DBMS > Databend vs. Faircom EDGE vs. Graph Engine vs. Lovefield

System Properties Comparison Databend vs. Faircom EDGE vs. Graph Engine vs. Lovefield

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Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Graph DBMS
Key-value store

Relational DBMS

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

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Website

github.com/datafuselabs/databend
www.databend.com

www.faircom.com/products/faircom-edge

www.graphengine.io

google.github.io/lovefield

Technical documentation

docs.databend.com

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

www.graphengine.io/docs/manual

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

Developer

Databend Labs

FairCom Corporation

Microsoft

Google

Initial release

2021

1979

2010

2014

Current release

1.0.59, April 2023

V3, October 2020

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

MIT License

Open Source

Apache 2.0

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

Rust

ANSI C, C++

.NET and C

JavaScript

Server operating systems

hosted
Linux
macOS

Android
Linux

ARM, x86
Raspbian
Windows

.NET

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

tested with Chrome, Firefox, IE, Safari

Data scheme

yes

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

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

ANSI SQL queries

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

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

RESTful HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

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

C#
C++
F#
Visual Basic

JavaScript

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Triggers

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning

Customizable business rules for partitioning

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Immediate Consistency

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

using MemoryDB

User concepts

Access control

Users with fine-grained authorization concept, user roles

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

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