DBMS > Apache IoTDB vs. ObjectBox vs. PostgreSQL vs. Stardog

System Properties Comparison Apache IoTDB vs. ObjectBox vs. PostgreSQL vs. Stardog

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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

Extremely fast embedded database for small devices, IoT and Mobile

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Time Series DBMS

Object oriented DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Graph DBMS
RDF store

Secondary database models

Time Series DBMS

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

Technical documentation

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

docs.objectbox.io

www.postgresql.org/docs

docs.stardog.com

Developer

Apache Software Foundation

ObjectBox Limited

PostgreSQL Global Development Group

www.postgresql.org/developer

Stardog-Union

Initial release

2018

2017

1989

1989: Postgres, 1996: PostgreSQL

2010

Current release

1.1.0, April 2023

16.3, May 2024

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache License 2.0

Open Source

BSD

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Android
iOS
Linux
macOS
Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free and OWL/RDFS-schema support

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

specific XML-type available, but no XML query functionality.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language

yes

standard with numerous extensions

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC
Native API

Proprietary native API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

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

none

partitioning by range, list and (since PostgreSQL 11) by hash

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

online/offline synchronization between client and server

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication in HA-Cluster

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

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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

fine grained access rights according to SQL-standard

Access rights for users and roles

More information provided by the system vendor

News

The on-device Vector Database for Android and Java
29 May 2024

Vector search: making sense of search queries
29 May 2024

Python on-device Vector and Object Database for Local AI
28 May 2024

Evolution of search: traditional vs vector search
23 May 2024

On-device Vector Database for Dart/Flutter
21 May 2024

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More resources

DB-Engines blog posts

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