DBMS > 4D vs. Atos Standard Common Repository vs. Google BigQuery vs. SQLite vs. VoltDB

System Properties Comparison 4D vs. Atos Standard Common Repository vs. Google BigQuery vs. SQLite vs. VoltDB

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Application development environment with integrated database management system

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Large scale data warehouse service with append-only tables

Widely used embeddable, in-process RDBMS

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

Document store
Key-value store

Relational DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.4d.com

atos.net/en/convergence-creators/portfolio/standard-common-repository

cloud.google.com/bigquery

www.sqlite.org

www.voltdb.com

Technical documentation

developer.4d.com

cloud.google.com/bigquery/docs

www.sqlite.org/docs.html

docs.voltdb.com

Developer

4D, Inc

Atos Convergence Creators

Google

Dwayne Richard Hipp

VoltDB Inc.

Initial release

1984

2016

2010

2000

2010

Current release

v20, April 2023

1703

3.45.3 (15 April 2024), April 2024

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Public Domain

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

Java, C++

Server operating systems

OS X
Windows

Linux

hosted

server-less

Linux
OS X

for development

Data scheme

yes

Schema and schema-less with LDAP views

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

optional

yes

not rigid because of 'dynamic typing' concept.

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

SQL-92 is not fully supported

yes

only a subset of SQL 99

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

LDAP

RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

4D proprietary IDE
PHP

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

cell division

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

none

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

Immediate Consistency or Eventual Consistency depending on configuration

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

Atomic execution of specific operations

Since BigQuery is designed for querying data

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

LDAP bind authentication

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

Google Cloud Identity & Access Management (IAM)

Users and roles with access to stored procedures

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