DBMS > 4D vs. BigObject vs. Citus vs. Microsoft Azure Cosmos DB vs. openGemini

System Properties Comparison 4D vs. BigObject vs. Citus vs. Microsoft Azure Cosmos DB vs. openGemini

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Analytic DBMS for real-time computations and queries

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Globally distributed, horizontally scalable, multi-model database service

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Document store

Spatial DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	2.15
Rank	#117	Overall
	#56	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.09
Rank	#361	Overall
	#37	Time Series DBMS

Website

www.4d.com

bigobject.io

www.citusdata.com

azure.microsoft.com/services/cosmos-db

www.opengemini.org
github.com/openGemini

Technical documentation

developer.4d.com

docs.bigobject.io

docs.citusdata.com

learn.microsoft.com/azure/cosmos-db

docs.opengemini.org/guide

Developer

4D, Inc

BigObject, Inc.

Microsoft

Huawei and openGemini community

Initial release

1984

2015

2010

2014

2022

Current release

v20, April 2023

8.1, December 2018

1.1, July 2023

License

Commercial or Open Source

commercial

free community edition available

Open Source

AGPL, commercial license also available

commercial

Open Source

Apache Version 2.0

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

Server operating systems

OS X
Windows

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

hosted

Linux
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

Integer, Float, Boolean, String

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

specific XML type available, but no XML query functionality

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like DML and DDL statements

yes

standard, with numerous extensions

SQL-like query language

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

fluentd
ODBC
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

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

HTTP REST

Supported programming languages

4D proprietary IDE
PHP

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

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

Server-side scripts

Stored procedures

yes

Lua

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Source-replica replication

other methods possible by using 3rd party extensions

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

Administrators and common users accounts

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