DBMS > 4D vs. H2 vs. Kinetica vs. LeanXcale vs. ObjectBox

System Properties Comparison 4D vs. H2 vs. Kinetica vs. LeanXcale vs. ObjectBox

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Fully vectorized database across both GPUs and CPUs

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Object oriented DBMS
Vector DBMS

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

Time Series DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS
	#7	Vector DBMS

Website

github.com/objectbox
objectbox.io

Technical documentation

developer.4d.com

www.h2database.com/html/main.html

docs.kinetica.com

docs.objectbox.io

Developer

4D, Inc

Thomas Mueller

Kinetica

LeanXcale

ObjectBox Limited

Initial release

1984

2005

2012

2015

2017

Current release

v20, April 2023

2.2.220, July 2023

7.1, August 2021

4.0 (May 2024)

License

Commercial or Open Source

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

Bindings are released under Apache 2.0

Apache License 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, C++

C and C++

Server operating systems

OS X
Windows

All OS with a Java VM

Linux

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes, plus "flex" map-like types

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

close to SQL 92

yes

SQL-like DML and DDL statements

yes

through Apache Derby

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Proprietary native API

Supported programming languages

4D proprietary IDE
PHP

Java

C++
Java
JavaScript (Node.js)
Python

C
Java
Scala

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

Server-side scripts

Stored procedures

yes

Java Stored Procedures and User-Defined Functions

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

With clustering: 2 database servers on different computers operate on identical copies of a database

Source-replica replication

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

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

yes, multi-version concurrency control (MVCC)

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

GPU vRAM or System RAM

yes

User concepts

Access control

Users and groups

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

yes

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

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

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Recent citations in the news

Kinetica Delivers Real-Time Vector Similarity Search
21 March 2024, insideBIGDATA

Kinetica Elevates RAG with Fast Access to Real-Time Data
26 March 2024, Datanami

Kinetica ramps up RAG for generative AI, empowering enterprises with real-time operational data
18 March 2024, SiliconANGLE News

Kinetica Launches Generative AI Solution for Real-Time Inferencing Powered by NVIDIA AI Enterprise
18 March 2024, GlobeNewswire

Transforming spatiotemporal data analysis with GPUs and generative AI
30 October 2023, InfoWorld

provided by Google News

ObjectBox Raises $2M in Funding
4 December 2018, FinSMEs

The Megashift Towards Decentralized Edge Computing
27 August 2021, hackernoon.com

provided by Google News

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