DBMS > 4D vs. Blueflood vs. Google Cloud Bigtable vs. Microsoft Azure Cosmos DB vs. mSQL

System Properties Comparison 4D vs. Blueflood vs. Google Cloud Bigtable vs. Microsoft Azure Cosmos DB vs. mSQL

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Scalable TimeSeries DBMS based on Cassandra

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Globally distributed, horizontally scalable, multi-model database service

mSQL (Mini SQL) is a simple and lightweight RDBMS

Primary database model

Relational DBMS

Time Series DBMS

Key-value store
Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Website

www.4d.com

blueflood.io

cloud.google.com/bigtable

azure.microsoft.com/services/cosmos-db

hughestech.com.au/products/msql

Technical documentation

developer.4d.com

github.com/rax-maas/blueflood/wiki

cloud.google.com/bigtable/docs

learn.microsoft.com/azure/cosmos-db

Developer

4D, Inc

Rackspace

Google

Microsoft

Hughes Technologies

Initial release

1984

2013

2015

2014

1994

Current release

v20, April 2023

4.4, October 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

free licenses can be provided

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

Server operating systems

OS X
Windows

Linux
OS X

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

predefined scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON 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

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

HTTP REST

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC

Supported programming languages

4D proprietary IDE
PHP

C#
C++
Go
Java
JavaScript (Node.js)
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

yes

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

based on Cassandra

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

Multi-item ACID transactions with snapshot isolation within a partition

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.

User concepts

Access control

Users and groups

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

Access rights can be defined down to the item level

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