DBMS > Apache IoTDB vs. Hazelcast vs. MySQL vs. Sadas Engine vs. WakandaDB

System Properties Comparison Apache IoTDB vs. Hazelcast vs. MySQL vs. Sadas Engine vs. WakandaDB

Editorial information provided by DB-Engines

Name

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 widely adopted in-memory data grid

Widely used open source RDBMS

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Time Series DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Object oriented DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

Technical documentation

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

hazelcast.org/imdg/docs

dev.mysql.com/doc

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

wakanda.github.io/doc

Developer

Apache Software Foundation

Hazelcast

Oracle

since 2010, originally MySQL AB, then Sun

SADAS s.r.l.

Wakanda SAS

Initial release

2018

2008

1995

2006

2012

Current release

1.1.0, April 2023

5.3.6, November 2023

8.4.0, April 2024

8.0

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

free trial version available

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

Java

C and C++

C++

C++, JavaScript

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

AIX
Linux
Windows

Linux
OS X
Windows

Data scheme

yes

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.

yes

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
Native API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC
Proprietary protocol

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

JavaScript

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

proprietary syntax

yes

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

horizontal partitioning

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

yes

Replicated Map

Multi-source replication
Source-replica replication

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 or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

not for MyISAM storage engine

ACID

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

managed by 'Learn by Usage'

User concepts

Access control

yes

Role-based access control

Users with fine-grained authorization concept

no user groups or roles

Access rights for users, groups and roles according to SQL-standard

yes

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