DBMS > Apache Doris vs. Apache IoTDB vs. Derby vs. Riak TS vs. SQLite

System Properties Comparison Apache Doris vs. Apache IoTDB vs. Derby vs. Riak TS vs. SQLite

Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

Widely used embeddable, in-process RDBMS

Primary database model

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

Score	0.57
Rank	#244	Overall
	#113	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

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

iotdb.apache.org

db.apache.org/derby

www.sqlite.org

Technical documentation

github.com/apache/doris/wiki

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

db.apache.org/derby/manuals/index.html

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

www.sqlite.org/docs.html

Developer

Apache Software Foundation, originally contributed from Baidu

Apache Software Foundation

Open Source, formerly Basho Technologies

Dwayne Richard Hipp

Initial release

2017

2018

1997

2015

2000

Current release

1.2.2, February 2023

1.1.0, April 2023

10.17.1.0, November 2023

3.0.0, September 2022

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Apache version 2

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

Java

Erlang

Server operating systems

Linux

All OS with a Java VM (>= 1.8)

All OS with a Java VM

Linux
OS X

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

yes

restricted

yes

SQL

Support of SQL

yes

SQL-like query language

yes

yes, limited

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
MySQL client

JDBC
Native API

JDBC

HTTP API
Native Erlang Interface

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Java

C
C#
C++
Go
Java
Python
Scala

Java

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

user defined functions

yes

Java Stored Procedures

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Source-replica replication

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

yes

fine grained access rights according to SQL-standard

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