DBMS > Apache IoTDB vs. ObjectBox vs. SQLite vs. Titan vs. Trafodion

System Properties Comparison Apache IoTDB vs. ObjectBox vs. SQLite vs. Titan vs. Trafodion

Editorial information provided by DB-Engines

Name

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Apache Trafodion has been retired in 2021. 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

Widely used embeddable, in-process RDBMS

Titan is a Graph DBMS optimized for distributed clusters.

Transactional SQL-on-Hadoop DBMS

Primary database model

Secondary database models

Time Series DBMS

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	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

iotdb.apache.org

objectbox.io

www.sqlite.org

github.com/thinkaurelius/titan

trafodion.apache.org

Technical documentation

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

docs.objectbox.io

www.sqlite.org/docs.html

github.com/thinkaurelius/titan/wiki

trafodion.apache.org/documentation.html

Developer

Apache Software Foundation

ObjectBox Limited

Dwayne Richard Hipp

Aurelius, owned by DataStax

Apache Software Foundation, originally developed by HP

Initial release

2018

2017

2000

2012

2014

Current release

1.1.0, April 2023

3.46.0 (23 May 2024), May 2024

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache License 2.0

Open Source

Public Domain

Open Source

Apache license, version 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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

C++, Java

Server operating systems

All OS with a Java VM (>= 1.8)

Android
iOS
Linux
macOS
Windows

server-less

Linux
OS X
Unix
Windows

Linux

Data scheme

yes

dynamic column types

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-92 is not fully supported

yes

APIs and other access methods

JDBC
Native API

Proprietary native API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC

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

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

Clojure
Java
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

yes

via pluggable storage backends

Sharding

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

none

yes

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

via Faunus, a graph analytics engine

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graph

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.