DBMS > Faircom EDGE vs. HugeGraph vs. Microsoft Azure AI Search vs. OpenSearch vs. Sphinx

System Properties Comparison Faircom EDGE vs. HugeGraph vs. Microsoft Azure AI Search vs. OpenSearch vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

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

A fast-speed and highly-scalable Graph DBMS

Search-as-a-service for web and mobile app development

A distributed, RESTful search and analytics engine forked from Elasticsearch and based on Apache Lucene

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

Primary database model

Key-value store
Relational DBMS

Graph DBMS

Search engine

Secondary database models

Vector DBMS

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

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	16.36
Rank	#37	Overall
	#4	Search engines

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

www.faircom.com/products/faircom-edge

github.com/hugegraph
hugegraph.apache.org

azure.microsoft.com/en-us/services/search

github.com/opensearch-project
opensearch.org

sphinxsearch.com

Technical documentation

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

hugegraph.apache.org/docs

learn.microsoft.com/en-us/azure/search

opensearch.org/docs/latest

sphinxsearch.com/docs

Developer

FairCom Corporation

Baidu

Microsoft

Amazon Web Services

Sphinx Technologies Inc.

Initial release

1979

2018

2015

2021

2001

Current release

V3, October 2020

0.9

2.5.0, January 2023

3.5.1, February 2023

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

ANSI C, C++

Java

C++

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux
macOS
Unix

hosted

All OS with a Java VM

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

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

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

Typing

predefined data types such as float or date

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

also supports composite index and range index

yes

All search fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

yes

ANSI SQL queries

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

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

Java API
RESTful HTTP API
TinkerPop Gremlin

RESTful HTTP API

Java API
RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

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

Groovy
Java
Python

C#
Java
JavaScript
Python

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

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++

asynchronous Gremlin script jobs

yes

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Implicit feature of the cloud service

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

Implicit feature of the cloud service

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Foreign keys

Referential integrity

yes

when using SQL

yes

edges in graph

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

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

Users, roles and permissions

yes

using Azure authentication

More information provided by the system vendor

Specific characteristics

OpenSearch is a highly scalable and extensible open-source software suite for search,...
» more

Competitive advantages

Distributed as fully open source under Apache License, Version 2.0 (ALv2), OpenSearch...
» more

Typical application scenarios

The OpenSearch platform is used across a broad range of search , analytics, and observability...
» more

Licensing and pricing models

The OpenSearch Project offers open source software licensed under Apache License...
» more

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