DBMS > 4D vs. Bangdb vs. BigObject vs. Hazelcast vs. Tibero

System Properties Comparison 4D vs. Bangdb vs. BigObject vs. Hazelcast vs. Tibero

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Converged and high performance database for device data, events, time series, document and graph

Analytic DBMS for real-time computations and queries

A widely adopted in-memory data grid

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS

Document store

JSON support with IMDG 3.12

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

us.tmaxsoft.com/products/tibero

Technical documentation

developer.4d.com

docs.bangdb.com

docs.bigobject.io

hazelcast.org/imdg/docs

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

4D, Inc

Sachin Sinha, BangDB

BigObject, Inc.

Hazelcast

TmaxSoft

Initial release

1984

2012

2015

2008

2003

Current release

v20, April 2023

BangDB 2.0, October 2021

5.3.6, November 2023

6, April 2015

License

Commercial or Open Source

commercial

Open Source

BSD 3

commercial

free community edition available

Open Source

Apache Version 2; commercial licenses available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++

Java

C and Assembler

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)

All OS with a Java VM

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

yes

close to SQL 92

SQL like support with command line tool

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

Proprietary protocol
RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

4D proprietary IDE
PHP

C
C#
C++
Java
Python

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

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

Lua

yes

Event Listeners, Executor Services

Persistent Stored Procedure (PSM)

Triggers

yes

yes, Notifications (with Streaming only)

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Sharding

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor, Knob for CAP (enterprise version only)

none

yes

Replicated Map

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

none

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate 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

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

planned for next version

User concepts

Access control

Users and groups

yes (enterprise version only)

Role-based access control

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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