DBMS > Apache Doris vs. Apache Spark (SQL) vs. Faircom EDGE

System Properties Comparison Apache Doris vs. Apache Spark (SQL) vs. Faircom EDGE

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

Apache Spark SQL is a component on top of 'Spark Core' for structured data processing

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

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

Score	0.48
Rank	#250	Overall
	#116	Relational DBMS

Score	17.39
Rank	#32	Overall
	#20	Relational DBMS

Score	0.01
Rank	#384	Overall
	#59	Key-value stores
	#157	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

spark.apache.org/sql

www.faircom.com/products/faircom-edge

Technical documentation

github.com/apache/doris/wiki

spark.apache.org/docs/latest/sql-programming-guide.html

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

Developer

Apache Software Foundation, originally contributed from Baidu

Apache Software Foundation

FairCom Corporation

Initial release

2017

2014

1979

Current release

1.2.2, February 2023

3.5.0 ( 2.13), September 2023

V3, October 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

Restricted, free version available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

ANSI C, C++

Server operating systems

Linux

Linux
OS X
Windows

Android
Linux

ARM, x86
Raspbian
Windows

Data scheme

yes

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

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

SQL-like DML and DDL statements

yes

ANSI SQL queries

APIs and other access methods

JDBC
MySQL client

JDBC
ODBC

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

Supported programming languages

Java

Java
Python
R
Scala

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

Server-side scripts

Stored procedures

user defined functions

yes

.Net, JavaScript, C/C++

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes, utilizing Spark Core

File partitioning

Customizable business rules for partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Synchronous and asynchronous realtime replication based on transaction logs

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

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

Durability

Support for making data persistent

yes

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

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

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