DBMS > HugeGraph vs. Kinetica vs. Microsoft Azure AI Search vs. Snowflake vs. Sphinx

System Properties Comparison HugeGraph vs. Kinetica vs. Microsoft Azure AI Search vs. Snowflake vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

Fully vectorized database across both GPUs and CPUs

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

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

Vector DBMS

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

github.com/hugegraph
hugegraph.apache.org

www.kinetica.com

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

www.snowflake.com

sphinxsearch.com

Technical documentation

hugegraph.apache.org/docs

docs.kinetica.com

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

docs.snowflake.net/manuals/index.html

sphinxsearch.com/docs

Developer

Baidu

Kinetica

Microsoft

Snowflake Computing Inc.

Sphinx Technologies Inc.

Initial release

2018

2012

2015

2014

2001

Current release

0.9

7.1, August 2021

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

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.

Implementation language

Java

C, C++

C++

Server operating systems

Linux
macOS
Unix

Linux

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language (SphinxQL)

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

CLI Client
JDBC
ODBC

Proprietary protocol

Supported programming languages

Groovy
Java
Python

C++
Java
JavaScript (Node.js)
Python

C#
Java
JavaScript
Python

JavaScript (Node.js)
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

similar concept for controling cloud resources

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

yes

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Source-replica replication

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

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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

GPU vRAM or System RAM

User concepts

Access control

Users, roles and permissions

Access rights for users and roles on table level

yes

using Azure authentication

Users with fine-grained authorization concept, user roles and pluggable authentication

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