DBMS > ObjectBox vs. ReductStore vs. SQLite vs. TimescaleDB vs. Trafodion

System Properties Comparison ObjectBox vs. ReductStore vs. SQLite vs. TimescaleDB vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Lightweight, fast on-device database for IoT, Mobile and Embedded devices, persisting and synchronising objects and vectors

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Widely used embeddable, in-process RDBMS

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Transactional SQL-on-Hadoop DBMS

Primary database model

Object oriented DBMS
Vector DBMS

Secondary database models

Time Series DBMS

Relational DBMS

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

Score	1.08
Rank	#179	Overall
	#6	Object oriented DBMS
	#9	Vector DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Score	4.06
Rank	#73	Overall
	#5	Time Series DBMS

Website

github.com/objectbox
objectbox.io

github.com/reductstore
www.reduct.store

www.sqlite.org

www.timescale.com

trafodion.apache.org

Technical documentation

docs.objectbox.io

www.reduct.store/docs

www.sqlite.org/docs.html

docs.timescale.com

trafodion.apache.org/documentation.html

Developer

ObjectBox Limited

ReductStore LLC

Dwayne Richard Hipp

Timescale

Apache Software Foundation, originally developed by HP

Initial release

2017

2023

2000

2017

2014

Current release

4.0 (May 2024)

1.9, March 2024

3.46.1 (13 August 2024), August 2024

2.15.0, May 2024

2.3.0, February 2019

License

Commercial or Open Source

Bindings are released under Apache 2.0

Apache License 2.0

Open Source

Business Source License 1.1

Open Source

Public Domain

Open Source

Apache 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

C and C++

C++, Rust

C++, Java

Server operating systems

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

Docker
Linux
macOS
Windows

server-less

Linux
OS X
Windows

Linux

Data scheme

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes, plus "flex" map-like types

yes

not rigid because of 'dynamic typing' concept.

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

SQL

Support of SQL

yes

SQL-92 is not fully supported

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

Proprietary native API

HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

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

ADO.NET
JDBC
ODBC

Supported programming languages

C
C++
Dart (Flutter)
Go
Java
Kotlin
Python
Swift

C++
JavaScript (Node.js)
Python
Rust

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, across time and space (hash partitioning) attributes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Data sync between devices allowing occasional connected databases to work completely offline

none

Source-replica replication with hot standby and reads on replicas

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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

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

More information provided by the system vendor

News

MongoDB Realm & Device Sync alternatives – ObjectBox
17 September 2024

Local AI – what it is and why we need it
11 September 2024

First on-device Vector Database (aka Semantic Index) for iOS
24 July 2024

Retrieval Augmented Generation (RAG) with vector databases: Expanding AI Capabilities
18 June 2024

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

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