DBMS > Apache IoTDB vs. Apache Jena - TDB vs. PouchDB vs. Spark SQL vs. SQLite

System Properties Comparison Apache IoTDB vs. Apache Jena - TDB vs. PouchDB vs. Spark SQL vs. SQLite

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 RDF storage and query DBMS, shipped as an optional-use component of the Apache Jena framework

JavaScript DBMS with an API inspired by CouchDB

Spark SQL is a component on top of 'Spark Core' for structured data processing

Widely used embeddable, in-process RDBMS

Primary database model

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	3.75
Rank	#84	Overall
	#3	RDF stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

iotdb.apache.org

jena.apache.org/documentation/tdb/index.html

pouchdb.com

spark.apache.org/sql

www.sqlite.org

Technical documentation

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

jena.apache.org/documentation/tdb/index.html

pouchdb.com/guides

spark.apache.org/docs/latest/sql-programming-guide.html

www.sqlite.org/docs.html

Developer

Apache Software Foundation

originally developed by HP Labs

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2018

2000

2012

2014

2000

Current release

1.1.0, April 2023

4.9.0, July 2023

7.1.1, June 2019

3.5.0 ( 2.13), September 2023

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache License, Version 2.0

Open Source

Apache 2.0

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

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Implementation language

Java

JavaScript

Scala

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java VM

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Windows

server-less

Data scheme

yes

RDF Schemas

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.

Secondary indexes

yes

via views

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
Native API

Fuseki

REST-style SPARQL HTTP Interface
Jena RDF API
RIO

RDF Input/Output

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java

JavaScript

Java
Python
R
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

Server-side scripts

Stored procedures

yes

View functions in JavaScript

Triggers

yes

via event handler

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

with a proxy-based framework, named couchdb-lounge

yes, utilizing Spark Core

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

none

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

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

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

TDB Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

Access control via Jena Security

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