DBMS > HugeGraph vs. Microsoft Azure AI Search vs. MySQL vs. Sphinx vs. Titan

System Properties Comparison HugeGraph vs. Microsoft Azure AI Search vs. MySQL vs. Sphinx vs. Titan

Editorial information provided by DB-Engines

Name

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

A fast-speed and highly-scalable Graph DBMS

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

Widely used open source RDBMS

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

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Graph DBMS

Search engine

Relational DBMS

Key/Value like access via memcached API

Search engine

Graph DBMS

Secondary database models

Vector DBMS

Document store
Spatial DBMS

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

github.com/hugegraph
hugegraph.apache.org

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

www.mysql.com

sphinxsearch.com

github.com/thinkaurelius/titan

Technical documentation

hugegraph.apache.org/docs

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

dev.mysql.com/doc

sphinxsearch.com/docs

github.com/thinkaurelius/titan/wiki

Developer

Baidu

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Sphinx Technologies Inc.

Aurelius, owned by DataStax

Initial release

2018

2015

1995

2001

2012

Current release

0.9

8.4.0, April 2024

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL version 2, commercial licence available

Open Source

Apache license, version 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

C++

Java

Server operating systems

Linux
macOS
Unix

hosted

FreeBSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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

full-text index on all search fields

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language (SphinxQL)

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Proprietary protocol

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

Groovy
Java
Python

C#
Java
JavaScript
Python

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

Clojure
Java
Python

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

proprietary syntax

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

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

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

not for MyISAM storage engine

yes

Relationships in graph

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

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.

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

In-memory capabilities

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

yes

User concepts

Access control

Users, roles and permissions

yes

using Azure authentication

Users with fine-grained authorization concept

no user groups or roles

User authentification and security via Rexster Graph Server

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