DBMS > Hazelcast vs. Microsoft Azure AI Search vs. mSQL vs. Snowflake vs. Sphinx

System Properties Comparison Hazelcast vs. Microsoft Azure AI Search vs. mSQL vs. Snowflake vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

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

Document store

JSON support with IMDG 3.12

Vector DBMS

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

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

hazelcast.com

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

hughestech.com.au/products/msql

www.snowflake.com

sphinxsearch.com

Technical documentation

hazelcast.org/imdg/docs

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

docs.snowflake.net/manuals/index.html

sphinxsearch.com/docs

Developer

Hazelcast

Microsoft

Hughes Technologies

Snowflake Computing Inc.

Sphinx Technologies Inc.

Initial release

2008

2015

1994

2014

2001

Current release

5.3.6, November 2023

4.4, October 2021

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

free licenses can be provided

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++

Server operating systems

All OS with a Java VM

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

SQL-like query language (SphinxQL)

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

RESTful HTTP API

JDBC
ODBC

CLI Client
JDBC
ODBC

Proprietary protocol

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C#
Java
JavaScript
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

JavaScript (Node.js)
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

user defined functions

Triggers

yes

Events

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

yes

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

Implicit feature of the cloud service

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

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

User concepts

Access control

Role-based access control

yes

using Azure authentication

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

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