DBMS > 4D vs. Atos Standard Common Repository vs. Drizzle vs. Google Cloud Datastore vs. SpaceTime

System Properties Comparison 4D vs. Atos Standard Common Repository vs. Drizzle vs. Google Cloud Datastore vs. SpaceTime

Editorial information provided by DB-Engines

Name

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

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded 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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Relational DBMS

Document store
Key-value store

Relational DBMS

Document store

Spatial DBMS

Secondary database models

Relational DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Website

www.4d.com

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

cloud.google.com/datastore

www.mireo.com/spacetime

Technical documentation

developer.4d.com

cloud.google.com/datastore/docs

Developer

4D, Inc

Atos Convergence Creators

Drizzle project, originally started by Brian Aker

Google

Mireo

Initial release

1984

2016

2008

2020

Current release

v20, April 2023

1703

7.2.4, September 2012

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

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++

Server operating systems

OS X
Windows

Linux

FreeBSD
Linux
OS X

hosted

Linux

Data scheme

yes

Schema and schema-less with LDAP views

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

yes

yes, details here

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

with proprietary extensions

SQL-like query language (GQL)

A subset of ANSI SQL is implemented

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

LDAP

JDBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

4D proprietary IDE
PHP

All languages with LDAP bindings

C
C++
Java
PHP

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Python

Server-side scripts

Stored procedures

yes

using Google App Engine

Triggers

yes

hooks for callbacks inside the server can be used.

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

cell division

Sharding

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Multi-source replication
Source-replica replication

Multi-source replication using Paxos

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Atomic execution of specific operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

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

Users and groups

LDAP bind authentication

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

More information provided by the system vendor

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Related products and services

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More resources

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