DBMS > Apache Doris vs. Apache Druid vs. Riak TS vs. VoltDB

System Properties Comparison Apache Doris vs. Apache Druid vs. Riak TS vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

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

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Time Series DBMS

Relational DBMS

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

Score	0.61
Rank	#235	Overall
	#110	Relational DBMS

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Score	1.38
Rank	#156	Overall
	#72	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

druid.apache.org

www.voltdb.com

Technical documentation

github.com/apache/doris/wiki

druid.apache.org/docs/latest/design

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

docs.voltdb.com

Developer

Apache Software Foundation, originally contributed from Baidu

Apache Software Foundation and contributors

Open Source, formerly Basho Technologies

VoltDB Inc.

Initial release

2017

2012

2015

2010

Current release

1.2.2, February 2023

30.0.0, June 2024

3.0.0, September 2022

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache license v2

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Java, C++

Server operating systems

Linux

Linux
OS X
Unix

Linux
OS X

for development

Data scheme

yes

schema-less columns are supported

schema-free

yes

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

yes

SQL

Support of SQL

yes

SQL for querying

yes, limited

yes

only a subset of SQL 99

APIs and other access methods

JDBC
MySQL client

JDBC
RESTful HTTP/JSON API

HTTP API
Native Erlang Interface

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Java

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

Erlang

Java

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

manual/auto, time-based

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, via HDFS, S3 or other storage engines

selectable replication factor

Multi-source replication
Source-replica replication

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

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

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

Users and roles with access to stored procedures

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