DBMS > 4D vs. Apache IoTDB vs. Solr vs. SQLite vs. TimesTen

System Properties Comparison 4D vs. Apache IoTDB vs. Solr vs. SQLite 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

A widely used distributed, scalable search engine based on Apache Lucene

Widely used embeddable, in-process RDBMS

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

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	44.28
Rank	#24	Overall
	#3	Search engines

Score	116.01
Rank	#10	Overall
	#7	Relational DBMS

Score	1.35
Rank	#165	Overall
	#75	Relational DBMS

Website

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

Technical documentation

developer.4d.com

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

solr.apache.org/resources.html

www.sqlite.org/docs.html

docs.oracle.com/database/timesten-18.1

Developer

4D, Inc

Apache Software Foundation

Dwayne Richard Hipp

Oracle, TimesTen Performance Software, HP

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

Initial release

1984

2018

2006

2000

1998

Current release

v20, April 2023

1.1.0, April 2023

9.5.0, February 2024

3.45.2 (12 March 2024), March 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2

Open Source

Public Domain

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

OS X
Windows

All OS with a Java VM (>= 1.8)

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

server-less

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

yes

not rigid because of 'dynamic typing' concept.

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

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

Solr Parallel SQL Interface

yes

SQL-92 is not fully supported

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
Native API

Java API
RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

4D proprietary IDE
PHP

C
C#
C++
Go
Java
Python
Scala

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

Java plugins

PL/SQL

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

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

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

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