DBMS > Apache IoTDB vs. etcd vs. MySQL vs. Riak KV vs. Yanza

System Properties Comparison Apache IoTDB vs. etcd vs. MySQL vs. Riak KV vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A distributed reliable key-value store

Widely used open source RDBMS

Distributed, fault tolerant key-value store

Time Series DBMS for IoT Applications

Primary database model

Time Series DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

iotdb.apache.org

etcd.io
github.com/etcd-io/etcd

www.mysql.com

yanza.com

Technical documentation

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

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

dev.mysql.com/doc

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

Developer

Apache Software Foundation

Oracle

since 2010, originally MySQL AB, then Sun

OpenSource, formerly Basho Technologies

Yanza

Initial release

2018

1995

2009

2015

Current release

1.1.0, April 2023

3.4, August 2019

8.4.0, April 2024

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache version 2, commercial enterprise edition

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

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Implementation language

Java

C and C++

Erlang

Server operating systems

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
Windows

experimental

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X

Windows

Data scheme

yes

schema-free

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.

yes

Secondary indexes

yes

restricted

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC
Native API

gRPC
JSON over HTTP

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API
Native Erlang Interface

HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

proprietary syntax

Erlang

Triggers

yes

yes, watching key changes

yes

pre-commit hooks and post-commit hooks

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication
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

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

links between data sets can be stored

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

yes

Users with fine-grained authorization concept

no user groups or roles

yes, using Riak Security

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