc

Open Source, formerly Basho Technologies

Initial release

2012

2015

Current release

29.0.1, April 2024

3.1.0, July 2020

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2.0

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Erlang

Server operating systems

Linux
OS X
Unix

Linux

Linux
OS X

Data scheme

yes

schema-less columns are supported

yes

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

yes

restricted

SQL

Support of SQL

SQL for querying

ANSI SQL for queries (using Apache Calcite)

yes, limited

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

Server-side scripts

Stored procedures

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, via HDFS, S3 or other storage engines

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

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

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.

User concepts

Access control

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

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More resources

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