DBMS > Dragonfly vs. ObjectBox vs. Postgres-XL vs. Trafodion

System Properties Comparison Dragonfly vs. ObjectBox vs. Postgres-XL vs. Trafodion

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Editorial information provided by DB-Engines

Name

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

Description

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Transactional SQL-on-Hadoop DBMS

Primary database model

Key-value store

Object oriented DBMS
Vector DBMS

Relational DBMS

Secondary database models

Time Series DBMS

Document store
Spatial DBMS

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

Score	0.44
Rank	#255	Overall
	#38	Key-value stores

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

Score	0.43
Rank	#260	Overall
	#119	Relational DBMS

Website

github.com/dragonflydb/dragonfly
www.dragonflydb.io

github.com/objectbox
objectbox.io

www.postgres-xl.org

trafodion.apache.org

Technical documentation

www.dragonflydb.io/docs

docs.objectbox.io

www.postgres-xl.org/documentation

trafodion.apache.org/documentation.html

Developer

DragonflyDB team and community contributors

ObjectBox Limited

Apache Software Foundation, originally developed by HP

Initial release

2023

2017

2014

since 2012, originally named StormDB

2014

Current release

1.0, March 2023

4.0 (May 2024)

10 R1, October 2018

2.3.0, February 2019

License

Commercial or Open Source

Open Source

BSL 1.1

Bindings are released under Apache 2.0

Apache License 2.0

Open Source

Mozilla public license

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++

C and C++

C++, Java

Server operating systems

Linux

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

Linux
macOS

Linux

Data scheme

scheme-free

yes

Typing

predefined data types such as float or date

strings, hashes, lists, sets, sorted sets, bit arrays

yes, plus "flex" map-like 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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

distributed, parallel query execution

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

Proprietary native API

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

ADO.NET
JDBC
ODBC

Supported programming languages

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

Lua

user defined functions

Java Stored Procedures

Triggers

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

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

Password-based authentication

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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More resources

Recent citations in the news

DragonflyDB Announces $21m in New Funding and General Availability
21 March 2023, Business Wire

DragonflyDB reels in $21M for its speedy in-memory database
21 March 2023, SiliconANGLE News

DragonflyDB Raises $21M in Funding
21 March 2023, FinSMEs

Intel Linux Kernel Optimizations Show Huge Benefit For High Core Count Servers
29 March 2023, Phoronix

The Performance Impact Of Intel's Register File Data Sampling "RFDS" Mitigation
15 March 2024, Phoronix

provided by Google News

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