DBMS > Apache IoTDB vs. ObjectBox vs. OrigoDB vs. Stardog vs. Yanza

System Properties Comparison Apache IoTDB vs. ObjectBox vs. OrigoDB vs. Stardog vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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

A fully ACID in-memory object graph database

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

Time Series DBMS for IoT Applications

Primary database model

Time Series DBMS

Object oriented DBMS

Document store
Object oriented DBMS

Graph DBMS
RDF store

Time Series DBMS

Secondary database models

Time Series DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

yanza.com

Technical documentation

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

docs.objectbox.io

origodb.com/docs

docs.stardog.com

Developer

Apache Software Foundation

ObjectBox Limited

Robert Friberg et al

Stardog-Union

Yanza

Initial release

2018

2017

2009

under the name LiveDB

2010

2015

Current release

1.1.0, April 2023

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache License 2.0

Open Source

commercial

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

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Android
iOS
Linux
macOS
Windows

Linux
Windows

Linux
macOS
Windows

Windows

Data scheme

yes

schema-free and OWL/RDFS-schema support

schema-free

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

can be achieved using .NET

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

APIs and other access methods

JDBC
Native API

Proprietary native API

.NET Client API
HTTP API
LINQ

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

HTTP API

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

Domain Events

yes

via event handlers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

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

none

horizontal partitioning

client side managed; servers are not synchronized

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

Multi-source replication in HA-Cluster

none

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

Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

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

Write ahead log

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

Role based authorization

Access rights for users and roles

More information provided by the system vendor

News

The first On-Device Vector Database: ObjectBox 4.0
16 May 2024

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

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