DBMS > Hazelcast vs. Microsoft Azure Cosmos DB vs. MySQL vs. Snowflake vs. SWC-DB

System Properties Comparison Hazelcast vs. Microsoft Azure Cosmos DB vs. MySQL vs. Snowflake vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

Globally distributed, horizontally scalable, multi-model database service

Widely used open source RDBMS

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

A high performance, scalable Wide Column DBMS

Primary database model

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Wide column store

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

Document store
Spatial DBMS

Time Series DBMS

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

Score	6.87
Rank	#55	Overall
	#6	Key-value stores

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	0.01
Rank	#387	Overall
	#13	Wide column stores

Website

hazelcast.com

azure.microsoft.com/services/cosmos-db

www.mysql.com

www.snowflake.com

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

hazelcast.org/imdg/docs

learn.microsoft.com/azure/cosmos-db

dev.mysql.com/doc

docs.snowflake.net/manuals/index.html

Developer

Hazelcast

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Snowflake Computing Inc.

Alex Kashirin

Initial release

2008

2014

1995

2014

2020

Current release

5.3.6, November 2023

8.3.0, January 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

C++

Server operating systems

All OS with a Java VM

hosted

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Linux

Data scheme

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes

JSON 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

the object must implement a serialization strategy

yes

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

yes

SQL-like query language

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

ADO.NET
JDBC
ODBC
Proprietary native API

CLI Client
JDBC
ODBC

Proprietary protocol
Thrift

Supported programming languages

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

JavaScript (Node.js)
Python

C++

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

JavaScript

yes

proprietary syntax

user defined functions

Triggers

yes

Events

JavaScript

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

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

Multi-item ACID transactions with snapshot isolation within a partition

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

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

Access rights can be defined down to the item level

Users with fine-grained authorization concept

no user groups or roles

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

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