DBMS > 4D vs. Google BigQuery vs. H2 vs. ObjectBox vs. VoltDB

System Properties Comparison 4D vs. Google BigQuery vs. H2 vs. ObjectBox vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Large scale data warehouse service with append-only tables

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Object oriented DBMS
Vector DBMS

Relational DBMS

Secondary database models

Spatial 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	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

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

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

www.4d.com

cloud.google.com/bigquery

www.h2database.com

github.com/objectbox
objectbox.io

www.voltdb.com

Technical documentation

developer.4d.com

cloud.google.com/bigquery/docs

www.h2database.com/html/main.html

docs.objectbox.io

docs.voltdb.com

Developer

4D, Inc

Google

Thomas Mueller

ObjectBox Limited

VoltDB Inc.

Initial release

1984

2010

2005

2017

2010

Current release

v20, April 2023

2.2.220, July 2023

4.0 (May 2024)

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

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

Bindings are released under Apache 2.0

Apache License 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

yes

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++

Server operating systems

OS X
Windows

hosted

All OS with a Java VM

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

Linux
OS X

for development

Data scheme

yes

Typing

predefined data types such as float or date

yes

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

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

yes

only a subset of SQL 99

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

RESTful HTTP/JSON API

JDBC
ODBC

Proprietary native API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

4D proprietary IDE
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Java Stored Procedures and User-Defined Functions

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

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

yes

User concepts

Access control

Users and groups

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

yes

Users and roles with access to stored procedures

More information provided by the system vendor

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