DBMS > AgensGraph vs. Faircom EDGE vs. MySQL vs. Sphinx

System Properties Comparison AgensGraph vs. Faircom EDGE vs. MySQL vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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

Widely used open source RDBMS

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Graph DBMS
Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Key/Value like access via memcached API

Search engine

Secondary database models

Document store
Spatial DBMS

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

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

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

bitnine.net/agensgraph

www.faircom.com/products/faircom-edge

www.mysql.com

sphinxsearch.com

Technical documentation

bitnine.net/documentation

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

dev.mysql.com/doc

sphinxsearch.com/docs

Developer

Bitnine Global Inc.

FairCom Corporation

Oracle

since 2010, originally MySQL AB, then Sun

Sphinx Technologies Inc.

Initial release

2016

1979

1995

2001

Current release

2.1, December 2018

V3, October 2020

8.4.0, April 2024

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Restricted, free version available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

ANSI C, C++

C and C++

C++

Server operating systems

Linux
OS X
Windows

Android
Linux

ARM, x86
Raspbian
Windows

FreeBSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

depending on used data model

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

full-text index on all search fields

SQL

Support of SQL

yes

ANSI SQL queries

yes

with proprietary extensions

SQL-like query language (SphinxQL)

APIs and other access methods

Cypher Query Language
JDBC

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

ADO.NET
JDBC
ODBC
Proprietary native API

Proprietary protocol

Supported programming languages

C
Java
JavaScript
Python

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

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

File partitioning

Customizable business rules for partitioning

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication
Source-replica replication

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

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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.

Users with fine-grained authorization concept

no user groups or roles

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