DBMS > Bangdb vs. Dolt vs. Faircom EDGE vs. Lovefield

System Properties Comparison Bangdb vs. Dolt vs. Faircom EDGE vs. Lovefield

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Editorial information provided by DB-Engines

Name

Description

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

A MySQL compatible DBMS with Git-like versioning of data and schema

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	1.02
Rank	#191	Overall
	#89	Relational DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Website

bangdb.com

github.com/dolthub/dolt
www.dolthub.com

www.faircom.com/products/faircom-edge

google.github.io/lovefield

Technical documentation

docs.bangdb.com

docs.dolthub.com

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

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

Developer

Sachin Sinha, BangDB

DoltHub Inc

FairCom Corporation

Google

Initial release

2012

2018

1979

2014

Current release

BangDB 2.0, October 2021

V3, October 2020

2.1.12, February 2017

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

ANSI C, C++

JavaScript

Server operating systems

Linux

Linux
macOS
Windows

Android
Linux

ARM, x86
Raspbian
Windows

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

tested with Chrome, Firefox, IE, Safari

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

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

yes

yes, ANSI Standard SQL 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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

ANSI SQL queries

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

Proprietary protocol
RESTful HTTP API

CLI Client
HTTP REST

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

Supported programming languages

C
C#
C++
Java
Python

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

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

JavaScript

Server-side scripts

Stored procedures

yes

currently in alpha release

yes

.Net, JavaScript, C/C++

Triggers

yes, Notifications (with Streaming only)

yes

Using read-only observers

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

File partitioning

Customizable business rules for partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

yes

Synchronous and asynchronous realtime replication based on transaction logs

none

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
Tunable Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

across SQL and NoSQL

yes

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

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

User concepts

Access control

yes (enterprise version only)

Only one user is configurable, and must be specified in the config file at startup

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

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