DBMS > 4D vs. Apache IoTDB vs. LevelDB vs. MySQL vs. TimesTen

System Properties Comparison 4D vs. Apache IoTDB vs. LevelDB vs. MySQL vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Widely used open source RDBMS

In-Memory RDBMS compatible to Oracle

Primary database model

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	2.68
Rank	#110	Overall
	#54	Relational DBMS

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	2.75
Rank	#107	Overall
	#19	Key-value stores

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	1.35
Rank	#165	Overall
	#75	Relational DBMS

Website

www.4d.com

iotdb.apache.org

github.com/google/leveldb

www.mysql.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

developer.4d.com

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

github.com/google/leveldb/blob/main/doc/index.md

dev.mysql.com/doc

docs.oracle.com/database/timesten-18.1

Developer

4D, Inc

Apache Software Foundation

Google

Oracle

since 2010, originally MySQL AB, then Sun

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

1984

2018

2011

1995

1998

Current release

v20, April 2023

1.1.0, April 2023

1.23, February 2021

8.3.0, January 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

BSD

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

C and C++

Server operating systems

OS X
Windows

All OS with a Java VM (>= 1.8)

Illumos
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
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

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

yes

with proprietary extensions

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
Native API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

4D proprietary IDE
PHP

C
C#
C++
Go
Java
Python
Scala

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

proprietary syntax

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

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

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

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

with automatic compression on writes

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

User concepts

Access control

Users and groups

yes

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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