DBMS > Bangdb vs. Cubrid vs. Lovefield vs. mSQL vs. Vitess

System Properties Comparison Bangdb vs. Cubrid vs. Lovefield vs. mSQL vs. Vitess

Editorial information provided by DB-Engines

Name

Description

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

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Embeddable relational database for web apps written in pure JavaScript

mSQL (Mini SQL) is a simple and lightweight RDBMS

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

bangdb.com

cubrid.com (korean)
cubrid.org (english)

google.github.io/lovefield

hughestech.com.au/products/msql

vitess.io

Technical documentation

docs.bangdb.com

cubrid.org/manuals

github.com/google/lovefield/blob/master/docs/spec_index.md

vitess.io/docs

Developer

Sachin Sinha, BangDB

CUBRID Corporation, CUBRID Foundation

Google

Hughes Technologies

The Linux Foundation, PlanetScale

Initial release

2012

2008

2014

1994

2013

Current release

BangDB 2.0, October 2021

11.0, January 2021

2.1.12, February 2017

4.4, October 2021

15.0.2, December 2022

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

free licenses can be provided

Open Source

Apache Version 2.0, commercial licenses available

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++

C, C++, Java

JavaScript

Server operating systems

Linux

Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

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

Docker
Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

SQL-like query language

via JavaScript builder pattern

A subset of ANSI SQL is implemented

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

yes

with proprietary extensions

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

C
C#
C++
Java
Python

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

JavaScript

C
C++
Delphi
Java
Perl
PHP
Tcl

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Java Stored Procedures

yes

proprietary syntax

Triggers

yes, Notifications (with Streaming only)

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication

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

Tunable consistency, set CAP knob accordingly

Immediate Consistency

none

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

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

using MemoryDB

yes

User concepts

Access control

yes (enterprise version only)

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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