DBMS > Faircom DB vs. Faircom EDGE vs. LevelDB vs. Trino

System Properties Comparison Faircom DB vs. Faircom EDGE vs. LevelDB vs. Trino

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Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Primary database model

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	0.18
Rank	#314	Overall
	#45	Key-value stores
	#141	Relational DBMS

Score	0.02
Rank	#370	Overall
	#55	Key-value stores
	#157	Relational DBMS

Score	2.33
Rank	#111	Overall
	#19	Key-value stores

Score	5.28
Rank	#61	Overall
	#35	Relational DBMS

Website

www.faircom.com/products/faircom-db

www.faircom.com/products/faircom-edge

github.com/google/leveldb

trino.io

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

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

github.com/google/leveldb/blob/main/doc/index.md

trino.io/broadcast
trino.io/docs/current

Developer

FairCom Corporation

Google

Trino Software Foundation

Initial release

1979

2011

2012

2020 rebranded from PrestoSQL

Current release

V13, July 2024

V3, October 2020

1.23, February 2021

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Restricted, free version available

Open Source

BSD

Open Source

Apache Version 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

ANSI C, C++

C++

Java

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Android
Linux

ARM, x86
Raspbian
Windows

Illumos
Linux
OS X
Windows

Linux
macOS

for devlopment

Data scheme

schema free, schema optional, schema required, partial schema,

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

schema-free

yes

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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

depending on connected data-source

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

ANSI SQL queries

yes

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

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

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

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

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

.Net, JavaScript, C/C++

yes, depending on connected data-source

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

File partitioning

Customizable business rules for partitioning

none

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

Synchronous and asynchronous realtime replication based on transaction logs

none

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency
Tunable Consistency

Immediate Consistency

depending on connected data-source

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

yes

with automatic compression on writes

depending on connected data-source

In-memory capabilities

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

yes

User concepts

Access control

Fine grained access rights according to SQL-standard with additional protections for files

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

SQL standard access control

More information provided by the system vendor

News

23 is a go, keeping pace with Java
17 September 2024

65: Performance boosts
12 September 2024

64: Control with Open Policy Agent OPA
22 August 2024

63: Querying with JS
1 August 2024

62: A lakehouse that simply works at Prezi
11 July 2024

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