DBMS > 4D vs. Bangdb vs. CrateDB vs. H2 vs. ObjectBox

System Properties Comparison 4D vs. Bangdb vs. CrateDB vs. H2 vs. ObjectBox

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Converged and high performance database for device data, events, time series, document and graph

Distributed Database based on Lucene

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

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

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Object oriented DBMS
Vector DBMS

Secondary database models

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.71
Rank	#227	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#10	Vector DBMS

Score	8.33
Rank	#46	Overall
	#30	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

docs.bangdb.com

cratedb.com/docs

www.h2database.com/html/main.html

docs.objectbox.io

Developer

4D, Inc

Sachin Sinha, BangDB

Crate

Thomas Mueller

ObjectBox Limited

Initial release

1984

2012

2013

2005

2017

Current release

v20, April 2023

BangDB 2.0, October 2021

2.2.220, July 2023

4.0 (May 2024)

License

Commercial or Open Source

commercial

Open Source

BSD 3

Open Source

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

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

C, C++

Java

C and C++

Server operating systems

OS X
Windows

Linux

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

All OS with a Java VM

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

Data scheme

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

yes

close to SQL 92

SQL like support with command line tool

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

JDBC
ODBC

Proprietary native API

Supported programming languages

4D proprietary IDE
PHP

C
C#
C++
Java
Python

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

Java

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

Server-side scripts

Stored procedures

yes

user defined functions (Javascript)

Java Stored Procedures and User-Defined Functions

Triggers

yes

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor, Knob for CAP (enterprise version only)

Configurable replication on table/partition-level

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

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

Tunable consistency, set CAP knob accordingly

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, run db with in-memory only mode

yes

User concepts

Access control

Users and groups

yes (enterprise version only)

rights management via user accounts

fine grained access rights according to SQL-standard

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

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